app.py

import streamlit as st
from queue import Queue
from langchain import HuggingFaceHub, PromptTemplate, LLMChain

# Set the title of the Streamlit app
st.title("Falcon QA Bot")

# Get the Hugging Face Hub API token from Streamlit secrets
huggingfacehub_api_token = st.secrets["hf_token"]

# Set the repository ID for the Falcon model
repo_id = "tiiuae/falcon-7b-instruct"

# Initialize the Hugging Face Hub and LLMChain
llm = HuggingFaceHub(
    huggingfacehub_api_token=huggingfacehub_api_token,
    repo_id=repo_id,
    model_kwargs={"temperature": 0.2, "max_new_tokens": 2000}
)

# Define the template for the assistant's response
template = """
You are an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

{question}
"""

# Create a queue to store user questions
queue = Queue()

def chat(query):
    """
    Generates a response to the user's question using the LLMChain model.

    :param query: User's question.
    :type query: str
    :return: Response to the user's question.
    :rtype: str
    """
    # Create a prompt template with the question variable
    prompt = PromptTemplate(template=template, input_variables=["question"])

    # Create an LLMChain instance with the prompt and the Falcon model
    llm_chain = LLMChain(prompt=prompt, verbose=True, llm=llm)

    # Generate a response to the user's question
    result = llm_chain.predict(question=query)

    return result

def main():
    """
    Main function for the Streamlit app.
    """
    # Get the user's question from the input text box
    user_question = st.text_input("What do you want to ask about", placeholder="Input your question here")

    if user_question:
        # Add the user's question to the queue
        queue.put(user_question)

        # Check if there are any waiting users
        if not queue.empty():
            # Get the next user's question from the queue
            query = queue.get()

            # Generate a response to the user's question
            response = chat(query)

            # Display the response to the user
            st.write(response, unsafe_allow_html=True)

if __name__ == '__main__':
    main()