app.py

import os

import openai
import streamlit as st
from transformers import pipeline

from helpers.foundation_models import *

openai_client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])


st.title("🌟 Streamlit + Hugging Face Demo 🤖")


# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []


# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


with st.expander("Instructions"):
    st.sidebar.markdown(
        r"""
        # 🌟 Streamlit + Hugging Face Demo 🤖

        ## Introduction 📖

        This demo showcases how to interact with Large Language Models (LLMs) on Hugging Face using Streamlit. 

        ## Setup 🛠️

        1. Install Requirements: 

        - Streamlit: `pip install streamlit`
        - Hugging Face Transformers: `pip install transformers`

        ## Running the Demo 🚀

        1. Clone the repository: `git clone <repo-url>`
        2. Navigate to the project directory: `cd <project-directory>`
        3. Run Streamlit: `streamlit run app.py`

        ## Features 🌈

        - **Text Input** 📝: Enter your query in the text box.
        - **Model Selection** 🤖: Choose an LLM from a dropdown menu.
        - **Submit Button** ✅: Click to submit your query to the model.
        - **Responses** 💬: View the model's responses in real-time.

        ## Contributing 🤝

        Feel free to fork the repository, make changes, and submit pull requests!

        ## License 📜

        This project is licensed under the MIT License.

        ## Contact 📬

        For any queries, contact us at `email@example.com`.

        ## Happy Coding! 🎉
        """
    )


option = st.sidebar.selectbox(
    "Which task do you want to do?",
    ("Sentiment Analysis", "Medical Summarization", "ChatGPT"),
)


clear_button = st.sidebar.button("Clear Conversation", key="clear")


# Reset everything
if clear_button:
    st.session_state.messages = []


# React to user input
if prompt := st.chat_input("What is up?"):
    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    if option == "Sentiment Analysis":
        pipe_sentiment_analysis = pipeline("sentiment-analysis")
        if prompt:
            out = pipe_sentiment_analysis(prompt)
            doc = f"""
                Prompt: {prompt}
                Sentiment: {out[0]["label"]}
                Score: {out[0]["score"]}
            """
    elif option == "Medical Summarization":
        pipe_summarization = pipeline(
            "summarization", model="Falconsai/medical_summarization"
        )
        if prompt:
            out = pipe_summarization(prompt)
            doc = out[0]["summary_text"]
    elif option == "ChatGPT":
        if prompt:
            out = call_chatgpt(query=prompt)
            doc = out
    else:
        None

    response = f"{doc}"
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})