app.py

import streamlit as st
from langchain import HuggingFaceHub
from dotenv import load_dotenv


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
def load_answer(question):
    try:
        # Initialize the Hugging Face model using LangChain's HuggingFaceHub class
        llm = HuggingFaceHub(
            repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Hugging Face model repo
            huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,  # Pass your API token
            model_kwargs={"temperature": 0}  # Optional: Control response randomness
        )
        
        # Call the model with the user's question and get the response
        answer = llm.run(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")

# Function to get user input
def get_text():
    input_text = st.text_input("You: ", key="input")
    return input_text

# Get user input
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 submit and user_input:
    response = load_answer(user_input)
    st.subheader("Answer:")
    st.write(response)
elif submit:
    st.warning("Please enter a question.")  # Warning for empty input