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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})
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