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"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
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}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## Install Required Dependencies\n",
"\n",
"This cell is responsible for setting up your Python environment by installing all the necessary libraries for the project.\n"
],
"metadata": {
"id": "HEYVY-MJuBTC"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 1: Install Required Dependencies ===\n",
"# Update pip first for best compatibility\n",
"!pip install --upgrade pip\n",
"\n",
"# Install Hugging Face Transformers from source for the latest Qwen2-Audio features\n",
"# This ensures you have the most up-to-date features and bug fixes for Qwen2-Audio.\n",
"!pip install git+https://github.com/huggingface/transformers.git\n",
"\n",
"# Install other necessary libraries\n",
"# gradio: for building the web UI\n",
"# librosa: for audio loading and processing\n",
"# accelerate: (Optional but recommended for distributed inference) Helps manage device placement\n",
"# reportlab: for PDF generation\n",
"# pydantic: for structured data validation and modeling\n",
"!pip install gradio librosa accelerate reportlab pydantic\n",
"\n",
"# Install bitsandbytes for 8-bit/4-bit quantization (CRUCIAL for large models on limited VRAM)\n",
"!pip install bitsandbytes\n",
"\n",
"# Add a small delay after installations for stability, especially on Colab\n",
"import time\n",
"time.sleep(2)\n",
"\n",
"print(\"Libraries installed successfully!\")"
],
"metadata": {
"id": "RQcc6M2gvI5P"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Import Modules and Configuration\n",
"This cell handles importing all necessary Python modules and setting up global configuration variables, including folder paths and device detection."
],
"metadata": {
"id": "SyvQGAjwwCnd"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 2: Import Modules and Configuration ===\n",
"from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor\n",
"from io import BytesIO\n",
"import librosa\n",
"import gradio as gr\n",
"import torch\n",
"import datetime\n",
"from reportlab.pdfgen import canvas\n",
"from reportlab.lib.pagesizes import A4\n",
"from reportlab.lib.units import inch\n",
"from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle\n",
"from reportlab.lib.styles import getSampleStyleSheet\n",
"from reportlab.lib import colors\n",
"import os\n",
"import re # For regex parsing of LLM output\n",
"import json # For JSON parsing of LLM output\n",
"import gc # For garbage collection\n",
"from pydantic import BaseModel, ValidationError, Field\n",
"from typing import List, Optional, Dict, Any\n",
"\n",
"# --- Configuration and Global Variables ---\n",
"LOCAL_AUDIO_FOLDER = \"/content/audio_inputs\"\n",
"LOCAL_PDF_FOLDER = \"/content/generated_invoices\"\n",
"QWEN2_AUDIO_MODEL_NAME = \"Qwen/Qwen2-Audio-7B-Instruct\"\n",
"\n",
"# Explicitly detect device and set global variable\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"if device == \"cuda\":\n",
" print(f\"CUDA is available! Using GPU: {torch.cuda.get_device_name(0)}\")\n",
"else:\n",
" print(\"CUDA not available. Falling back to CPU. Performance will be slower.\")\n",
"\n",
"\n",
"# Ensure local folders exist\n",
"os.makedirs(LOCAL_AUDIO_FOLDER, exist_ok=True)\n",
"os.makedirs(LOCAL_PDF_FOLDER, exist_ok=True)\n",
"\n",
"# Global variables to hold model instances for efficiency\n",
"qwen2_audio_processor = None\n",
"qwen2_audio_model = None\n",
"\n",
"# Utility function for clearing GPU memory\n",
"def clear_gpu_memory():\n",
" if torch.cuda.is_available():\n",
" torch.cuda.empty_cache()\n",
" torch.cuda.synchronize()\n",
" print(\"CUDA cache emptied.\")\n",
" gc.collect()\n",
" print(\"Garbage collection performed.\")\n",
"\n",
"\n",
"print(\"Configuration and global variables set up.\")\n",
"print(f\"Detected device: {device}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3nuPU-KFvIzL",
"outputId": "031d0d60-c44d-493d-fed3-394dec624f31"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CUDA is available! Using GPU: Tesla T4\n",
"Configuration and global variables set up.\n",
"Detected device: cuda\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Define Data Schemas for Structured Output\n",
"This cell defines the structure and validation rules for the invoice data using Pydantic models. This is vital for ensuring that the information extracted or generated for the invoice conforms to a predefined format and has calculated fields."
],
"metadata": {
"id": "H1LbC77xudGo"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 3: Define Data Schemas for Structured Output ===\n",
"\n",
"# Pydantic model for an individual invoice item\n",
"class InvoiceItem(BaseModel):\n",
" description: str\n",
" quantity: float\n",
" unit_price: float # Renamed from 'rate' for clarity in invoice\n",
" total: Optional[float] = None # Calculated, so optional initially\n",
"\n",
" # Add a validator to ensure total is calculated if not provided\n",
" def model_post_init(self, __context: Any) -> None:\n",
" if self.total is None:\n",
" self.total = self.quantity * self.unit_price\n",
"\n",
"# Pydantic model for the entire invoice data\n",
"class InvoiceData(BaseModel):\n",
" client_name: Optional[str] = None\n",
" client_address: Optional[str] = None\n",
" invoice_number: Optional[str] = None # Can be generated if missing\n",
" invoice_date: Optional[str] = None # Will default to today if missing\n",
" due_date: Optional[str] = None # Will default to invoice_date + 30 days if missing\n",
" items: List[InvoiceItem] = Field(default_factory=list)\n",
" subtotal: Optional[float] = None\n",
" tax_rate: Optional[float] = 0.08 # Default 8% tax\n",
" tax_amount: Optional[float] = None\n",
" grand_total: Optional[float] = None\n",
" notes: Optional[str] = None\n",
"\n",
" # Post-initialization to calculate totals if not provided by LLM\n",
" def model_post_init(self, __context: Any) -> None:\n",
" # Ensure items have totals calculated before summing\n",
" for item in self.items:\n",
" if item.total is None:\n",
" item.total = item.quantity * item.unit_price\n",
"\n",
" self.subtotal = sum(item.total for item in self.items) if self.items else 0.0\n",
" self.tax_amount = self.subtotal * self.tax_rate\n",
" self.grand_total = self.subtotal + self.tax_amount\n",
"\n",
"print(\"Pydantic data schemas defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BcaPjo7JvIq6",
"outputId": "21120a0e-1b96-44d5-c319-ce2f88103dca"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Pydantic data schemas defined.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Simulated Local DB for Autofill\n",
"This cell sets up simple in-memory dictionaries that simulate small databases for client and item information. These \"databases\" will be used to autofill common client details and item prices, making the invoice generation process more efficient and accurate."
],
"metadata": {
"id": "SOfpqPz8ug7Q"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 4: Simulated Local DB for Autofill ===\n",
"# Extend with more realistic data and potentially more fields (e.g., client email, phone)\n",
"user_db = {\n",
" \"john doe\": {\"name\": \"John Doe\", \"address\": \"123 Elm St, Springfield, IL\", \"email\": \"john.doe@example.com\", \"default_tax_rate\": 0.07},\n",
" \"acme corp\": {\"name\": \"ACME Corporation\", \"address\": \"456 Oak Ave, Metropolis, NY\", \"email\": \"info@acmecorp.com\", \"default_tax_rate\": 0.09},\n",
" \"sarah miller\": {\"name\": \"Sarah Miller\", \"address\": \"456 Oak Avenue, Anytown\", \"email\": \"sarah.m@example.com\"},\n",
" \"green solutions inc.\": {\"name\": \"Green Solutions Inc.\", \"address\": \"101 Forest Lane, Woodville\"},\n",
" # Add more users/clients as needed\n",
"}\n",
"item_db = {\n",
" \"laptop\": {\"description\": \"Gaming Laptop\", \"price\": 1500.00},\n",
" \"wireless mouse\": {\"description\": \"Ergonomic Wireless Mouse\", \"price\": 25.00},\n",
" \"keyboard\": {\"description\": \"Mechanical Keyboard\", \"price\": 120.00},\n",
" \"webcam\": {\"description\": \"HD Webcam\", \"price\": 50.00},\n",
" \"consulting service\": {\"description\": \"IT Consulting Service (per hour)\", \"price\": 100.00},\n",
" \"software license\": {\"description\": \"Standard Software License\", \"price\": 500.00},\n",
" \"mechanical keyboard\": {\"description\": \"Mechanical Keyboard\", \"price\": 120.00}, # Explicit for case matching\n",
" \"hd webcam\": {\"description\": \"HD Webcam\", \"price\": 50.00}, # Explicit for case matching\n",
" \"solar panel\": {\"description\": \"Solar Panel\", \"price\": 300.00},\n",
" \"custom software license\": {\"description\": \"Custom Software License\", \"price\": 200.00}, # Explicit for case matching\n",
" # Add more items as needed\n",
"}\n",
"\n",
"print(\"Simulated local databases created.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WPQEZBJDwTrp",
"outputId": "8adc9665-5de5-4ef6-aba8-03528d27c5bb"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Simulated local databases created.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Model Loading Function\n",
"This cell defines a crucial function responsible for loading the Qwen2-Audio model and its associated processor. It incorporates optimizations for memory efficiency, particularly when running on GPUs.\n"
],
"metadata": {
"id": "K6yIAsY5uom2"
}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CWAVHUV9oUxQ",
"outputId": "3a9307a3-bfd6-4aaf-a5f4-ffdd935d33cc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model loading function defined.\n"
]
}
],
"source": [
"# === Cell 5: Model Loading Function ===\n",
"def load_qwen2_audio_model():\n",
" \"\"\"\n",
" Load the Qwen2-Audio-7B-Instruct model and processor onto the detected device (GPU if available).\n",
" \"\"\"\n",
" global qwen2_audio_processor, qwen2_audio_model, device\n",
"\n",
" if qwen2_audio_processor is None or qwen2_audio_model is None:\n",
" print(f\"Attempting to load Qwen2-Audio model: {QWEN2_AUDIO_MODEL_NAME} on {device}...\")\n",
" try:\n",
" qwen2_audio_processor = AutoProcessor.from_pretrained(QWEN2_AUDIO_MODEL_NAME)\n",
"\n",
" # --- CRITICAL CHANGE FOR VRAM REDUCTION ---\n",
" # Use load_in_8bit or load_in_4bit for quantization if on GPU\n",
" if device == \"cuda\":\n",
" # Option 1: Load in 8-bit (generally good balance of memory and performance)\n",
" qwen2_audio_model = Qwen2AudioForConditionalGeneration.from_pretrained(\n",
" QWEN2_AUDIO_MODEL_NAME,\n",
" load_in_8bit=True, # Load model in 8-bit to drastically reduce VRAM\n",
" device_map=\"auto\",\n",
" torch_dtype=torch.float16, # Still use float16 for activations if possible\n",
" trust_remote_code=True\n",
" )\n",
" print(\"Attempting to load model with 8-bit quantization.\")\n",
" # Option 2: If 8-bit still causes OOM, try 4-bit (more aggressive VRAM reduction, slight perf/quality hit)\n",
" # from transformers import BitsAndBytesConfig\n",
" # qwen2_audio_model = Qwen2AudioForConditionalGeneration.from_pretrained(\n",
" # QWEN2_AUDIO_MODEL_NAME,\n",
" # quantization_config=BitsAndBytesConfig(load_in_4bit=True),\n",
" # device_map=\"auto\",\n",
" # torch_dtype=torch.float16,\n",
" # trust_remote_code=True\n",
" # )\n",
" # print(\"Attempting to load model with 4-bit quantization.\")\n",
" else:\n",
" # For CPU, load in float32 as 8-bit/4-bit is less beneficial and might not be supported.\n",
" qwen2_audio_model = Qwen2AudioForConditionalGeneration.from_pretrained(\n",
" QWEN2_AUDIO_MODEL_NAME,\n",
" device_map=\"auto\", # Still useful for CPU for parallelization if available\n",
" torch_dtype=torch.float32,\n",
" trust_remote_code=True\n",
" )\n",
" print(\"Loading model for CPU in float32.\")\n",
"\n",
"\n",
" qwen2_audio_model.eval() # Set model to evaluation mode\n",
" status_message = f\"Qwen2-Audio model loaded on {device} successfully!\"\n",
" print(status_message)\n",
" return status_message\n",
" except Exception as e:\n",
" error_message = f\"Error loading Qwen2-Audio model: {e}\\n\" \\\n",
" \"Ensure you have a GPU runtime enabled in Colab (Runtime -> Change runtime type). \" \\\n",
" \"If still failing, try a higher-tier Colab GPU (A100) or consider a smaller model.\"\n",
" print(error_message)\n",
" # Clean up potential partial loads\n",
" qwen2_audio_processor = None\n",
" qwen2_audio_model = None\n",
" clear_gpu_memory() # Explicitly clear GPU memory on load failure\n",
" return f\"Failed to load Qwen2-Audio model: {e}\"\n",
" else:\n",
" status_message = \"Qwen2-Audio model already loaded.\"\n",
" print(status_message)\n",
" return status_message\n",
"\n",
"print(\"Model loading function defined.\")"
]
},
{
"cell_type": "markdown",
"source": [
"## Audio Handling and LLM Interaction for Structured Extraction\n",
"This cell is the core of the voice-to-invoice data extraction. It defines functions that take audio input, formulate a prompt for the Qwen2-Audio LLM to extract structured data, process the LLM's response, and then enrich/validate that data using local databases and Pydantic models."
