app.py

import numpy as np
import streamlit as st
from openai import OpenAI
import os
import sys
from dotenv import load_dotenv, dotenv_values
load_dotenv()

# initialize the client
client = OpenAI(
  base_url="https://api-inference.huggingface.co/v1",
  api_key=os.environ.get('API_KEY')  # Replace with your token
)

# Create supported models
model_links = {
    "GPT-4o": "meta-llama/Meta-Llama-3-8B-Instruct",
    "GPT-4": "meta-llama/Meta-Llama-3.1-70B-Instruct",
}

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.conversation = []
    st.session_state.messages = []
    return None
    



# Define the available models
models =[key for key in model_links.keys()]

# Create the sidebar with the dropdown for model selection
selected_model = st.sidebar.selectbox("Select a GPT model", models)

#Add reset button to clear conversation
st.sidebar.button('New Chat', on_click=reset_conversation) #Reset button

# Create a temperature slider
temp_values = st.sidebar.slider('ChatGPT Temperature', 0.0, 1.0, (0.5))
st.sidebar.markdown("Temperature in ChatGPT affects the quality and coherence of the generated text.")
st.sidebar.markdown("**For optimum results, we recommend selecting a temperature between 0.5 and 0.7**")                  


# Create model description
st.sidebar.markdown("*The content created may not be accurate.*")
st.sidebar.markdown("\n Our website: [Chat-GPT-Free.com](https://chat-gpt-free.com/).")


if "prev_option" not in st.session_state:
    st.session_state.prev_option = selected_model

if st.session_state.prev_option != selected_model:
    st.session_state.messages = []
    # st.write(f"Changed to {selected_model}")
    st.session_state.prev_option = selected_model
    reset_conversation()



#Pull in the model we want to use
repo_id = model_links[selected_model]


st.subheader(f'[Chat-GPT-Free.com](https://chat-gpt-free.com/) with AI model {selected_model}')
# st.title(f'Chat-GPT-Free is now using {selected_model}')

# Set a default model
if selected_model not in st.session_state:
    st.session_state[selected_model] = model_links[selected_model] 

# 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"])



# Accept user input
if prompt := st.chat_input(f"Hi. I'm {selected_model}. How can I help you today?"):
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        try:
            stream = client.chat.completions.create(
                model=model_links[selected_model],
                messages=[
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ],
                temperature=temp_values,#0.5,
                stream=True,
                max_tokens=3000,
            )
    
            response = st.write_stream(stream)

        except Exception as e:
            # st.empty()
            response = "The GPT is overloaded!\
                    \n Repeat your request later :( "
            st.write(response)

            

    st.session_state.messages.append({"role": "assistant", "content": response})