feat: complete pragativeer v2 frontend and backend, docker setup, and cbt engine

backend/llm.py

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+import os
+import json
+from openai import AsyncOpenAI
+from sqlmodel import Session, select
+from fastapi.responses import StreamingResponse
+from uuid import UUID
+
+import models
+
+# Point the standard AsyncOpenAI client to your local Ollama server
+client = AsyncOpenAI(
+    base_url=os.getenv("LLM_BASE_URL", "http://host.docker.internal:11434/v1"),
+    api_key=os.getenv("OPENAI_API_KEY", "ollama"), # Required by the SDK, but Ollama ignores it
+)
+
+async def stream_tutor_response(user_id: UUID, user_message: str, session: Session):
+    # 1. Fetch Context: Get the student's active weak areas
+    active_mistakes = session.exec(
+        select(models.Mistake)
+        .where(models.Mistake.user_id == user_id)
+        .where(models.Mistake.is_mastered == False)
+    ).all()
+    
+    weak_topics = list(set([m.topic for m in active_mistakes]))
+    context_string = ", ".join(weak_topics) if weak_topics else "General Syllabus"
+
+    # 2. Construct the highly-specific System Prompt
+    system_prompt = f"""
+    You are PragatiVeer's elite AI Tutor for JEE & NEET aspirants. 
+    Your goal is to guide students to the answer, not just give it to them.
+    
+    STUDENT CONTEXT:
+    This student currently struggles with the following topics: {context_string}.
+    Keep this in mind and relate explanations back to foundational concepts in these areas if applicable.
+    
+    RULES:
+    1. Respond in the student's preferred language (English or Hindi).
+    2. Use LaTeX wrapped in single $ for inline math and $$ for block equations.
+    3. Keep responses concise and focused on problem-solving steps.
+    """
+
+    # 3. Call the LLM
+    response = await client.chat.completions.create(
+        model=os.getenv("LLM_MODEL_NAME", "gemma"),
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_message}
+        ],
+        stream=True,
+    )
+
+    # 4. Yield tokens as they arrive
+    async for chunk in response:
+        if chunk.choices[0].delta.content is not None:
+            yield chunk.choices[0].delta.content