],
"metadata": {
"id": "MPV6nKbWuuDX"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 6: Audio Handling and LLM Interaction for Structured Extraction ===\n",
"\n",
"# Utility function for clearing GPU memory\n",
"def clear_gpu_memory():\n",
" \"\"\"Clears CUDA memory cache and runs garbage collection.\"\"\"\n",
" if torch.cuda.is_available():\n",
" torch.cuda.empty_cache()\n",
" gc.collect() # Python garbage collector\n",
"\n",
"\n",
"def create_qwen_invoice_prompt(audio_file_path: str, prompt_text: str = \"\") -> Dict:\n",
" \"\"\"\n",
" Creates the prompt for the Qwen2-Audio LLM, instructing it to extract structured invoice data.\n",
" The output format is explicitly requested as JSON.\n",
" \"\"\"\n",
" # Load audio for the prompt structure (Qwen2-Audio expects raw audio data here)\n",
" try:\n",
" # Use processor's sampling rate for consistency\n",
" audio, _ = librosa.load(audio_file_path, sr=qwen2_audio_processor.feature_extractor.sampling_rate)\n",
" except Exception as e:\n",
" raise ValueError(f\"Error loading audio file: {e}\")\n",
"\n",
" # Define the desired JSON schema for the LLM. Updated to match Pydantic model closely.\n",
" json_schema = \"\"\"\n",
" {\n",
" \"client_name\": \"string (e.g., John Doe, ACME Corp)\",\n",
" \"client_address\": \"string (e.g., 123 Main St, Anytown, CA)\",\n",
" \"invoice_number\": \"string (optional, e.g., INV-2025-001)\",\n",
" \"invoice_date\": \"YYYY-MM-DD (optional, defaults to today if not specified)\",\n",
" \"due_date\": \"YYYY-MM-DD (optional, defaults to invoice_date + 30 days if not specified)\",\n",
" \"items\": [\n",
" {\n",
" \"description\": \"string (e.g., Gaming Laptop, IT Consulting Service)\",\n",
" \"quantity\": \"float (e.g., 1.0, 0.5, 2.0)\",\n",
" \"unit_price\": \"float (e.g., 1500.00, 25.00)\"\n",
" }\n",
" ],\n",
" \"notes\": \"string (optional, any additional notes for the invoice)\"\n",
" }\n",
" \"\"\"\n",
"\n",
" system_instruction = (\n",
" \"You are an intelligent, autonomous invoice generation agent. \"\n",
" \"Your task is to extract all relevant information for a business invoice from the user's voice input. \"\n",
" \"Strictly adhere to the provided JSON schema for your output. \"\n",
" \"Do NOT include any conversational text or explanations outside the JSON. \"\n",
" \"Only output the JSON object directly. \"\n",
" \"For each item, ensure 'description', 'quantity', and 'unit_price' are extracted. \"\n",
" \"Infer missing quantities as 1.0 if not specified. \"\n",
" \"If a client name or item is ambiguous, try to pick the most likely or provide a best guess \"\n",
" \"that matches common business terms or items you might know.\"\n",
" )\n",
"\n",
" user_query = f\"{prompt_text}\\n\\nExtract the invoice data from the audio and output it directly as a JSON object, following this schema:\\n```json\\n{json_schema}\\n```\"\n",
"\n",
" conversation = [\n",
" {\"role\": \"system\", \"content\": system_instruction},\n",
" {\"role\": \"user\", \"content\": [\n",
" {\"type\": \"audio\", \"audio\": audio},\n",
" {\"type\": \"text\", \"text\": user_query},\n",
" ]},\n",
" ]\n",
" return conversation\n",
"\n",
"\n",
"def extract_and_structure_invoice_data(audio_file_input, user_text_prompt: str) -> tuple[Dict[str, Any], str]:\n",
" \"\"\"\n",
" Processes audio input using Qwen2-Audio to extract structured invoice data,\n",
" then attempts to autofill missing fields from local DBs.\n",
" Returns a dictionary of parsed data and a status message.\n",
" \"\"\"\n",
" global qwen2_audio_processor, qwen2_audio_model, device\n",
"\n",
" if qwen2_audio_processor is None or qwen2_audio_model is None:\n",
" return {}, \"Error: Qwen2-Audio model not loaded. Please load models first by clicking the 'Load Model' button.\"\n",
"\n",
" # Handle Gradio audio input type (can be a string path or a file object with .name)\n",
" audio_file_path = None\n",
" if isinstance(audio_file_input, str):\n",
" audio_file_path = audio_file_input\n",
" elif hasattr(audio_file_input, 'name'):\n",
" audio_file_path = audio_file_input.name\n",
"\n",
" if not audio_file_path or not os.path.exists(audio_file_path):\n",
" return {}, \"Error: No audio file provided or file not found. Please upload or record audio.\"\n",
"\n",
" invoice_data_dict = {}\n",
" status_message = \"Processing audio...\"\n",
"\n",
" try:\n",
" # Step 1: Create Qwen2-Audio prompt with audio and text\n",
" conversation_prompt = create_qwen_invoice_prompt(audio_file_path, user_text_prompt)\n",
"\n",
" # Step 2: Prepare inputs for Qwen2-Audio model\n",
" text = qwen2_audio_processor.apply_chat_template(conversation_prompt, add_generation_prompt=True, tokenize=False)\n",
" audio_for_inference, _ = librosa.load(audio_file_path, sr=qwen2_audio_processor.feature_extractor.sampling_rate)\n",
"\n",
" # --- CORRECTED INPUTS HANDLING ---\n",
" # Get BatchEncoding object from processor\n",
" # Changed 'audios=[audio_for_inference]' to 'audio=audio_for_inference' to resolve FutureWarning\n",
" inputs = qwen2_audio_processor(text=text, audio=audio_for_inference, return_tensors=\"pt\", padding=True)\n",
"\n",
" # Iterate over the items in the BatchEncoding and move each tensor to the device.\n",
" # This modifies the tensors *in place* within the 'inputs' object,\n",
" # preserving its structure that the model expects while ensuring tensors are on GPU.\n",
" for k, v in inputs.items():\n",
" inputs[k] = v.to(device)\n",
" # --- END CORRECTED INPUTS HANDLING ---\n",
"\n",
" # Step 3: Generate response from Qwen2-Audio\n",
" with torch.no_grad():\n",
" # Increased max_new_tokens for potentially longer JSON outputs\n",
" # Using do_sample=False for deterministic and more factual extraction\n",
" generate_ids = qwen2_audio_model.generate(**inputs, max_new_tokens=1024, do_sample=False)\n",
"\n",
" # Decode only the newly generated tokens (excluding the prompt part)\n",
" response_ids = generate_ids[:, inputs.input_ids.size(1):]\n",
" llm_raw_response = qwen2_audio_processor.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n",
"\n",
" # Clean up memory immediately after inference\n",
" del inputs, generate_ids # Ensure all large tensors are deleted\n",
" clear_gpu_memory() # Use the utility function\n",
"\n",
" status_message += f\"\\nLLM Raw Response:\\n{llm_raw_response}\\n\"\n",
"\n",
" # Step 4: Parse LLM response to extract JSON\n",
" # The model might still wrap JSON in ```json...``` even if asked not to.\n",
" json_match = re.search(r\"```json\\n(.*?)\\n```\", llm_raw_response, re.DOTALL)\n",
" if json_match:\n",
" json_string = json_match.group(1).strip()\n",
" else:\n",
" json_string = llm_raw_response.strip() # Assume it's directly JSON if no markdown block\n",
"\n",
" # Attempt to parse the JSON string\n",
" try:\n",
" parsed_llm_data = json.loads(json_string)\n",
" status_message += \"\\nSuccessfully parsed JSON from LLM response.\"\n",
" except json.JSONDecodeError as e:\n",
" status_message += f\"\\nError parsing JSON from LLM response: {e}\\nAttempted JSON string:\\n{json_string}\"\n",
" return {}, status_message + \"\\nFailed to parse structured data. Please refine your voice input or prompt and ensure JSON formatting.\"\n",
"\n",
" # Step 5: Validate and structure with Pydantic\n",
" try:\n",
" # First attempt to instantiate InvoiceData\n",
" invoice_data = InvoiceData(**parsed_llm_data)\n",
" status_message += \"\\nSuccessfully validated initial extracted data with Pydantic schema.\"\n",
"\n",
" # Step 6: Autofill from simulated DBs and handle missing info\n",
" autofill_status_updates = []\n",
"\n",
" # Autofill client details (case-insensitive matching)\n",
" # Prioritize LLM extraction if client_name was provided, then try autofill.\n",
" # If client_name exists, but client_address doesn't, try to find address for that client.\n",
" client_matched_in_db = False\n",
" if invoice_data.client_name:\n",
" for key, data in user_db.items():\n",
" # Check for exact match or substring match in case LLM extracts slightly differently\n",
" if key.lower() == invoice_data.client_name.lower() or key.lower() in invoice_data.client_name.lower():\n",
" if not invoice_data.client_address:\n",
" invoice_data.client_address = data.get(\"address\")\n",
" autofill_status_updates.append(f\"Autofilled client address for '{data['name']}' from DB.\")\n",
" if invoice_data.tax_rate == 0.08 and \"default_tax_rate\" in data: # Only change if default is still active\n",
" invoice_data.tax_rate = data[\"default_tax_rate\"]\n",
" autofill_status_updates.append(f\"Autofilled tax rate for '{data['name']}' from DB: {invoice_data.tax_rate}.\")\n",
" client_matched_in_db = True\n",
" break\n",
"\n",
" # If no client name was extracted by LLM, try to find it in the prompt text\n",
" if not invoice_data.client_name:\n",
" for key, data in user_db.items():\n",
" if key.lower() in user_text_prompt.lower():\n",
" invoice_data.client_name = data.get(\"name\")\n",
" autofill_status_updates.append(f\"Autofilled client name for '{data['name']}' from DB based on text prompt.\")\n",
" if not invoice_data.client_address:\n",
" invoice_data.client_address = data.get(\"address\")\n",
" autofill_status_updates.append(f\"Autofilled client address for '{data['name']}' from DB based on text prompt.\")\n",
" if invoice_data.tax_rate == 0.08 and \"default_tax_rate\" in data:\n",
" invoice_data.tax_rate = data[\"default_tax_rate\"]\n",
" autofill_status_updates.append(f\"Autofilled tax rate for '{data['name']}' from DB: {invoice_data.tax_rate}.\")\n",
" client_matched_in_db = True\n",
" break\n",
"\n",
" # Autofill item prices if not provided or zero (case-insensitive matching)\n",
" for item in invoice_data.items:\n",
" if item.unit_price == 0.0 or item.unit_price is None: # Check for 0.0 as well\n",
" for db_key, db_item_data in item_db.items():\n",
" if db_key.lower() in item.description.lower():\n",
" item.unit_price = db_item_data[\"price\"]\n",
" # Ensure quantity is not None before calculating total\n",
" item.quantity = item.quantity if item.quantity is not None else 1.0\n",
" item.total = item.quantity * item.unit_price # Recalculate total after autofill\n",
" autofill_status_updates.append(f\"Autofilled price for item '{item.description}' from DB.\")\n",
" break\n",
"\n",
" # Recalculate global totals after potential item autofill or tax rate update\n",
" # Ensure items have their totals calculated before summing\n",
" for item in invoice_data.items:\n",
" if item.total is None:\n",
" item.total = (item.quantity if item.quantity is not None else 1.0) * (item.unit_price if item.unit_price is not None else 0.0)\n",
"\n",
" invoice_data.subtotal = sum(item.total for item in invoice_data.items)\n",
" invoice_data.tax_amount = invoice_data.subtotal * invoice_data.tax_rate\n",
" invoice_data.grand_total = invoice_data.subtotal + invoice_data.tax_amount\n",
"\n",
" # Set default invoice and due dates if not provided\n",
" if not invoice_data.invoice_date:\n",
" invoice_data.invoice_date = datetime.date.today().strftime(\"%Y-%m-%d\")\n",
" autofill_status_updates.append(f\"Defaulted invoice date to today: {invoice_data.invoice_date}.\")\n",
"\n",
" if not invoice_data.due_date:\n",
" inv_date_obj = datetime.datetime.strptime(invoice_data.invoice_date, \"%Y-%m-%d\").date()\n",
" invoice_data.due_date = (inv_date_obj + datetime.timedelta(days=30)).strftime(\"%Y-%m-%d\")\n",
" autofill_status_updates.append(f\"Defaulted due date to 30 days from invoice date: {invoice_data.due_date}.\")\n",
"\n",
" # Consolidate autofill status messages\n",
" if autofill_status_updates:\n",
" status_message += \"\\n\" + \"\\n\".join(autofill_status_updates)\n",
"\n",
" # Check for critical missing fields after all autofill attempts\n",
" missing_fields = []\n",
" if not invoice_data.client_name: missing_fields.append(\"Client Name\")\n",
" if not invoice_data.client_address: missing_fields.append(\"Client Address\")\n",
" if not invoice_data.items:\n",
" missing_fields.append(\"Invoice Items (description, quantity, unit price)\")\n",
" elif any(item.unit_price == 0 for item in invoice_data.items):\n",
" missing_fields.append(\"Some item prices (or full item details)\")\n",
"\n",
" if missing_fields:\n",
" status_message += f\"\\n**WARNING:** Missing required information after extraction and autofill: {', '.join(missing_fields)}.\" \\\n",
" \"\\nPlease provide more details in your voice input or prompt.\"\n",
" else:\n",
" status_message += \"\\nAll critical invoice fields successfully extracted or autofilled.\"\n",
"\n",
" invoice_data_dict = invoice_data.model_dump() # Convert Pydantic model to dict for Gradio Json output\n",
"\n",
" except ValidationError as e:\n",
" status_message += f\"\\nError validating data with Pydantic after initial parsing: {e}. Raw data from LLM: {parsed_llm_data}\"\n",
" return {}, status_message + \"\\nData extraction failed validation. Please check audio input/prompt and JSON structure.\"\n",
" except Exception as e:\n",
" status_message += f\"\\nAn unexpected error occurred during data structuring or autofill: {e}\"\n",
" return {}, status_message + \"\\nProcessing error. Please try again or check console for details.\"\n",
"\n",
" except ValueError as e: # Catch errors from create_qwen_invoice_prompt (e.g., audio load)\n",
" status_message += f\"\\nError during audio processing for Qwen2-Audio: {e}\"\n",
" return {}, status_message + \"\\nAudio input issue. Please ensure your audio file is valid.\"\n",
" except Exception as e:\n",
" status_message += f\"\\nAn general error occurred during Qwen2-Audio processing: {e}\"\n",
" return {}, status_message + \"\\nTranscription or LLM generation failed. Check model loading and console.\"\n",
"\n",
" return invoice_data_dict, status_message\n",
"\n",
"print(\"Audio handling and LLM interaction functions defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ELN4Ke45oaMN",
"outputId": "0b36620f-f5f0-4cd1-d475-0a06ecbf869b"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Audio handling and LLM interaction functions defined.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## PDF Generation Function (Enhanced)\n",
"This cell defines the function responsible for taking the structured invoice data and rendering it into a professional, printable PDF document using the ReportLab library."
