Update app.py

app.py

@@ -1,6 +1,7 @@
-import streamlit as st
+from flask import Flask, request, jsonify
 import logging
 import os
+import zipfile
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import CSVLoader
 from langchain_community.embeddings import HuggingFaceEmbeddings
@@ -8,28 +9,30 @@ from langchain.prompts import PromptTemplate
 from langchain.llms import HuggingFaceHub
 import dotenv
 import yaml
-import os
-import zipfile
 
-zip_file = "faiss_index.zip"
+# Initialize Flask app
+app = Flask(__name__)
 
-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()
 
 def load_config():
     with open("yaml-editor-online.yaml", "r") as f:
-        config = yaml.safe_load(f)
-    return config
+        return yaml.safe_load(f)
 
 hf_token = os.getenv("HUGGING")
 config = load_config()
 logging.basicConfig(level=logging.INFO)
-
 embeddings_model = HuggingFaceEmbeddings(model_name=config["embedding_model"])
 
+# Unzip FAISS index
+zip_file = "faiss_index.zip"
+if os.path.exists(zip_file):
+    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
+        zip_ref.extractall(".")
+    print("Unzipping completed successfully.")
+
+# Create vector database if not found
 def create_vector_db():
     try:
         loader = CSVLoader(file_path="disease.csv", source_column="Disease Information")
@@ -40,6 +43,11 @@ def create_vector_db():
     except Exception as e:
         logging.error("Error creating vector database:", exc_info=e)
 
+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()
+
+# Function to handle query requests
 def get_qa_chain(query):
     try:
         if not os.path.exists(config["vector_db_path"]):
@@ -82,50 +90,18 @@ def get_qa_chain(query):
         logging.error("Error getting response:", exc_info=e)
         return "Sorry, there was an error processing your request."
 
-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.")
+# Define API route
+@app.route("/query", methods=["POST"])
+def query_api():
+    data = request.get_json()
+    query = data.get("query", "").strip()
+
+    if not query:
+        return jsonify({"error": "Query parameter is required."}), 400
     
-    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.")
+    response = get_qa_chain(query)
+    return jsonify({"response": response})
 
+# Run Flask app
 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()
+    app.run(host="0.0.0.0", port=5000, debug=True)