Update app.py

app.py

@@ -1,61 +1,63 @@
-import streamlit as st
-import logging
 import os
+import logging
+import threading
+import streamlit as st
+import requests
+import yaml
+import dotenv
+from flask import Flask, request, jsonify
 from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import CSVLoader
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.prompts import PromptTemplate
 from langchain.llms import HuggingFaceHub
-import dotenv
-import yaml
-import os
-import zipfile
-
-zip_file = "faiss_index.zip"
-with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-    zip_ref.extractall(".")  # Extract to the current directory
-
-print("Unzipping completed successfully.")
 
+# Load environment variables
 dotenv.load_dotenv()
 
+# Load config
 def load_config():
-    with open("yaml-editor-online.yaml", "r") as f:
-        config = yaml.safe_load(f)
-    return config
+    with open("config.yaml", "r") as f:
+        return yaml.safe_load(f)
 
-hf_token = os.getenv("HUGGING")
 config = load_config()
+
+# Initialize Flask app (Backend)
+app = Flask(__name__)
 logging.basicConfig(level=logging.INFO)
 
+# Hugging Face Token
+hf_token = os.getenv("HUGGING")
+
+# Load Embeddings Model
 embeddings_model = HuggingFaceEmbeddings(model_name=config["embedding_model"])
 
-def create_vector_db():
-    try:
-        loader = CSVLoader(file_path="disease", source_column="Disease Information")
-        data = loader.load()
-        vectordb = FAISS.from_documents(documents=data, embedding=embeddings_model)
-        vectordb.save_local(config["vector_db_path"])
-        logging.info("Vector database successfully created and saved.")
-    except Exception as e:
-        logging.error("Error creating vector database:", exc_info=e)
+# Load FAISS Vector Store
+if os.path.exists(config["vector_db_path"]):
+    vectordb = FAISS.load_local(config["vector_db_path"], embeddings_model, allow_dangerous_deserialization=True)
+else:
+    logging.error(f"Vector database not found at {config['vector_db_path']}")
+    vectordb = None
 
-def get_qa_chain(query):
+@app.route("/query", methods=["POST"])
+def get_qa_chain():
     try:
-        if not os.path.exists(config["vector_db_path"]):
-            logging.error(f"FAISS index path does not exist: {config['vector_db_path']}")
-            return "Error: No data found."
+        data = request.json
+        query = data.get("query")
+
+        if not query:
+            return jsonify({"error": "Query is required"}), 400
+
+        if not vectordb:
+            return jsonify({"error": "Vector database is missing"}), 500
 
-        vectordb = FAISS.load_local(
-            config["vector_db_path"], embeddings_model, allow_dangerous_deserialization=True
-        )
         retriever = vectordb.as_retriever(score_threshold=config["score_threshold"])
         relevant_docs = retriever.get_relevant_documents(query)[:3]
 
         if not relevant_docs:
-            return "No relevant information found."
+            return jsonify({"response": "No relevant information found."})
 
         summarized_context = " ".join(doc.page_content for doc in relevant_docs)
+
         prompt_template = """
         Given the following health-related context and a question, generate a structured answer:
         
@@ -77,55 +79,58 @@ def get_qa_chain(query):
         )
 
         response = llm(prompt)
-        return response.strip()
+        return jsonify({"response": response.strip()})
+
     except Exception as e:
-        logging.error("Error getting response:", exc_info=e)
-        return "Sorry, there was an error processing your request."
+        logging.error("Error processing query:", exc_info=e)
+        return jsonify({"error": "An error occurred processing your request"}), 500
 
-def main():
-    st.set_page_config(page_title="Health Disease Chatbot", page_icon="🩺", layout="centered")
-    
-    st.markdown(
-        """
-        <style>
-        .stApp {
-            background-color: #f0f2f6;
-            color: #333;
-            font-family: 'Arial', sans-serif;
-        }
-        .title {
-            color: #2E7D32;
-            text-align: center;
-        }
-        .query-input {
-            border-radius: 10px;
-            padding: 10px;
-        }
-        .response-box {
-            background-color: #ffffff;
-            padding: 15px;
-            border-radius: 8px;
-            box-shadow: 2px 2px 10px rgba(0,0,0,0.1);
-        }
-        </style>
-        """,
-        unsafe_allow_html=True
-    )
-    
-    st.markdown("<h1 class='title'>🩺 Health Disease Chatbot</h1>", unsafe_allow_html=True)
-    st.write("Enter a question related to health conditions, symptoms, or treatments.")
-    
-    query = st.text_input("Your health-related question:", key="query", help="Ask about diseases, symptoms, or treatments.")
-    
-    if st.button("Get Information"):
-        if query:
-            response = get_qa_chain(query)
-            st.markdown(f"<div class='response-box'><b>Response:</b><br>{response}</div>", unsafe_allow_html=True)
-        else:
-            st.warning("Please enter a query to get a response.")
+def run_flask():
+    """Run Flask server on a separate thread."""
+    app.run(host="0.0.0.0", port=7860, debug=False, use_reloader=False)
 
-if __name__ == "__main__":
-    if not os.path.exists(config["vector_db_path"]):
-        logging.info(f"Vector database not found at {config['vector_db_path']}, creating it now.")
-        create_vector_db()
-    main()
+# Start Flask server in a thread
+threading.Thread(target=run_flask, daemon=True).start()
+
+# --- STREAMLIT FRONTEND ---
+st.set_page_config(page_title="🩺 Health Disease Chatbot", page_icon="🩺", layout="centered")
+
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background-color: #f0f2f6;
+        color: #333;
+        font-family: 'Arial', sans-serif;
+    }
+    .title {
+        color: #2E7D32;
+        text-align: center;
+    }
+    .response-box {
+        background-color: #ffffff;
+        padding: 15px;
+        border-radius: 8px;
+        box-shadow: 2px 2px 10px rgba(0,0,0,0.1);
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown("<h1 class='title'>🩺 Health Disease Chatbot</h1>", unsafe_allow_html=True)
+st.write("Enter a question related to health conditions, symptoms, or treatments.")
+
+query = st.text_input("Your health-related question:", key="query")
+
+API_URL = "http://0.0.0.0:7860/query"
+
+if st.button("Get Information"):
+    if query:
+        response = requests.post(API_URL, json={"query": query})
+        if response.status_code == 200:
+            st.markdown(f"<div class='response-box'><b>Response:</b><br>{response.json().get('response')}</div>", unsafe_allow_html=True)
+        else:
+            st.error("Error fetching response. Please try again.")
+    else:
+        st.warning("Please enter a query.")