Update app.py

app.py

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+from flask import Flask, render_template, jsonify, request
+from src.helper import download_hugging_face_embeddings
+from langchain_pinecone import PineconeVectorStore
+from langchain_openai import OpenAI
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from dotenv import load_dotenv
+from src.prompt import *
+import os
+
+app = Flask(__name__)
+
+# Load environment variables - these will be set in Hugging Face Space secrets
+load_dotenv()  # Still useful for local development
+
+PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+
+if not PINECONE_API_KEY or not OPENAI_API_KEY:
+    raise ValueError("Missing PINECONE_API_KEY or OPENAI_API_KEY")
+
+os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+# Initialize embeddings and chain at startup
+embeddings = None
+rag_chain = None
+
+def initialize_chain():
+    global embeddings, rag_chain
+    try:
+        embeddings = download_hugging_face_embeddings()
+        index_name = "medprep"
+        
+        # Embed each chunk and upsert the embeddings into your Pinecone index.
+        docsearch = PineconeVectorStore.from_existing_index(
+            index_name=index_name,
+            embedding=embeddings
+        )
+        
+        retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k":3})
+        
+        llm = OpenAI(temperature=0.4, max_tokens=500)
+        prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_prompt),
+                ("human", "{input}"),
+            ]
+        )
+        
+        question_answer_chain = create_stuff_documents_chain(llm, prompt)
+        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+        print("RAG chain initialized successfully")
+        return True
+    except Exception as e:
+        print(f"Failed to initialize RAG chain: {e}")
+        return False
+
+# Initialize the chain when the application starts
+initialize_chain()
+
+@app.route("/")
+def index():
+    return render_template('chat.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    global rag_chain
+    
+    # Make sure chain is initialized
+    if rag_chain is None:
+        if not initialize_chain():
+            return "Error: System not initialized properly. Please check the logs."
+    
+    msg = request.form["msg"]
+    try:
+        response = rag_chain.invoke({"input": msg})
+        return str(response["answer"])
+    except Exception as e:
+        print(f"Error processing request: {e}")
+        return f"Error: {str(e)}"
+
+# Health check endpoint for monitoring
+@app.route("/health")
+def health_check():
+    return jsonify({"status": "healthy"})
+
+if __name__ == '__main__':
+    # Get the port from the environment variable - Hugging Face uses 7860
+    port = int(os.environ.get("PORT", 7860))
+    app.run(host="0.0.0.0", port=port, debug=False)