app.py

import os
import gradio as gr
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from huggingface_hub import InferenceClient

# 1. Load the FULL pre-computed Vector Database
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = FAISS.load_local("learncpp_faiss_index_full", embeddings, allow_dangerous_deserialization=True)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# 2. Set up the Native Hugging Face Client
hf_token = os.environ.get("HF_TOKEN")
client = InferenceClient(
    model="Qwen/Qwen2.5-Coder-7B-Instruct",
    token=hf_token
)

# 3. Define the Chat Interface
def chat_function(message, history):
    try:
        # Step A: Retrieve relevant C++ tutorials from our database
        docs = retriever.invoke(message)
        context = "\n\n".join(doc.page_content for doc in docs)

        # Step B: Build the system prompt with the injected context
        system_prompt = (
            "You are an expert C++ programming assistant. You answer questions strictly based "
            "on the provided context from learncpp.com. If the answer is not in the context, "
            "say 'I cannot find the answer in the LearnCpp documentation.'\n\n"
            f"Context:\n{context}"
        )

        # Step C: Format the conversation history for the modern Chat API
        messages = [{"role": "system", "content": system_prompt}]

        if history:
            for msg in history:
                if isinstance(msg, dict):
                    role = msg.get("role", "user")
                    messages.append({"role": role, "content": msg.get("content", "")})
                else:
                    messages.append({"role": "user", "content": msg[0]})
                    messages.append({"role": "assistant", "content": msg[1]})
        
        messages.append({"role": "user", "content": message})

        # Step D: Call the conversational backend directly (Bypassing LangChain's broken wrapper!)
        response = client.chat_completion(
            messages=messages,
            max_tokens=512,
            temperature=0.1
        )
        
        return response.choices[0].message.content
    except Exception as e:
        return f"Error: {str(e)}"

demo = gr.ChatInterface(
    fn=chat_function,
    title="LearnCpp.com AI Assistant",
    description="Ask me any C++ question! I retrieve my answers directly from the complete LearnCpp tutorials.",
    examples=["What is a pointer?", "Explain dynamic memory allocation.", "Give me an example of the previous concept."]
)

if __name__ == "__main__":
    demo.launch()