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('HUGGINGFACEHUB_API_TOKEN') # Replace with your token ) # Create supported model model_links = { "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct" } # Pull info about the model to display model_info = { "Meta-Llama-3-8B": { 'description': """The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.** \n""", 'logo': 'Llama_logo.png' } } # Random dog images for error message random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg", "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg"] 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 Model", models) # Custom description for SciMom st.sidebar.write("Built for my mom, with love. This model is pretrained with textbooks of Science NCERT.") st.sidebar.write("Model used: Meta Llama, trained using: Docker AutoTrain.") # Create a temperature slider temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5)) # Add reset button to clear conversation st.sidebar.button('Reset Chat', on_click=reset_conversation) # Create model description st.sidebar.write(f"You're now chatting with **{selected_model}**") st.sidebar.markdown(model_info[selected_model]['description']) st.sidebar.image(model_info[selected_model]['logo']) st.sidebar.markdown("*Generated content may be inaccurate or false.*") 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.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'AI - {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}, ask me a question"): # Display user message in chat message container with st.chat_message("user"): st.markdown(prompt) 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, stream=True, max_tokens=3000, ) response = st.write_stream(stream) except Exception as e: response = "😵‍💫 Looks like something went wrong! Here's a random pic of a 🐶:" st.write(response) random_dog_pick = 'https://random.dog/' + random_dog[np.random.randint(len(random_dog))] st.image(random_dog_pick) st.write("This was the error message:") st.write(e) st.session_state.messages.append({"role": "assistant", "content": response})