#Hello! It seems like you want to import the Streamlit library in Python. Streamlit is a powerful open-source framework used for building web applications with interactive data visualizations and machine learning models. To import Streamlit, you'll need to ensure that you have it installed in your Python environment.
#Once you have Streamlit installed, you can import it into your Python script using the import statement,

import streamlit as st
from langchain import HuggingFaceHub  # Correct import for Hugging Face

# Set your Hugging Face API token
HUGGINGFACE_API_TOKEN = "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"

# Function to return the response from Hugging Face model
def load_answer(question):
    # Initialize the Hugging Face model
    llm = HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Specify the Hugging Face model
        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(question)
    return answer

# 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