Update app.py

app.py

@@ -1,4 +1,4 @@
-from flask import Flask, request, jsonify
+import streamlit as st
 import logging
 import os
 from langchain_community.vectorstores import FAISS
@@ -8,48 +8,38 @@ from langchain.prompts import PromptTemplate
 from langchain.llms import HuggingFaceHub
 import dotenv
 import yaml
+import os
 import zipfile
-from flask_cors import CORS
+
 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()
 
-# Initialize Flask app
-app = Flask(__name__)
-CORS(app) 
-
-# Load configuration
 def load_config():
     with open("yaml-editor-online.yaml", "r") as f:
         config = yaml.safe_load(f)
     return config
 
+hf_token = os.getenv("HUGGING")
 config = load_config()
-hf_token = os.getenv("HUGGINGFACE_API_TOKEN")
-
 logging.basicConfig(level=logging.INFO)
 
-# Load embedding model
 embeddings_model = HuggingFaceEmbeddings(model_name=config["embedding_model"])
 
-# Create FAISS vector database from CSV
 def create_vector_db():
     try:
-        loader = CSVLoader(file_path="disease.csv", source_column="Disease")
+        loader = CSVLoader(file_path="disease.csv", 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(f"Error creating vector database: {e}", exc_info=True)
+        logging.error("Error creating vector database:", exc_info=e)
 
-# Load vector database and get response
 def get_qa_chain(query):
     try:
         if not os.path.exists(config["vector_db_path"]):
@@ -69,14 +59,11 @@ def get_qa_chain(query):
         prompt_template = """
         Given the following health-related context and a question, generate a structured answer:
         
-        CONTEXT: {context}
         QUESTION: {query}
         
         Ensure the response is easy to understand and medically accurate.
         """
-        prompt = PromptTemplate(input_variables=["query", "context"], template=prompt_template).format(
-            query=query, context=summarized_context
-        )
+        prompt = PromptTemplate(input_variables=["query"], template=prompt_template).format(query=query)
 
         llm = HuggingFaceHub(
             repo_id=config["model_name"],
@@ -92,24 +79,53 @@ def get_qa_chain(query):
         response = llm(prompt)
         return response.strip()
     except Exception as e:
-        logging.error(f"Error getting response: {e}", exc_info=True)
+        logging.error("Error getting response:", exc_info=e)
         return "Sorry, there was an error processing your request."
 
-# API route for queries
-@app.route("/query", methods=["GET", "POST"])
-def query_api():
-    data = request.get_json()
-    query = data.get("query", "").strip()
-
-    if not query:
-        return jsonify({"error": "Query cannot be empty"}), 400
-
-    response = get_qa_chain(query)
-    return jsonify({"response": response})
+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.")
 
-# 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()
-    app.run(host="0.0.0.0", port=7860)
+    main()