],
"metadata": {
"id": "7InRPvu7u3Nl"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 7: PDF Generation Function (Enhanced) ===\n",
"\n",
"def generate_invoice_pdf(invoice_data: Dict[str, Any]) -> str:\n",
" \"\"\"\n",
" Generates a professional PDF invoice from structured invoice data.\n",
" Returns the path to the generated PDF or an error message.\n",
" \"\"\"\n",
" if not invoice_data:\n",
" return \"Error: No invoice data provided to generate PDF.\"\n",
"\n",
" # Convert dict back to Pydantic model for easier attribute access and calculations\n",
" try:\n",
" invoice = InvoiceData(**invoice_data)\n",
" except ValidationError as e:\n",
" return f\"Error: Invalid invoice data schema for PDF generation: {e}\"\n",
"\n",
" # Basic check for critical missing data before generating PDF\n",
" if not invoice.client_name or not invoice.client_address or not invoice.items:\n",
" missing_parts = []\n",
" if not invoice.client_name: missing_parts.append(\"Client Name\")\n",
" if not invoice.client_address: missing_parts.append(\"Client Address\")\n",
" if not invoice.items: missing_parts.append(\"Invoice Items\")\n",
" return f\"Error: Cannot generate PDF due to missing critical information: {', '.join(missing_parts)}. Please provide more details.\"\n",
"\n",
" if any(item.unit_price == 0 for item in invoice.items):\n",
" return \"Error: Cannot generate PDF as some item prices are zero. Please ensure all items have a valid price.\"\n",
"\n",
"\n",
" # Generate a unique filename\n",
" client_name_safe = re.sub(r'[^a-zA-Z0-9_]', '', (invoice.client_name or \"unknown_client\").replace(\" \", \"_\"))\n",
" timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
" output_filename = f\"invoice_{client_name_safe}_{timestamp}.pdf\"\n",
" file_path = os.path.join(LOCAL_PDF_FOLDER, output_filename)\n",
"\n",
" doc = SimpleDocTemplate(file_path, pagesize=A4,\n",
" rightMargin=inch/4, leftMargin=inch/4,\n",
" topMargin=inch/4, bottomMargin=inch/4)\n",
" styles = getSampleStyleSheet()\n",
"\n",
" elements = []\n",
"\n",
" # --- Header ---\n",
" elements.append(Paragraph(\"INVOICE\", styles['h1']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # Company Info (Simulated - update with your actual company details)\n",
" elements.append(Paragraph(\"Your Company Name LLC\", styles['h3']))\n",
" elements.append(Paragraph(\"123 Business Way, Corporate City, CC 98765\", styles['Normal']))\n",
" elements.append(Paragraph(\"Phone: (123) 456-7890 | Email: invoices@yourcompany.com | Website: www.yourcompany.com\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # Invoice Details and Client Details Side-by-Side (using a table for layout)\n",
" header_data = [\n",
" [\n",
" Paragraph(\"BILL TO:\", styles['h3']),\n",
" Paragraph(\"INVOICE DETAILS:\", styles['h3'])\n",
" ],\n",
" [\n",
" Paragraph(f\"{invoice.client_name or 'N/A'}\", styles['Normal']),\n",
" Paragraph(f\"Invoice #: {invoice.invoice_number or 'N/A'}\", styles['Normal'])\n",
" ],\n",
" [\n",
" Paragraph(invoice.client_address or \"N/A\", styles['Normal']),\n",
" Paragraph(f\"Invoice Date: {invoice.invoice_date or 'N/A'}\", styles['Normal'])\n",
" ],\n",
" [\n",
" \"\", # Placeholder for client email if you add it\n",
" Paragraph(f\"Due Date: {invoice.due_date or 'N/A'}\", styles['Normal'])\n",
" ]\n",
" ]\n",
"\n",
" header_table = Table(header_data, colWidths=[4*inch, 3*inch])\n",
" header_table.setStyle(TableStyle([\n",
" ('ALIGN', (0,0), (-1,-1), 'LEFT'),\n",
" ('VALIGN', (0,0), (-1,-1), 'TOP'),\n",
" ('BOTTOMPADDING', (0,0), (-1,0), 6),\n",
" ('SPAN', (0,0), (0,0)), # Span \"BILL TO\" across its column\n",
" ('SPAN', (1,0), (1,0)), # Span \"INVOICE DETAILS\"\n",
" ]))\n",
" elements.append(header_table)\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # --- Items Table ---\n",
" data = [['Description', 'Qty', 'Unit Price', 'Total']]\n",
" for item in invoice.items:\n",
" data.append([\n",
" item.description,\n",
" f\"{item.quantity:.2f}\",\n",
" f\"${item.unit_price:.2f}\",\n",
" f\"${item.total:.2f}\"\n",
" ])\n",
"\n",
" table_style = TableStyle([\n",
" ('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#F2F2F2')), # Light grey header\n",
" ('TEXTCOLOR', (0, 0), (-1, 0), colors.black),\n",
" ('ALIGN', (0, 0), (-1, -1), 'RIGHT'), # Align all columns to right by default\n",
" ('ALIGN', (0, 0), (0, -1), 'LEFT'), # Except description column, align left\n",
" ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),\n",
" ('BOTTOMPADDING', (0, 0), (-1, 0), 12),\n",
" ('BACKGROUND', (0, 1), (-1, -1), colors.white),\n",
" ('GRID', (0, 0), (-1, -1), 0.5, colors.HexColor('#CCCCCC')), # Lighter grid lines\n",
" ('BOX', (0, 0), (-1, -1), 1, colors.HexColor('#888888')), # Border around table\n",
" ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),\n",
" ('TOPPADDING', (0,1), (-1,-1), 6), # Add padding to content rows\n",
" ('BOTTOMPADDING', (0,1), (-1,-1), 6),\n",
" ])\n",
"\n",
" # Calculate column widths to fit A4 page\n",
" # A4 width ~ 8.27 inches. Usable width after margins = 8.27 - 0.5 = 7.77 inches\n",
" # Proportionally adjusted: 4:0.75:1:1 -> 4/6.75, 0.75/6.75, 1/6.75, 1/6.75 of usable width\n",
" total_proportion = 4 + 0.75 + 1 + 1 # Sum of example proportions\n",
" usable_width = 7.77 * inch\n",
" col_widths = [\n",
" (4/total_proportion) * usable_width,\n",
" (0.75/total_proportion) * usable_width,\n",
" (1/total_proportion) * usable_width,\n",
" (1/total_proportion) * usable_width\n",
" ]\n",
"\n",
" # Check if data has more than just the header row\n",
" if len(data) > 1:\n",
" table = Table(data, colWidths=col_widths)\n",
" table.setStyle(table_style)\n",
" elements.append(table)\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
" else:\n",
" elements.append(Paragraph(\"No items found for this invoice.\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # --- Totals ---\n",
" totals_data = [\n",
" [\"Subtotal:\", f\"${invoice.subtotal:.2f}\"],\n",
" [f\"Tax ({invoice.tax_rate*100:.0f}%):\", f\"${invoice.tax_amount:.2f}\"],\n",
" [\"GRAND TOTAL:\", f\"${invoice.grand_total:.2f}\"]\n",
" ]\n",
"\n",
" # Use a table for totals for consistent right alignment\n",
" totals_table = Table(totals_data, colWidths=[col_widths[0] + col_widths[1] + col_widths[2], col_widths[3]]) # Left columns combined for label, right for value\n",
" totals_table.setStyle(TableStyle([\n",
" ('ALIGN', (0, 0), (-1, -1), 'RIGHT'),\n",
" ('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),\n",
" ('TOPPADDING', (0, 0), (-1, -1), 4),\n",
" ('BOTTOMPADDING', (0, 0), (-1, -1), 4),\n",
" ]))\n",
" elements.append(totals_table)\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # --- Notes ---\n",
" if invoice.notes:\n",
" elements.append(Paragraph(\"Notes:\", styles['h3']))\n",
" elements.append(Paragraph(invoice.notes, styles['Normal']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # --- Footer (Optional) ---\n",
" elements.append(Spacer(1, 0.5 * inch))\n",
" elements.append(Paragraph(\"Thank you for your business!\", styles['Italic']))\n",
"\n",
" # Build PDF\n",
" try:\n",
" doc.build(elements)\n",
" print(f\"Invoice PDF generated successfully: {file_path}\")\n",
" return file_path\n",
" except Exception as e:\n",
" print(f\"Error generating PDF: {e}\")\n",
" return f\"Error: Failed to generate PDF: {e}\"\n",
"\n",
"print(\"PDF generation function defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zRjj2J5jz9I3",
"outputId": "18fe34ca-e7bb-49c7-d957-35452dd2f888"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"PDF generation function defined.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Gradio UI Definition and Launch (Experiment Draft2: For Gradio UI : Adding Confirm Audio Button for confirming audio input)\n",
"This cell constructs the web-based user interface using the Gradio library, allowing users to interact with the invoice generation system through a browser. It defines the layout, input/output components, and the flow of interactions."
