from flask import Flask, request, jsonify
from flask_cors import CORS
import sys
import os

# Add the src/python directory to the path so we can import our modules
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))

from src.python.vector_store import VectorStore
from backend.embedder_wrapper import SyncEmbedder
from src.python.config import OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)
CORS(app, origins=["http://localhost:3000", "http://localhost:5000", "http://127.0.0.1:3000", "http://127.0.0.1:5000"], supports_credentials=True, allow_headers=["Content-Type", "Authorization"])

# Initialize our components
try:
    vector_store = VectorStore()
    embedder = SyncEmbedder()
    logger.info("Service initialized successfully")
    logger.info(f"Connected to Qdrant at: {QDRANT_URL}")
except Exception as e:
    logger.error(f"Failed to initialize services: {str(e)}")
    raise

@app.route('/chat', methods=['POST'])
def chat():
    """
    Chat endpoint that takes user query and returns a response based on RAG
    Expected JSON format: {"message": "user question"}
    """
    try:
        data = request.get_json()

        if not data or 'message' not in data:
            return jsonify({'error': 'Message field is required'}), 400

        user_message = data['message']

        # Create embedding for the user message
        try:
            query_embedding = embedder.embed_text(user_message)
        except Exception as e:
            logger.error(f"Error creating embedding: {str(e)}")
            return jsonify({'error': f'Error processing your message: {str(e)}'}), 500

        # Search for similar documents in the vector store
        try:
            similar_docs = vector_store.search_similar(query_embedding, top_k=5)
        except Exception as e:
            logger.error(f"Error searching for documents: {str(e)}")
            return jsonify({'error': f'Error retrieving documents: {str(e)}'}), 500

        # Format the retrieved documents as context
        context = "\n".join([doc['content'] for doc in similar_docs])

        # Prepare the prompt for the LLM
        if context.strip() == "":
            # If no context is found, let the model respond without it
            prompt = f"""
            Please answer the following question. If you don't know the answer, please say so.

            Question: {user_message}
            Answer:
            """
        else:
            prompt = f"""
            Answer the question based on the context provided.
            If the answer is not in the context, say "I don't have enough information to answer that question."

            Context: {context}

            Question: {user_message}
            Answer:
            """

        # Use OpenAI API to generate the response
        try:
            from openai import OpenAI

            client = OpenAI(api_key=OPENAI_API_KEY)

            response = client.chat.completions.create(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": prompt}],
                temperature=0.3,
                max_tokens=500
            )

            bot_response = response.choices[0].message.content.strip()
        except Exception as e:
            logger.error(f"Error calling LLM: {str(e)}")
            return jsonify({'error': f'Error generating response: {str(e)}'}), 500

        return jsonify({
            'response': bot_response,
            'sources': [doc.get('source', '') for doc in similar_docs],
            'scores': [doc.get('score', 0.0) for doc in similar_docs],
            'retrieved_context': context
        })

    except Exception as e:
        logger.error(f"Unexpected error in chat endpoint: {str(e)}")
        return jsonify({'error': str(e)}), 500


@app.route('/health', methods=['GET'])
def health():
    """Health check endpoint"""
    return jsonify({'status': 'healthy'})


@app.route('/documents/count', methods=['GET'])
def document_count():
    """Get the count of documents in the vector store"""
    try:
        count = vector_store.get_all_documents_count()
        return jsonify({'count': count})
    except Exception as e:
        logger.error(f"Error getting document count: {str(e)}")
        return jsonify({'error': str(e)}), 500


if __name__ == '__main__':
    # Get port from environment variable, default to 5000 for local development
    port = int(os.environ.get('PORT', 3000))
    app.run(debug=True, host='0.0.0.0', port=port)