Update app.py

app.py

@@ -1,17 +1,15 @@
+import os
 import streamlit as st
+from dotenv import load_dotenv  # Importing load_dotenv to load environment variables
 from langchain import HuggingFaceHub
-from dotenv import load_dotenv
-import os
-
 
+# Load environment variables from the .env file
 load_dotenv()
 
 # Set your Hugging Face API token from the environment variable
 HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
 
-
-
-# Function to return the response from Hugging Face model
+# Function to return the response from the Hugging Face model
 def load_answer(question):
     try:
         # Initialize the Hugging Face model using LangChain's HuggingFaceHub class
@@ -21,16 +19,16 @@ def load_answer(question):
             model_kwargs={"temperature": 0}  # Optional: Control response randomness
         )
         
-        # Call the model with the user's question and get the response
-        answer = llm.run(question)
+        # Call the model with the user's question and get the response using .predict()
+        answer = llm.predict(question)
         return answer
     except Exception as e:
         # Capture and return any exceptions or errors
         return f"Error: {str(e)}"
 
 # Streamlit App UI starts here
-st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
-st.header("LangChain Demo")
+st.set_page_config(page_title="Hugging Face Demo", page_icon=":robot:")
+st.header("Hugging Face Demo")
 
 # Function to get user input
 def get_text():
@@ -43,7 +41,7 @@ user_input = get_text()
 # Create a button for generating the response
 submit = st.button('Generate')
 
-# If generate button is clicked and user input is not empty
+# If the generate button is clicked and user input is not empty
 if submit and user_input:
     response = load_answer(user_input)
     st.subheader("Answer:")