],
"metadata": {
"id": "FCWZnON9doGK"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 8: Gradio UI Definition and Launch ===\n",
"\n",
"# Define check_model_devices_ui function (from previous updates, adapted for Qwen2-Audio)\n",
"def check_model_devices_ui():\n",
" global qwen2_audio_model, device\n",
"\n",
" status_output = []\n",
" status_output.append(\"--- Checking Model Device Placement ---\")\n",
"\n",
" if qwen2_audio_model:\n",
" try:\n",
" # Check if model has a device map (for multi-GPU or distributed loads)\n",
" if hasattr(qwen2_audio_model, 'hf_device_map') and qwen2_audio_model.hf_device_map:\n",
" status_output.append(f\"Qwen2-Audio model device map: {qwen2_audio_model.hf_device_map}\")\n",
" else:\n",
" # Fallback to checking device of first parameter for single-device models\n",
" qwen_device = next(qwen2_audio_model.parameters()).device\n",
" status_output.append(f\"Qwen2-Audio model (first parameter) is on: {qwen_device}\")\n",
"\n",
" status_output.append(f\"Qwen2-Audio model data type: {next(qwen2_audio_model.parameters()).dtype}\")\n",
"\n",
" if device == \"cuda\":\n",
" status_output.append(f\"Current GPU Memory Allocated: {torch.cuda.memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Current GPU Memory Cached: {torch.cuda.memory_reserved() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Allocated: {torch.cuda.max_memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Reserved: {torch.cuda.max_memory_reserved() / (1024**3):.2f} GB\")\n",
"\n",
" except Exception as e:\n",
" status_output.append(f\"Error checking Qwen2-Audio device: {e}\")\n",
" else:\n",
" status_output.append(\"Qwen2-Audio model not yet loaded (qwen2_audio_model is None).\")\n",
"\n",
" status_output.append(\"\\n--- Global Device Information ---\")\n",
" status_output.append(f\"Overall CUDA availability from torch: {torch.cuda.is_available()}\")\n",
" if torch.cuda.is_available():\n",
" status_output.append(f\"Number of CUDA devices: {torch.cuda.device_count()}\")\n",
" if torch.cuda.device_count() > 0:\n",
" status_output.append(f\"Current CUDA device: {torch.cuda.current_device()} ({torch.cuda.get_device_name(torch.cuda.current_device())})\")\n",
" status_output.append(f\"Total GPU Memory on current device: {torch.cuda.get_device_properties(torch.cuda.current_device()).total_memory / (1024**3):.2f} GB\")\n",
" else:\n",
" status_output.append(\"No CUDA devices detected. Running on CPU.\")\n",
"\n",
" return \"\\n\".join(status_output)\n",
"\n",
"\n",
"with gr.Blocks(title=\"Voice-Powered Autonomous Invoice Generator\") as full_app:\n",
" gr.Markdown(\"# 🎙️ Voice-Powered Autonomous Invoice Generator\")\n",
" gr.Markdown(\"This system processes voice commands to extract invoice details and generate a professional PDF invoice.\")\n",
"\n",
" current_audio_path_state = gr.State(None)\n",
" extracted_invoice_data_state = gr.State({}) # Stores the Pydantic-validated dict\n",
"\n",
" with gr.Tab(\"Invoice Generation Workflow\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 1: Load AI Model\")\n",
" load_model_button = gr.Button(\"Click to Load Qwen2-Audio Model\", variant=\"primary\")\n",
" model_load_status = gr.Textbox(label=\"Model Loading Status\", interactive=False, lines=3, value=\"Model not loaded yet. Click 'Load Model' to begin.\")\n",
" load_model_button.click(load_qwen2_audio_model, inputs=[], outputs=model_load_status)\n",
"\n",
" with gr.Accordion(\"Model Device Diagnostics\", open=False):\n",
" check_device_button = gr.Button(\"Check Model Device Placement\", variant=\"secondary\")\n",
" device_status_output = gr.Textbox(label=\"Device Status\", interactive=False, lines=10)\n",
" check_device_button.click(check_model_devices_ui, inputs=[], outputs=device_status_output)\n",
"\n",
" gr.Markdown(\"### Step 2: Audio Input (Upload or Record Invoice Request)\")\n",
" with gr.Tab(\"Upload Audio\"):\n",
" upload_audio_input = gr.Audio(type=\"filepath\", label=\"Upload an audio file (e.g., MP3, WAV, OGG)\", sources=[\"upload\"])\n",
" confirm_upload_button = gr.Button(\"✅ Confirm Uploaded Audio\", variant=\"secondary\")\n",
" upload_audio_status = gr.Textbox(label=\"Upload Status\", interactive=False)\n",
"\n",
" confirm_upload_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),\n",
" inputs=[upload_audio_input],\n",
" outputs=[current_audio_path_state, upload_audio_status]\n",
" )\n",
"\n",
" with gr.Tab(\"Record Audio\"):\n",
" record_audio_input = gr.Audio(type=\"filepath\", label=\"Record invoice request using your microphone\", sources=[\"microphone\"])\n",
" confirm_record_button = gr.Button(\"✅ Confirm Recorded Audio\", variant=\"secondary\")\n",
" record_audio_status = gr.Textbox(label=\"Record Status\", interactive=False)\n",
"\n",
" confirm_record_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),\n",
" inputs=[record_audio_input],\n",
" outputs=[current_audio_path_state, record_audio_status]\n",
" )\n",
"\n",
" user_text_prompt = gr.Textbox(\n",
" label=\"Optional Text Prompt for Context\",\n",
" value=\"Please generate a professional invoice from this voice input.\",\n",
" info=\"Add additional text instructions or context for the AI.\"\n",
" )\n",
"\n",
" gr.Markdown(\"### Step 3: Extract Invoice Details\")\n",
" extract_button = gr.Button(\"Extract Invoice Details from Voice\", variant=\"primary\")\n",
" extraction_status = gr.Textbox(label=\"Extraction & Autofill Status\", interactive=False, lines=5)\n",
" extracted_data_json = gr.Json(label=\"Structured Invoice Data (JSON)\", value={}, open=False)\n",
"\n",
" extract_button.click(\n",
" extract_and_structure_invoice_data,\n",
" inputs=[current_audio_path_state, user_text_prompt],\n",
" outputs=[extracted_invoice_data_state, extraction_status]\n",
" ).then(\n",
" lambda data: data,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[extracted_data_json]\n",
" )\n",
"\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 4: Generate Invoice PDF\")\n",
" generate_pdf_button = gr.Button(\"Generate Invoice PDF\", variant=\"secondary\")\n",
" pdf_output_file = gr.File(label=\"Download Generated Invoice (PDF)\")\n",
" pdf_generation_status = gr.Textbox(label=\"PDF Generation Status\", interactive=False)\n",
"\n",
" generate_pdf_button.click(\n",
" generate_invoice_pdf,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[pdf_output_file]\n",
" ).then(\n",
" lambda x: \"PDF generated successfully! Download from above.\" if x and os.path.exists(x) else \"PDF generation failed. Check console for errors or if data was extracted.\",\n",
" inputs=[pdf_output_file],\n",
" outputs=[pdf_generation_status]\n",
" )\n",
" gr.Markdown(f\"*(Generated PDFs will be saved in: `{LOCAL_PDF_FOLDER}` on Colab runtime. You can download them directly from the 'Download Generated Invoice' link.)*\")\n",
"\n",
" with gr.Accordion(\"Usage Notes & Tips\", open=False):\n",
" gr.Markdown(\"\"\"\n",
" * **Workflow:** Follow the steps linearly: **1) Load Model** -> **2) Provide Audio** -> **3) Extract Details** -> **4) Generate PDF**.\n",
" * **GPU Runtime:** Ensure your Google Colab runtime type is set to GPU (Runtime -> Change runtime type -> T4 GPU or A100 GPU). The model requires significant VRAM.\n",
" * **Voice Input:** Speak clearly, mentioning client names, item descriptions, quantities, and prices. Example: \"Generate an invoice for John Doe for two laptops at fifteen hundred each, and one wireless mouse for twenty-five dollars.\"\n",
" * **Optional Prompt:** Use the text prompt to provide additional context or clarify ambiguities if the voice input alone isn't sufficient.\n",
" * **Autofill:** The system will attempt to autofill client addresses and known item prices from its internal simulated databases (`user_db`, `item_db`). You can modify these databases in Cell 4.\n",
" * **Missing Info:** If critical details are missing after extraction and autofill, the \"Extraction Status\" will alert you. You would then need to provide more specific voice input or a text prompt.\n",
" * **PDF Output:** The generated PDF will include standard invoice fields, a table for items, and calculated totals.\n",
" \"\"\")\n",
"\n",
"full_app.launch(debug=True, share=True)\n",
"print(\"Gradio app launched!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"id": "MGVt_jJHdnuF",
"outputId": "7d5cdbeb-ea93-4cbc-d9cb-dd4a19dcc3d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"* Running on public URL: https://368a5c0f588b64c081.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
""
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Attempting to load Qwen2-Audio model: Qwen/Qwen2-Audio-7B-Instruct on cuda...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"preprocessor_config.json: 0%| | 0.00/342 [00:00 None:\n",
" if self.total is None:\n",
" self.total = self.quantity * self.unit_price\n",
"\n",
"# Pydantic model for the entire invoice data\n",
"class InvoiceData(BaseModel):\n",
" client_name: Optional[str] = None\n",
" client_address: Optional[str] = None\n",
" invoice_number: Optional[str] = None # Can be generated if missing\n",
" invoice_date: Optional[str] = None # Will default to today if missing\n",
" due_date: Optional[str] = None # Will default to invoice_date + 30 days if missing\n",
" items: List[InvoiceItem] = Field(default_factory=list)\n",
" subtotal: Optional[float] = None\n",
" tax_rate: Optional[float] = 0.08 # Default 8% tax\n",
" tax_amount: Optional[float] = None\n",
" grand_total: Optional[float] = None\n",
" notes: Optional[str] = None\n",
"\n",
" # Post-initialization to calculate totals if not provided by LLM\n",
" def model_post_init(self, __context: Any) -> None:\n",
" self.subtotal = sum(item.total for item in self.items) if self.items else 0.0\n",
" self.tax_amount = self.subtotal * self.tax_rate\n",
" self.grand_total = self.subtotal + self.tax_amount\n",
"\n",
"print(\"Pydantic data schemas defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OsRxRNpm0bOk",
"outputId": "adf3c49c-106a-44e6-acb2-f5e94f891fef"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Pydantic data schemas defined.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# === Cell 4: Simulated Local DB for Autofill ===\n",
"# Extend with more realistic data and potentially more fields (e.g., client email, phone)\n",
"user_db = {\n",
" \"john doe\": {\"name\": \"John Doe\", \"address\": \"123 Elm St, Springfield, IL\", \"email\": \"john.doe@example.com\", \"default_tax_rate\": 0.07},\n",
" \"acme corp\": {\"name\": \"ACME Corporation\", \"address\": \"456 Oak Ave, Metropolis, NY\", \"email\": \"info@acmecorp.com\", \"default_tax_rate\": 0.09},\n",
" # Add more users/clients\n",
"}\n",
"item_db = {\n",
" \"laptop\": {\"description\": \"Gaming Laptop\", \"price\": 1500.00},\n",
" \"wireless mouse\": {\"description\": \"Ergonomic Wireless Mouse\", \"price\": 25.00},\n",
" \"keyboard\": {\"description\": \"Mechanical Keyboard\", \"price\": 120.00},\n",
" \"webcam\": {\"description\": \"HD Webcam\", \"price\": 50.00},\n",
" \"consulting service\": {\"description\": \"IT Consulting Service (per hour)\", \"price\": 100.00},\n",
" # Add more items\n",
"}\n",
"\n",
"print(\"Simulated local databases created.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Gtpav8ET0ece",
"outputId": "3680adb1-4fe5-4598-9547-8231d8297931"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Simulated local databases created.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# === Cell 5: Model Loading Function ===\n",
"def load_qwen2_audio_model():\n",
" \"\"\"\n",
" Load the Qwen2-Audio-7B-Instruct model and processor.\n",
" \"\"\"\n",
" global qwen2_audio_processor, qwen2_audio_model, device\n",
"\n",
" if qwen2_audio_processor is None or qwen2_audio_model is None:\n",
" print(f\"Loading Qwen2-Audio model: {QWEN2_AUDIO_MODEL_NAME} on {device}...\")\n",
" try:\n",
" qwen2_audio_processor = AutoProcessor.from_pretrained(QWEN2_AUDIO_MODEL_NAME)\n",
" qwen2_audio_model = Qwen2AudioForConditionalGeneration.from_pretrained(\n",
" QWEN2_AUDIO_MODEL_NAME,\n",
" device_map=\"auto\", # Automatically distributes model layers across available GPUs\n",
" torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n",
