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| import os | |
| import openai | |
| import streamlit as st | |
| from langchain.agents import AgentType, initialize_agent, load_tools | |
| from langchain.llms import OpenAI as l_OpenAI | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from helpers.foundation_models import * | |
| OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] | |
| SERPAPI_API_KEY = os.environ["SERPAPI_API_KEY"] | |
| openai_client = openai.OpenAI(api_key=OPENAI_API_KEY) | |
| # tokenizer = AutoTokenizer.from_pretrained("eagle0504/llama-2-7b-miniguanaco") | |
| # model = AutoModelForCausalLM.from_pretrained("eagle0504/llama-2-7b-miniguanaco") | |
| # def generate_response_from_llama2(query): | |
| # # Tokenize the input text | |
| # input_ids = tokenizer.encode(query, return_tensors="pt") | |
| # # Generate a response | |
| # # Adjust the parameters like max_length according to your needs | |
| # output = model.generate(input_ids, max_length=50, num_return_sequences=1, temperature=0.7) | |
| # # Decode the output to human-readable text | |
| # generated_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| # # output | |
| # return generated_text | |
| # 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. | |
| """ | |
| ) | |
| option = st.sidebar.selectbox( | |
| "Which task do you want to do?", | |
| ("Sentiment Analysis", "Medical Summarization", "ChatGPT", "ChatGPT (with Google)"), | |
| ) | |
| clear_button = st.sidebar.button("Clear Conversation", key="clear") | |
| st.sidebar.write("---") | |
| st.sidebar.markdown("Yiqiao Yin: [Site](https://www.y-yin.io/) | [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)") | |
| # 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}) | |
| with st.spinner("Wait for it..."): | |
| 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 == "Llama2": | |
| # if prompt: | |
| # out = generate_response_from_llama2(query=prompt) | |
| # doc = out | |
| elif option == "ChatGPT": | |
| if prompt: | |
| out = call_chatgpt(query=prompt) | |
| doc = out | |
| elif option == "ChatGPT (with Google)": | |
| if prompt: | |
| ans_langchain = call_langchain(prompt) | |
| prompt = f""" | |
| Based on the internet search results: {ans_langchain}; | |
| Answer the user question: {prompt} | |
| """ | |
| out = call_chatgpt(query=prompt) | |
| doc = out | |
| else: | |
| doc = "" | |
| 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}) | |