" trust_remote_code=True # Required for some custom model components\n",
" )\n",
" print(f\"Qwen2-Audio model loaded on {device} successfully.\")\n",
" return \"Qwen2-Audio model loaded successfully!\"\n",
" except Exception as e:\n",
" error_message = f\"Error loading Qwen2-Audio model: {e}\"\n",
" print(error_message)\n",
" return f\"Failed to load Qwen2-Audio model: {e}\"\n",
" else:\n",
" return \"Qwen2-Audio model already loaded.\"\n",
"\n",
"print(\"Model loading function defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5ocCicSJ0iAG",
"outputId": "ee4ccbe2-38dd-4422-9c9e-c6a9482697f2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model loading function defined.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# === Cell 6: Audio Handling and LLM Interaction for Structured Extraction ===\n",
"\n",
"def create_qwen_invoice_prompt(audio_file_path: str, prompt_text: str = \"\") -> Dict:\n",
" \"\"\"\n",
" Creates the prompt for the Qwen2-Audio LLM, instructing it to extract structured invoice data.\n",
" The output format is explicitly requested as JSON.\n",
" \"\"\"\n",
" # Load audio for the prompt structure (Qwen2-Audio expects raw audio data here)\n",
" try:\n",
" audio, _ = librosa.load(audio_file_path, sr=qwen2_audio_processor.feature_extractor.sampling_rate)\n",
" except Exception as e:\n",
" raise ValueError(f\"Error loading audio file: {e}\")\n",
"\n",
" # Define the desired JSON schema for the LLM\n",
" json_schema = \"\"\"\n",
" {\n",
" \"client_name\": \"string (required if known)\",\n",
" \"client_address\": \"string (required if known)\",\n",
" \"invoice_number\": \"string (optional)\",\n",
" \"invoice_date\": \"YYYY-MM-DD (optional, defaults to today)\",\n",
" \"due_date\": \"YYYY-MM-DD (optional, defaults to invoice_date + 30 days)\",\n",
" \"items\": [\n",
" {\n",
" \"description\": \"string (required)\",\n",
" \"quantity\": \"float (required)\",\n",
" \"unit_price\": \"float (required)\"\n",
" }\n",
" ],\n",
" \"notes\": \"string (optional)\"\n",
" }\n",
" \"\"\"\n",
"\n",
" system_instruction = (\n",
" \"You are an intelligent, autonomous invoice generation agent. \"\n",
" \"Your task is to extract all relevant information for a business invoice from the user's voice input. \"\n",
" \"Strictly adhere to the provided JSON schema for your output. \"\n",
" \"Do NOT include any conversational text or explanations outside the JSON. \"\n",
" \"If an item's description, quantity, or unit_price is explicitly mentioned, prioritize that. \"\n",
" \"Infer missing values where logical (e.g., 'three laptops' means quantity 3, item 'laptop'). \"\n",
" \"If a client name or item is ambiguous, try to pick the most likely or provide a best guess.\"\n",
" )\n",
"\n",
" user_query = f\"{prompt_text}\\n\\nHere is the desired JSON structure for the invoice data. Fill it based on the audio input:\\n```json\\n{json_schema}\\n```\"\n",
"\n",
" conversation = [\n",
" {\"role\": \"system\", \"content\": system_instruction},\n",
" {\"role\": \"user\", \"content\": [\n",
" {\"type\": \"audio\", \"audio\": audio},\n",
" {\"type\": \"text\", \"text\": user_query},\n",
" ]},\n",
" ]\n",
" return conversation\n",
"\n",
"\n",
"def extract_and_structure_invoice_data(audio_file_path: str, user_text_prompt: str) -> Dict[str, Any]:\n",
" \"\"\"\n",
" Processes audio input using Qwen2-Audio to extract structured invoice data,\n",
" then attempts to autofill missing fields from local DBs.\n",
" Returns a dictionary of parsed data and a status message.\n",
" \"\"\"\n",
" global qwen2_audio_processor, qwen2_audio_model, device\n",
"\n",
" if qwen2_audio_processor is None or qwen2_audio_model is None:\n",
" return {}, \"Error: Qwen2-Audio model not loaded. Please load models first.\"\n",
" if not audio_file_path or not os.path.exists(audio_file_path):\n",
" return {}, \"Error: No audio file provided or file not found.\"\n",
"\n",
" invoice_data_dict = {}\n",
" status_message = \"Processing audio...\"\n",
"\n",
" try:\n",
" # Step 1: Create Qwen2-Audio prompt with audio and text\n",
" conversation_prompt = create_qwen_invoice_prompt(audio_file_path, user_text_prompt)\n",
"\n",
" # Step 2: Prepare inputs for Qwen2-Audio model\n",
" # apply_chat_template processes the conversation into a single string\n",
" # then processor tokenizes it and adds audio features\n",
" text = qwen2_audio_processor.apply_chat_template(conversation_prompt, add_generation_prompt=True, tokenize=False)\n",
" audio_for_inference, _ = librosa.load(audio_file_path, sr=qwen2_audio_processor.feature_extractor.sampling_rate)\n",
"\n",
" inputs = qwen2_audio_processor(text=text, audios=[audio_for_inference], return_tensors=\"pt\", padding=True)\n",
" inputs = {k: v.to(device) for k, v in inputs.items()} # Move inputs to device\n",
"\n",
" # Step 3: Generate response from Qwen2-Audio\n",
" with torch.no_grad():\n",
" generate_ids = qwen2_audio_model.generate(**inputs, max_new_tokens=1024, do_sample=False) # Use max_new_tokens for potentially larger JSON\n",
"\n",
" # Decode only the newly generated tokens\n",
" response_ids = generate_ids[:, inputs.input_ids.size(1):]\n",
" llm_raw_response = qwen2_audio_processor.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n",
"\n",
" # Clean up memory\n",
" del inputs, generate_ids\n",
" if device == \"cuda\":\n",
" torch.cuda.empty_cache()\n",
" torch.cuda.synchronize()\n",
" gc.collect()\n",
"\n",
" status_message += f\"\\nLLM Raw Response:\\n{llm_raw_response}\\n\"\n",
"\n",
" # Step 4: Parse LLM response to extract JSON\n",
" # Qwen2-Audio might wrap JSON in ```json...```\n",
" json_match = re.search(r\"```json\\n(.*?)\\n```\", llm_raw_response, re.DOTALL)\n",
" if json_match:\n",
" json_string = json_match.group(1).strip()\n",
" else:\n",
" json_string = llm_raw_response.strip() # Assume it's directly JSON if no markdown block\n",
"\n",
" # Attempt to parse the JSON string\n",
" try:\n",
" parsed_llm_data = json.loads(json_string)\n",
" status_message += \"\\nSuccessfully parsed JSON from LLM response.\"\n",
" except json.JSONDecodeError as e:\n",
" status_message += f\"\\nError parsing JSON from LLM response: {e}\\nRaw JSON attempt:\\n{json_string}\"\n",
" return {}, status_message + \"\\nFailed to parse structured data. Please refine your voice input or prompt.\"\n",
"\n",
" # Step 5: Validate and structure with Pydantic\n",
" try:\n",
" invoice_data = InvoiceData(**parsed_llm_data)\n",
" status_message += \"\\nSuccessfully validated extracted data with Pydantic schema.\"\n",
"\n",
" # Step 6: Autofill from simulated DBs and handle missing info\n",
" initial_missing = []\n",
" if not invoice_data.client_name:\n",
" initial_missing.append(\"Client Name\")\n",
" if not invoice_data.client_address:\n",
" initial_missing.append(\"Client Address\")\n",
" if not invoice_data.items:\n",
" initial_missing.append(\"Invoice Items\")\n",
"\n",
" # Try to autofill client details\n",
" if not invoice_data.client_name and any(key in user_text_prompt.lower() for key in user_db.keys()):\n",
" for key, data in user_db.items():\n",
" if key in user_text_prompt.lower():\n",
" invoice_data.client_name = data.get(\"name\")\n",
" invoice_data.client_address = data.get(\"address\")\n",
" # You could also set default tax rate per client\n",
" if \"Client Name\" in initial_missing: initial_missing.remove(\"Client Name\")\n",
" if \"Client Address\" in initial_missing: initial_missing.remove(\"Client Address\")\n",
" status_message += f\"\\nAutofilled client details for '{data['name']}' from DB.\"\n",
" break\n",
"\n",
" # Try to autofill item prices if only description and quantity are present\n",
" for item in invoice_data.items:\n",
" if item.unit_price == 0 and item.description.lower() in item_db:\n",
" item.unit_price = item_db[item.description.lower()][\"price\"]\n",
" status_message += f\"\\nAutofilled price for item '{item.description}' from DB.\"\n",
" # Re-calculate total for the item\n",
" item.total = item.quantity * item.unit_price\n",
"\n",
" # Recalculate totals after potential autofill\n",
" invoice_data.subtotal = sum(item.total for item in invoice_data.items)\n",
" invoice_data.tax_amount = invoice_data.subtotal * invoice_data.tax_rate\n",
" invoice_data.grand_total = invoice_data.subtotal + invoice_data.tax_amount\n",
"\n",
" # Set default invoice and due dates if not provided\n",
" if not invoice_data.invoice_date:\n",
" invoice_data.invoice_date = datetime.date.today().strftime(\"%Y-%m-%d\")\n",
" status_message += f\"\\nDefaulted invoice date to today: {invoice_data.invoice_date}.\"\n",
"\n",
" if not invoice_data.due_date:\n",
" # Default due date to 30 days from invoice date\n",
" inv_date_obj = datetime.datetime.strptime(invoice_data.invoice_date, \"%Y-%m-%d\").date()\n",
" invoice_data.due_date = (inv_date_obj + datetime.timedelta(days=30)).strftime(\"%Y-%m-%d\")\n",
" status_message += f\"\\nDefaulted due date to 30 days from invoice date: {invoice_data.due_date}.\"\n",
"\n",
" missing_after_autofill = []\n",
" if not invoice_data.client_name: missing_after_autofill.append(\"Client Name\")\n",
" if not invoice_data.client_address: missing_after_autofill.append(\"Client Address\")\n",
" if not invoice_data.items or any(item.unit_price == 0 for item in invoice_data.items):\n",
" missing_after_autofill.append(\"Item prices or full item details (description, quantity, price)\")\n",
"\n",
" if missing_after_autofill:\n",
" status_message += f\"\\nWARNING: Missing required information after autofill: {', '.join(missing_after_autofill)}. Please provide more details.\"\n",
" # For agentic flow, this is where you'd loop back to prompt the user\n",
" else:\n",
" status_message += \"\\nAll required invoice fields successfully extracted or autofilled.\"\n",
"\n",
" invoice_data_dict = invoice_data.model_dump() # Convert Pydantic model to dict\n",
"\n",
" except ValidationError as e:\n",
" status_message += f\"\\nError validating data with Pydantic: {e}. Raw parsed data: {parsed_llm_data}\"\n",
" return {}, status_message + \"\\nData extraction failed validation. Please check audio input.\"\n",
" except Exception as e:\n",
" status_message += f\"\\nAn unexpected error occurred during data structuring or autofill: {e}\"\n",
" return {}, status_message + \"\\nProcessing error. Please try again.\"\n",
"\n",
" except Exception as e:\n",
" status_message += f\"\\nAn error occurred during Qwen2-Audio processing: {e}\"\n",
" return {}, status_message + \"\\nTranscription or LLM generation failed.\"\n",
"\n",
" return invoice_data_dict, status_message\n",
"\n",
"print(\"Audio handling and LLM interaction functions defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w54RVGBN0lMV",
"outputId": "6220069a-f539-4926-f217-8a26bf4039aa"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Audio handling and LLM interaction functions defined.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# === Cell 7: PDF Generation Function (Enhanced) ===\n",
"\n",
"def generate_invoice_pdf(invoice_data: Dict[str, Any]) -> str:\n",
" \"\"\"\n",
" Generates a professional PDF invoice from structured invoice data.\n",
" \"\"\"\n",
" if not invoice_data:\n",
" return \"Error: No invoice data provided to generate PDF.\"\n",
"\n",
" # Convert dict back to Pydantic model for easier access to attributes\n",
" try:\n",
" invoice = InvoiceData(**invoice_data)\n",
" except ValidationError as e:\n",
" return f\"Error: Invalid invoice data schema for PDF generation: {e}\"\n",
"\n",
" # Generate a unique filename\n",
" client_name_safe = re.sub(r'[^a-zA-Z0-9_]', '', (invoice.client_name or \"unknown_client\").replace(\" \", \"_\"))\n",
" timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
" output_filename = f\"invoice_{client_name_safe}_{timestamp}.pdf\"\n",
" file_path = os.path.join(LOCAL_PDF_FOLDER, output_filename)\n",
"\n",
" doc = SimpleDocTemplate(file_path, pagesize=A4,\n",
" rightMargin=inch/4, leftMargin=inch/4,\n",
" topMargin=inch/4, bottomMargin=inch/4)\n",
" styles = getSampleStyleSheet()\n",
"\n",
" elements = []\n",
"\n",
" # --- Header ---\n",
" elements.append(Paragraph(\"INVOICE\", styles['h1']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # Company Info (Simulated)\n",
" elements.append(Paragraph(\"Your Company Name\", styles['h3']))\n",
" elements.append(Paragraph(\"Your Company Address, City, Zip\", styles['Normal']))\n",
" elements.append(Paragraph(\"Phone: (123) 456-7890 | Email: info@yourcompany.com\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # Invoice Details and Client Details Side-by-Side\n",
" invoice_details_data = [\n",
" [\"Invoice #:\", invoice.invoice_number or \"N/A\"],\n",
" [\"Invoice Date:\", invoice.invoice_date or \"N/A\"],\n",
" [\"Due Date:\", invoice.due_date or \"N/A\"]\n",
" ]\n",
" client_details_data = [\n",
" [\"BILL TO:\"],\n",
" [invoice.client_name or \"N/A\"],\n",
" [invoice.client_address or \"N/A\"],\n",
" # Add client email if available in your Pydantic model or DB\n",
" ]\n",
"\n",
" # Use a nested table or two tables to align these\n",
" # For simplicity, let's put them linearly for now, or create a complex table for layout\n",
" # A more robust layout would involve ReportLab Frames or a single large table for layout.\n",
" # For demonstration, we'll put client details first, then invoice details.\n",
" elements.append(Paragraph(\"BILL TO:\", styles['h3']))\n",
" elements.append(Paragraph(invoice.client_name or \"N/A\", styles['Normal']))\n",
" elements.append(Paragraph(invoice.client_address or \"N/A\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" elements.append(Paragraph(\"Invoice Details:\", styles['h3']))\n",
" for row in invoice_details_data:\n",
" elements.append(Paragraph(f\"{row[0]} {row[1]}\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # --- Items Table ---\n",
" data = [['Description', 'Qty', 'Unit Price', 'Total']]\n",
" for item in invoice.items:\n",
" data.append([\n",
" item.description,\n",
" f\"{item.quantity:.2f}\",\n",
" f\"${item.unit_price:.2f}\",\n",
" f\"${item.total:.2f}\"\n",
" ])\n",
"\n",
" table_style = TableStyle([\n",
" ('BACKGROUND', (0, 0), (-1, 0), colors.grey),\n",
" ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),\n",
" ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\n",
" ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),\n",
" ('BOTTOMPADDING', (0, 0), (-1, 0), 12),\n",
" ('BACKGROUND', (0, 1), (-1, -1), colors.beige),\n",
" ('GRID', (0, 0), (-1, -1), 1, colors.black),\n",
" ('BOX', (0, 0), (-1, -1), 1, colors.black),\n",
" ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),\n",
" ('ALIGN', (0, 1), (0, -1), 'LEFT'), # Align description to left\n",
" ])\n",
"\n",
" # Calculate column widths (percentage based or fixed)\n",
" col_widths = [4*inch, 0.75*inch, 1*inch, 1*inch]\n",
"\n",
" # Check if data has more than just the header row\n",
" if len(data) > 1:\n",
" table = Table(data, colWidths=col_widths)\n",
" table.setStyle(table_style)\n",
" elements.append(table)\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
" else:\n",
" elements.append(Paragraph(\"No items found for this invoice.\", styles['Normal']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # --- Totals ---\n",
" totals_data = [\n",
" [\"Subtotal:\", f\"${invoice.subtotal:.2f}\"],\n",
" [f\"Tax ({invoice.tax_rate*100:.0f}%):\", f\"${invoice.tax_amount:.2f}\"],\n",
" [\"GRAND TOTAL:\", f\"${invoice.grand_total:.2f}\"]\n",
" ]\n",
"\n",
" totals_table = Table(totals_data, colWidths=[5.75*inch, 1*inch]) # Align to right\n",
" totals_table.setStyle(TableStyle([\n",
" ('ALIGN', (0, 0), (-1, -1), 'RIGHT'),\n",
" ('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),\n",
" ('TOPPADDING', (0, 0), (-1, -1), 6),\n",
" ('BOTTOMPADDING', (0, 0), (-1, -1), 6),\n",
" ]))\n",
" elements.append(totals_table)\n",
" elements.append(Spacer(1, 0.3 * inch))\n",
"\n",
" # --- Notes ---\n",
" if invoice.notes:\n",
" elements.append(Paragraph(\"Notes:\", styles['h3']))\n",
" elements.append(Paragraph(invoice.notes, styles['Normal']))\n",
" elements.append(Spacer(1, 0.2 * inch))\n",
"\n",
" # Build PDF\n",
" try:\n",
" doc.build(elements)\n",
" print(f\"Invoice PDF generated successfully: {file_path}\")\n",
" return file_path\n",
" except Exception as e:\n",
" print(f\"Error generating PDF: {e}\")\n",
" return f\"Error: Failed to generate PDF: {e}\"\n",
"\n",
"print(\"PDF generation function defined.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "J9hhZCHK0oiN",
"outputId": "76f22f4f-d205-4b10-aa88-4ed56ba1e426"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"PDF generation function defined.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# === Cell 8: Gradio UI Definition and Launch ===\n",
"\n",
"# Define check_model_devices_ui function (from previous updates, adapted for Qwen2-Audio)\n",
"def check_model_devices_ui():\n",
" global qwen2_audio_model, device\n",
"\n",
" status_output = []\n",
" status_output.append(\"--- Checking Model Device Placement ---\")\n",
"\n",
" if qwen2_audio_model:\n",
" try:\n",
" qwen_device = next(qwen2_audio_model.parameters()).device\n",
" status_output.append(f\"Qwen2-Audio model is on: {qwen_device}\")\n",
" if hasattr(qwen2_audio_model, 'hf_device_map'):\n",
" status_output.append(f\"Qwen2-Audio device map: {qwen2_audio_model.hf_device_map}\")\n",
" except Exception as e:\n",
" status_output.append(f\"Error checking Qwen2-Audio device: {e}\")\n",
" else:\n",
" status_output.append(\"Qwen2-Audio model not yet loaded (qwen2_audio_model is None).\")\n",
"\n",
" status_output.append(\"\\n--- Device Check Complete ---\")\n",
" status_output.append(f\"\\nOverall CUDA availability from torch: {torch.cuda.is_available()}\")\n",
" if torch.cuda.is_available():\n",
" status_output.append(f\"Current CUDA device: {torch.cuda.current_device()} ({torch.cuda.get_device_name(torch.cuda.current_device())})\")\n",
"\n",
" return \"\\n\".join(status_output)\n",
"\n",
"\n",
"with gr.Blocks(title=\"Voice-Powered Autonomous Invoice Generator\") as full_app:\n",
" gr.Markdown(\"# 🎙️ Voice-Powered Autonomous Invoice Generator\")\n",
" gr.Markdown(\"This system processes voice commands to extract invoice details and generate a professional PDF invoice.\")\n",
"\n",
" # Gradio State variables to pass data between components reliably\n",
" current_audio_path_state = gr.State(None)\n",
" extracted_invoice_data_state = gr.State({}) # Stores the Pydantic-validated dict\n",
"\n",
" with gr.Tab(\"Invoice Generation Workflow\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 1: Load AI Model\")\n",
" load_model_button = gr.Button(\"Click to Load Qwen2-Audio Model\", variant=\"primary\")\n",
" model_load_status = gr.Textbox(label=\"Model Loading Status\", interactive=False, lines=3, value=\"Model not loaded yet. Click 'Load Model' to begin.\")\n",
" load_model_button.click(load_qwen2_audio_model, inputs=[], outputs=model_load_status)\n",
"\n",
" with gr.Accordion(\"Model Device Diagnostics\", open=False):\n",
" check_device_button = gr.Button(\"Check Model Device Placement\", variant=\"secondary\")\n",
" device_status_output = gr.Textbox(label=\"Device Status\", interactive=False, lines=10)\n",
" check_device_button.click(check_model_devices_ui, inputs=[], outputs=device_status_output)\n",
"\n",
" gr.Markdown(\"### Step 2: Audio Input (Upload or Record Invoice Request)\")\n",
" with gr.Tab(\"Upload Audio\"):\n",
" upload_audio_input = gr.Audio(type=\"filepath\", label=\"Upload an audio file (e.g., MP3, WAV, OGG)\", sources=[\"upload\"])\n",
" upload_audio_status = gr.Textbox(label=\"Upload Status\", interactive=False)\n",
" # Use a lambda to save path to state for consistent handling\n",
" upload_audio_input.change(lambda x: (x, f\"Audio saved to temporary path: {x.name if hasattr(x, 'name') else x}\"),\n",
" inputs=upload_audio_input, outputs=[current_audio_path_state, upload_audio_status])\n",
"\n",
" with gr.Tab(\"Record Audio\"):\n",
" record_audio_input = gr.Audio(type=\"filepath\", label=\"Record invoice request using your microphone\", sources=[\"microphone\"])\n",
" record_audio_status = gr.Textbox(label=\"Record Status\", interactive=False)\n",
" record_audio_input.change(lambda x: (x, f\"Audio saved to temporary path: {x.name if hasattr(x, 'name') else x}\"),\n",
" inputs=record_audio_input, outputs=[current_audio_path_state, record_audio_status])\n",
"\n",
" user_text_prompt = gr.Textbox(\n",
" label=\"Optional Text Prompt for Context\",\n",
" value=\"Please generate a professional invoice from this voice input.\",\n",
" info=\"Add additional text instructions or context for the AI.\"\n",
" )\n",
"\n",
" gr.Markdown(\"### Step 3: Extract Invoice Details\")\n",
" extract_button = gr.Button(\"Extract Invoice Details from Voice\", variant=\"primary\")\n",
" extraction_status = gr.Textbox(label=\"Extraction & Autofill Status\", interactive=False, lines=5)\n",
" extracted_data_json = gr.Json(label=\"Structured Invoice Data (JSON)\", value={}, open=False) # Display structured data\n",
"\n",
" extract_button.click(\n",
" extract_and_structure_invoice_data,\n",
" inputs=[current_audio_path_state, user_text_prompt],\n",
" outputs=[extracted_invoice_data_state, extraction_status]\n",
" ).then(\n",
" lambda data: data, # Pass the dict from state to the Json component\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[extracted_data_json]\n",
" )\n",
"\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 4: Generate Invoice PDF\")\n",
" generate_pdf_button = gr.Button(\"Generate Invoice PDF\", variant=\"secondary\")\n",
" pdf_output_file = gr.File(label=\"Download Generated Invoice (PDF)\")\n",
" pdf_generation_status = gr.Textbox(label=\"PDF Generation Status\", interactive=False)\n",
"\n",
" generate_pdf_button.click(\n",
" generate_invoice_pdf,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[pdf_output_file] # The PDF path will be the output for the File component\n",
" ).then(\n",
" lambda x: \"PDF generated successfully!\" if x and os.path.exists(x) else \"PDF generation failed. Check console for errors.\",\n",
" inputs=[pdf_output_file],\n",
" outputs=[pdf_generation_status]\n",
" )\n",
" gr.Markdown(f\"*(Generated PDFs will be saved in: `{LOCAL_PDF_FOLDER}` on Colab runtime.)*\")\n",
"\n",
" with gr.Accordion(\"Usage Notes & Tips\", open=False):\n",
" gr.Markdown(\"\"\"\n",
" * **Workflow:** Follow the steps linearly: **1) Load Model** -> **2) Provide Audio** -> **3) Extract Details** -> **4) Generate PDF**.\n",
" * **Voice Input:** Speak clearly, mentioning client names, item descriptions, quantities, and prices. Example: \"Generate an invoice for John Doe, for two laptops at fifteen hundred each, and one wireless mouse for twenty-five dollars.\"\n",
" * **Optional Prompt:** Use the text prompt to provide additional context or clarify ambiguities if the voice input alone isn't sufficient.\n",
" * **Autofill:** The system will attempt to autofill client addresses and known item prices from its internal simulated databases (`user_db`, `item_db`).\n",
" * **Missing Info:** If critical details are missing after extraction and autofill, the \"Extraction Status\" will alert you. You would then need to provide more specific voice input or text prompt.\n",
" * **PDF Output:** The generated PDF will include standard invoice fields, a table for items, and calculated totals.\n",
" \"\"\")\n",
"\n",
"full_app.launch(debug=True, share=True)\n",
"print(\"Gradio app launched!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"id": "iok86ai6z-Kq",
"outputId": "62c890bd-07b7-44fb-f7bc-cee6057b328f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"* Running on public URL: https://10672b38aed5f1a2a1.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
""
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading Qwen2-Audio model: Qwen/Qwen2-Audio-7B-Instruct on cpu...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"preprocessor_config.json: 0%| | 0.00/342 [00:00 0:\n",
" status_output.append(f\"Current CUDA device: {torch.cuda.current_device()} ({torch.cuda.get_device_name(torch.cuda.current_device())})\")\n",
" status_output.append(f\"Total GPU Memory on current device: {torch.cuda.get_device_properties(torch.cuda.current_device()).total_memory / (1024**3):.2f} GB\")\n",
" else:\n",
" status_output.append(\"No CUDA devices detected. Running on CPU.\")\n",
"\n",
" return \"\\n\".join(status_output)\n",
"\n",
"\n",
"with gr.Blocks(title=\"Voice-Powered Autonomous Invoice Generator\") as full_app:\n",
" gr.Markdown(\"# 🎙️ Voice-Powered Autonomous Invoice Generator\")\n",
" gr.Markdown(\"This system processes voice commands to extract invoice details and generate a professional PDF invoice.\")\n",
"\n",
" current_audio_path_state = gr.State(None)\n",
" extracted_invoice_data_state = gr.State({}) # Stores the Pydantic-validated dict\n",
"\n",
" with gr.Tab(\"Invoice Generation Workflow\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 1: Load AI Model\")\n",
" load_model_button = gr.Button(\"Click to Load Qwen2-Audio Model\", variant=\"primary\")\n",
" model_load_status = gr.Textbox(label=\"Model Loading Status\", interactive=False, lines=3, value=\"Model not loaded yet. Click 'Load Model' to begin.\")\n",
" load_model_button.click(load_qwen2_audio_model, inputs=[], outputs=model_load_status)\n",
"\n",
" with gr.Accordion(\"Model Device Diagnostics\", open=False):\n",
" check_device_button = gr.Button(\"Check Model Device Placement\", variant=\"secondary\")\n",
" device_status_output = gr.Textbox(label=\"Device Status\", interactive=False, lines=10)\n",
" check_device_button.click(check_model_devices_ui, inputs=[], outputs=device_status_output)\n",
"\n",
" gr.Markdown(\"### Step 2: Audio Input (Upload or Record Invoice Request)\")\n",
" with gr.Tab(\"Upload Audio\"):\n",
" upload_audio_input = gr.Audio(type=\"filepath\", label=\"Upload an audio file (e.g., MP3, WAV, OGG)\", sources=[\"upload\"])\n",
" confirm_upload_button = gr.Button(\"✅ Confirm Uploaded Audio\", variant=\"secondary\")\n",
" upload_audio_status = gr.Textbox(label=\"Upload Status\", interactive=False)\n",
"\n",
" confirm_upload_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),\n",
" inputs=[upload_audio_input],\n",
" outputs=[current_audio_path_state, upload_audio_status]\n",
" )\n",
"\n",
" with gr.Tab(\"Record Audio\"):\n",
" record_audio_input = gr.Audio(type=\"filepath\", label=\"Record invoice request using your microphone\", sources=[\"microphone\"])\n",
" confirm_record_button = gr.Button(\"✅ Confirm Recorded Audio\", variant=\"secondary\")\n",
" record_audio_status = gr.Textbox(label=\"Record Status\", interactive=False)\n",
"\n",
" confirm_record_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),\n",
" inputs=[record_audio_input],\n",
" outputs=[current_audio_path_state, record_audio_status]\n",
" )\n",
"\n",
" user_text_prompt = gr.Textbox(\n",
" label=\"Optional Text Prompt for Context\",\n",
" value=\"Please generate a professional invoice from this voice input.\",\n",
" info=\"Add additional text instructions or context for the AI.\"\n",
" )\n",
"\n",
" gr.Markdown(\"### Step 3: Extract Invoice Details\")\n",
" extract_button = gr.Button(\"Extract Invoice Details from Voice\", variant=\"primary\")\n",
" extraction_status = gr.Textbox(label=\"Extraction & Autofill Status\", interactive=False, lines=5)\n",
" extracted_data_json = gr.Json(label=\"Structured Invoice Data (JSON)\", value={}, open=False)\n",
"\n",
" extract_button.click(\n",
" extract_and_structure_invoice_data,\n",
" inputs=[current_audio_path_state, user_text_prompt],\n",
" outputs=[extracted_invoice_data_state, extraction_status]\n",
" ).then(\n",
" lambda data: data,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[extracted_data_json]\n",
" )\n",
"\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 4: Generate Invoice PDF\")\n",
" generate_pdf_button = gr.Button(\"Generate Invoice PDF\", variant=\"secondary\")\n",
" pdf_output_file = gr.File(label=\"Download Generated Invoice (PDF)\")\n",
" pdf_generation_status = gr.Textbox(label=\"PDF Generation Status\", interactive=False)\n",
"\n",
" generate_pdf_button.click(\n",
" generate_invoice_pdf,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[pdf_output_file]\n",
" ).then(\n",
" lambda x: \"PDF generated successfully! Download from above.\" if x and os.path.exists(x) else \"PDF generation failed. Check console for errors or if data was extracted.\",\n",
" inputs=[pdf_output_file],\n",
" outputs=[pdf_generation_status]\n",
" )\n",
" gr.Markdown(f\"*(Generated PDFs will be saved in: `{LOCAL_PDF_FOLDER}` on Colab runtime. You can download them directly from the 'Download Generated Invoice' link.)*\")\n",
"\n",
" with gr.Accordion(\"Usage Notes & Tips\", open=False):\n",
" gr.Markdown(\"\"\"\n",
" * **Workflow:** Follow the steps linearly: **1) Load Model** -> **2) Provide Audio** -> **3) Extract Details** -> **4) Generate PDF**.\n",
" * **GPU Runtime:** Ensure your Google Colab runtime type is set to GPU (Runtime -> Change runtime type -> T4 GPU or A100 GPU). The model requires significant VRAM.\n",
" * **Voice Input:** Speak clearly, mentioning client names, item descriptions, quantities, and prices. Example: \"Generate an invoice for John Doe for two laptops at fifteen hundred each, and one wireless mouse for twenty-five dollars.\"\n",
" * **Optional Prompt:** Use the text prompt to provide additional context or clarify ambiguities if the voice input alone isn't sufficient.\n",
" * **Autofill:** The system will attempt to autofill client addresses and known item prices from its internal simulated databases (`user_db`, `item_db`). You can modify these databases in Cell 4.\n",
" * **Missing Info:** If critical details are missing after extraction and autofill, the \"Extraction Status\" will alert you. You would then need to provide more specific voice input or a text prompt.\n",
" * **PDF Output:** The generated PDF will include standard invoice fields, a table for items, and calculated totals.\n",
" \"\"\")\n",
"\n",
"full_app.launch(debug=True, share=True)\n",
"print(\"Gradio app launched!\")"
],
"metadata": {
"id": "DDDv6ybDz9Gi",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 611
},
"outputId": "0f2da7ae-34a1-4465-de38-3bcc0c9d1db6"
},
"execution_count": null,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"* Running on public URL: https://903610262ce91e8be9.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"cell_type": "markdown",
"source": [
"# Experiment"
],
"metadata": {
"id": "vkhXB6f9dU0m"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 8: Gradio UI Definition and Launch ===\n",
"\n",
"# Define check_model_devices_ui function (from previous updates, adapted for Qwen2-Audio)\n",
"def check_model_devices_ui():\n",
" global qwen2_audio_model, device\n",
"\n",
" status_output = []\n",
" status_output.append(\"--- Checking Model Device Placement ---\")\n",
"\n",
" if qwen2_audio_model:\n",
" try:\n",
" # Check if model has a device map (for multi-GPU or distributed loads)\n",
" if hasattr(qwen2_audio_model, 'hf_device_map') and qwen2_audio_model.hf_device_map:\n",
" status_output.append(f\"Qwen2-Audio model device map: {qwen2_audio_model.hf_device_map}\")\n",
" else:\n",
" # Fallback to checking device of first parameter for single-device models\n",
" qwen_device = next(qwen2_audio_model.parameters()).device\n",
" status_output.append(f\"Qwen2-Audio model (first parameter) is on: {qwen_device}\")\n",
"\n",
" status_output.append(f\"Qwen2-Audio model data type: {next(qwen2_audio_model.parameters()).dtype}\")\n",
"\n",
" if device == \"cuda\":\n",
" status_output.append(f\"Current GPU Memory Allocated: {torch.cuda.memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Current GPU Memory Cached: {torch.cuda.memory_reserved() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Allocated: {torch.cuda.max_memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Reserved: {torch.cuda.max_memory_reserved() / (1024**3):.2f} GB\")\n",
"\n",
" except Exception as e:\n",
" status_output.append(f\"Error checking Qwen2-Audio device: {e}\")\n",
" else:\n",
" status_output.append(\"Qwen2-Audio model not yet loaded (qwen2_audio_model is None).\")\n",
"\n",
" status_output.append(\"\\n--- Global Device Information ---\")\n",
" status_output.append(f\"Overall CUDA availability from torch: {torch.cuda.is_available()}\")\n",
" if torch.cuda.is_available():\n",
" status_output.append(f\"Number of CUDA devices: {torch.cuda.device_count()}\")\n",
" if torch.cuda.device_count() > 0:\n",
" status_output.append(f\"Current CUDA device: {torch.cuda.current_device()} ({torch.cuda.get_device_name(torch.cuda.current_device())})\")\n",
" status_output.append(f\"Total GPU Memory on current device: {torch.cuda.get_device_properties(torch.cuda.current_device()).total_memory / (1024**3):.2f} GB\")\n",
" else:\n",
" status_output.append(\"No CUDA devices detected. Running on CPU.\")\n",
"\n",
" return \"\\n\".join(status_output)\n",
"\n",
"\n",
" with gr.Blocks(title=\"Voice-Powered Autonomous Invoice Generator\") as full_app:\n",
" gr.Markdown(\"# 🎙️ Voice-Powered Autonomous Invoice Generator\")\n",
" gr.Markdown(\"This system processes voice commands to extract invoice details and generate a professional PDF invoice.\")\n",
"\n",
" current_audio_path_state = gr.State(None)\n",
" extracted_invoice_data_state = gr.State({}) # Stores the Pydantic-validated dict\n",
"\n",
" with gr.Tab(\"Invoice Generation Workflow\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 1: Load AI Model\")\n",
" load_model_button = gr.Button(\"Click to Load Qwen2-Audio Model\", variant=\"primary\")\n",
" model_load_status = gr.Textbox(label=\"Model Loading Status\", interactive=False, lines=3, value=\"Model not loaded yet. Click 'Load Model' to begin.\")\n",
" load_model_button.click(load_qwen2_audio_model, inputs=[], outputs=model_load_status)\n",
"\n",
" with gr.Accordion(\"Model Device Diagnostics\", open=False):\n",
" check_device_button = gr.Button(\"Check Model Device Placement\", variant=\"secondary\")\n",
" device_status_output = gr.Textbox(label=\"Device Status\", interactive=False, lines=10)\n",
" check_device_button.click(check_model_devices_ui, inputs=[], outputs=device_status_output)\n",
"\n",
" gr.Markdown(\"### Step 2: Audio Input (Upload or Record Invoice Request)\")\n",
" with gr.Tab(\"Upload Audio\"):\n",
" upload_audio_input = gr.Audio(type=\"filepath\", label=\"Upload an audio file (e.g., MP3, WAV, OGG)\", sources=[\"upload\"])\n",
" confirm_upload_button = gr.Button(\"✅ Confirm Uploaded Audio\", variant=\"secondary\")\n",
" upload_audio_status = gr.Textbox(label=\"Upload Status\", interactive=False)\n",
"\n",
" confirm_upload_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),\n",
" inputs=[upload_audio_input],\n",
" outputs=[current_audio_path_state, upload_audio_status]\n",
" )\n",
"\n",
" with gr.Tab(\"Record Audio\"):\n",
" record_audio_input = gr.Audio(type=\"filepath\", label=\"Record invoice request using your microphone\", sources=[\"microphone\"])\n",
" confirm_record_button = gr.Button(\"✅ Confirm Recorded Audio\", variant=\"secondary\")\n",
" record_audio_status = gr.Textbox(label=\"Record Status\", interactive=False)\n",
"\n",
" confirm_record_button.click(\n",
" lambda x: (x, f\"✅ Audio confirmed: {os.path.basename(x) if x else 'N/A'}\"),8te, record_audio_status]\n",
" )\n",
"\n",
" user_text_prompt = gr.Textbox(\n",
" label=\"Optional Text Prompt for Context\",\n",
" value=\"Please generate a professional invoice from this voice input.\",\n",
" info=\"Add additional text instructions or context for the AI.\"\n",
" )\n",
"\n",
" gr.Markdown(\"### Step 3: Extract Invoice Details\")\n",
" extract_button = gr.Button(\"Extract Invoice Details from Voice\", variant=\"primary\")\n",
" extraction_status = gr.Textbox(label=\"Extraction & Autofill Status\", interactive=False, lines=5)\n",
" extracted_data_json = gr.Json(label=\"Structured Invoice Data (JSON)\", value={}, open=False)\n",
"\n",
" extract_button.click(\n",
" extract_and_structure_invoice_data,\n",
" inputs=[current_audio_path_state, user_text_prompt],\n",
" outputs=[extracted_invoice_data_state, extraction_status]\n",
" ).then(\n",
" lambda data: data,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[extracted_data_json]\n",
" )\n",
"\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 4: Generate Invoice PDF\")\n",
" generate_pdf_button = gr.Button(\"Generate Invoice PDF\", variant=\"secondary\")\n",
" pdf_output_file = gr.File(label=\"Download Generated Invoice (PDF)\")\n",
" pdf_generation_status = gr.Textbox(label=\"PDF Generation Status\", interactive=False)\n",
"\n",
" generate_pdf_button.click(\n",
" generate_invoice_pdf,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[pdf_output_file]\n",
" ).then(\n",
" lambda x: \"PDF generated successfully! Download from above.\" if x and os.path.exists(x) else \"PDF generation failed. Check console for errors or if data was extracted.\",\n",
" inputs=[pdf_output_file],\n",
" outputs=[pdf_generation_status]\n",
" )\n",
" gr.Markdown(f\"*(Generated PDFs will be saved in: `{LOCAL_PDF_FOLDER}` on Colab runtime. You can download them directly from the 'Download Generated Invoice' link.)*\")\n",
"\n",
" with gr.Accordion(\"Usage Notes & Tips\", open=False):\n",
" gr.Markdown(\"\"\"\n",
" * **Workflow:** Follow the steps linearly: **1) Load Model** -> **2) Provide Audio** -> **3) Extract Details** -> **4) Generate PDF**.\n",
" * **GPU Runtime:** Ensure your Google Colab runtime type is set to GPU (Runtime -> Change runtime type -> T4 GPU or A100 GPU). The model requires significant VRAM.\n",
" * **Voice Input:** Speak clearly, mentioning client names, item descriptions, quantities, and prices. Example: \"Generate an invoice for John Doe for two laptops at fifteen hundred each, and one wireless mouse for twenty-five dollars.\"\n",
" * **Optional Prompt:** Use the text prompt to provide additional context or clarify ambiguities if the voice input alone isn't sufficient.\n",
" * **Autofill:** The system will attempt to autofill client addresses and known item prices from its internal simulated databases (`user_db`, `item_db`). You can modify these databases in Cell 4.\n",
" * **Missing Info:** If critical details are missing after extraction and autofill, the \"Extraction Status\" will alert you. You would then need to provide more specific voice input or a text prompt.\n",
" * **PDF Output:** The generated PDF will include standard invoice fields, a table for items, and calculated totals.\n",
" \"\"\")\n",
"\n",
" full_app.launch(debug=True, share=True)\n",
" print(\"Gradio app launched!\")"
],
"metadata": {
"id": "_WrJg3eUFfe3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Experimnet Draft1 : for cell 8 UI"
],
"metadata": {
"id": "54rscsLQu56R"
}
},
{
"cell_type": "code",
"source": [
"# === Cell 8: Gradio UI Definition and Launch ===\n",
"\n",
"# Define check_model_devices_ui function (from previous updates, adapted for Qwen2-Audio)\n",
"def check_model_devices_ui():\n",
" global qwen2_audio_model, device\n",
"\n",
" status_output = []\n",
" status_output.append(\"--- Checking Model Device Placement ---\")\n",
"\n",
" if qwen2_audio_model:\n",
" try:\n",
" # Check if model has a device map (for multi-GPU or distributed loads)\n",
" if hasattr(qwen2_audio_model, 'hf_device_map') and qwen2_audio_model.hf_device_map:\n",
" status_output.append(f\"Qwen2-Audio model device map: {qwen2_audio_model.hf_device_map}\")\n",
" else:\n",
" # Fallback to checking device of first parameter for single-device models\n",
" qwen_device = next(qwen2_audio_model.parameters()).device\n",
" status_output.append(f\"Qwen2-Audio model (first parameter) is on: {qwen_device}\")\n",
"\n",
" status_output.append(f\"Qwen2-Audio model data type: {next(qwen2_audio_model.parameters()).dtype}\")\n",
"\n",
" if device == \"cuda\":\n",
" status_output.append(f\"Current GPU Memory Allocated: {torch.cuda.memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Current GPU Memory Cached: {torch.cuda.memory_cached() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Allocated: {torch.cuda.max_memory_allocated() / (1024**3):.2f} GB\")\n",
" status_output.append(f\"Max GPU Memory Cached: {torch.cuda.max_memory_cached() / (1024**3):.2f} GB\")\n",
"\n",
" except Exception as e:\n",
" status_output.append(f\"Error checking Qwen2-Audio device: {e}\")\n",
" else:\n",
" status_output.append(\"Qwen2-Audio model not yet loaded (qwen2_audio_model is None).\")\n",
"\n",
" status_output.append(\"\\n--- Global Device Information ---\")\n",
" status_output.append(f\"Overall CUDA availability from torch: {torch.cuda.is_available()}\")\n",
" if torch.cuda.is_available():\n",
" status_output.append(f\"Number of CUDA devices: {torch.cuda.device_count()}\")\n",
" if torch.cuda.device_count() > 0:\n",
" status_output.append(f\"Current CUDA device: {torch.cuda.current_device()} ({torch.cuda.get_device_name(torch.cuda.current_device())})\")\n",
" status_output.append(f\"Total GPU Memory on current device: {torch.cuda.get_device_properties(torch.cuda.current_device()).total_memory / (1024**3):.2f} GB\")\n",
" else:\n",
" status_output.append(\"No CUDA devices detected. Running on CPU.\")\n",
"\n",
"\n",
" return \"\\n\".join(status_output)\n",
"\n",
"\n",
"with gr.Blocks(title=\"Voice-Powered Autonomous Invoice Generator\") as full_app:\n",
" gr.Markdown(\"# 🎙️ Voice-Powered Autonomous Invoice Generator\")\n",
" gr.Markdown(\"This system processes voice commands to extract invoice details and generate a professional PDF invoice.\")\n",
"\n",
" # Gradio State variables to pass data between components reliably\n",
" current_audio_path_state = gr.State(None)\n",
" extracted_invoice_data_state = gr.State({}) # Stores the Pydantic-validated dict\n",
"\n",
" with gr.Tab(\"Invoice Generation Workflow\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 1: Load AI Model\")\n",
" load_model_button = gr.Button(\"Click to Load Qwen2-Audio Model\", variant=\"primary\")\n",
" model_load_status = gr.Textbox(label=\"Model Loading Status\", interactive=False, lines=3, value=\"Model not loaded yet. Click 'Load Model' to begin.\")\n",
" load_model_button.click(load_qwen2_audio_model, inputs=[], outputs=model_load_status)\n",
"\n",
" with gr.Accordion(\"Model Device Diagnostics\", open=False):\n",
" check_device_button = gr.Button(\"Check Model Device Placement\", variant=\"secondary\")\n",
" device_status_output = gr.Textbox(label=\"Device Status\", interactive=False, lines=10)\n",
" check_device_button.click(check_model_devices_ui, inputs=[], outputs=device_status_output)\n",
"\n",
" gr.Markdown(\"### Step 2: Audio Input (Upload or Record Invoice Request)\")\n",
" with gr.Tab(\"Upload Audio\"):\n",
" upload_audio_input = gr.Audio(type=\"filepath\", label=\"Upload an audio file (e.g., MP3, WAV, OGG)\", sources=[\"upload\"])\n",
" upload_audio_status = gr.Textbox(label=\"Upload Status\", interactive=False)\n",
" # Use a lambda to save path to state for consistent handling\n",
" upload_audio_input.change(lambda x: (x.name if hasattr(x, 'name') else None, f\"Audio loaded from: {os.path.basename(x.name) if hasattr(x, 'name') else 'N/A'}\"),\n",
" inputs=upload_audio_input, outputs=[current_audio_path_state, upload_audio_status])\n",
"\n",
" with gr.Tab(\"Record Audio\"):\n",
" record_audio_input = gr.Audio(type=\"filepath\", label=\"Record invoice request using your microphone\", sources=[\"microphone\"])\n",
" record_audio_status = gr.Textbox(label=\"Record Status\", interactive=False)\n",
" record_audio_input.change(lambda x: (x.name if hasattr(x, 'name') else None, f\"Audio recorded to: {os.path.basename(x.name) if hasattr(x, 'name') else 'N/A'}\"),\n",
" inputs=record_audio_input, outputs=[current_audio_path_state, record_audio_status])\n",
"\n",
" user_text_prompt = gr.Textbox(\n",
" label=\"Optional Text Prompt for Context\",\n",
" value=\"Please generate a professional invoice from this voice input.\",\n",
" info=\"Add additional text instructions or context for the AI.\"\n",
" )\n",
"\n",
" gr.Markdown(\"### Step 3: Extract Invoice Details\")\n",
" extract_button = gr.Button(\"Extract Invoice Details from Voice\", variant=\"primary\")\n",
" extraction_status = gr.Textbox(label=\"Extraction & Autofill Status\", interactive=False, lines=5)\n",
" extracted_data_json = gr.Json(label=\"Structured Invoice Data (JSON)\", value={}, open=False) # Display structured data\n",
"\n",
" extract_button.click(\n",
" extract_and_structure_invoice_data,\n",
" inputs=[current_audio_path_state, user_text_prompt],\n",
" outputs=[extracted_invoice_data_state, extraction_status]\n",
" ).then(\n",
" # This .then() ensures the JSON display updates *after* the extraction_status is set\n",
" lambda data: data, # Pass the dict from state to the Json component\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[extracted_data_json]\n",
" )\n",
"\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Step 4: Generate Invoice PDF\")\n",
" generate_pdf_button = gr.Button(\"Generate Invoice PDF\", variant=\"secondary\")\n",
" pdf_output_file = gr.File(label=\"Download Generated Invoice (PDF)\")\n",
" pdf_generation_status = gr.Textbox(label=\"PDF Generation Status\", interactive=False)\n",
"\n",
" generate_pdf_button.click(\n",
" generate_invoice_pdf,\n",
" inputs=[extracted_invoice_data_state],\n",
" outputs=[pdf_output_file] # The PDF path will be the output for the File component\n",
" ).then(\n",
" # This .then() updates the status message after the PDF is generated\n",
" lambda x: \"PDF generated successfully! Download from above.\" if x and os.path.exists(x) else \"PDF generation failed. Check console for errors or if data was extracted.\",\n",
" inputs=[pdf_output_file],\n",
" outputs=[pdf_generation_status]\n",
" )\n",
" gr.Markdown(f\"*(Generated PDFs will be saved in: `{LOCAL_PDF_FOLDER}` on Colab runtime. You can download them directly from the 'Download Generated Invoice' link.)*\")\n",
"\n",
" with gr.Accordion(\"Usage Notes & Tips\", open=False):\n",
" gr.Markdown(\"\"\"\n",
" * **Workflow:** Follow the steps linearly: **1) Load Model** -> **2) Provide Audio** -> **3) Extract Details** -> **4) Generate PDF**.\n",
" * **GPU Runtime:** Ensure your Google Colab runtime type is set to GPU (Runtime -> Change runtime type -> T4 GPU or A100 GPU). The model requires significant VRAM.\n",
" * **Voice Input:** Speak clearly, mentioning client names, item descriptions, quantities, and prices. Example: \"Generate an invoice for John Doe for two laptops at fifteen hundred each, and one wireless mouse for twenty-five dollars.\"\n",
" * **Optional Prompt:** Use the text prompt to provide additional context or clarify ambiguities if the voice input alone isn't sufficient.\n",
" * **Autofill:** The system will attempt to autofill client addresses and known item prices from its internal simulated databases (`user_db`, `item_db`). You can modify these databases in Cell 4.\n",
" * **Missing Info:** If critical details are missing after extraction and autofill, the \"Extraction Status\" will alert you. You would then need to provide more specific voice input or a text prompt.\n",
" * **PDF Output:** The generated PDF will include standard invoice fields, a table for items, and calculated totals.\n",
" \"\"\")\n",
"\n",
"full_app.launch(debug=True, share=True)\n",
"print(\"Gradio app launched!\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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},
"id": "tMn2lZqCBvw4",
"outputId": "058e8b08-7ada-4d0f-8a35-8698fbc7a031"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"* Running on public URL: https://e761fa5c2566a29fca.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
""
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Attempting to load Qwen2-Audio model: Qwen/Qwen2-Audio-7B-Instruct on cuda...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"preprocessor_config.json: 0%| | 0.00/342 [00:00:24: FutureWarning: `torch.cuda.memory_cached` has been renamed to `torch.cuda.memory_reserved`\n",
" status_output.append(f\"Current GPU Memory Cached: {torch.cuda.memory_cached() / (1024**3):.2f} GB\")\n",
":26: FutureWarning: `torch.cuda.max_memory_cached` has been renamed to `torch.cuda.max_memory_reserved`\n",
" status_output.append(f\"Max GPU Memory Cached: {torch.cuda.max_memory_cached() / (1024**3):.2f} GB\")\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Qwen2-Audio model already loaded.\n",
"Keyboard interruption in main thread... closing server.\n",
"Killing tunnel 127.0.0.1:7860 <> https://e761fa5c2566a29fca.gradio.live\n",
"Gradio app launched!\n"
]
}
]
}
]
}