Spaces:
Running
Running
| """ Simple Chatbot | |
| @author: Nigel Gebodh | |
| @email: [email protected] | |
| """ | |
| 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() | |
| # #=========================================== | |
| # updates = ''' | |
| # Updates | |
| # + 04/20/2025 | |
| # - Changed the inference from HF b/c | |
| # API calls are not very limted. | |
| # - Added API call limiting to allow for demoing | |
| # - Added support for adding your own API token. | |
| # + 04/16/2025 | |
| # - Changed the inference points on HF b/c | |
| # older points no longer supported. | |
| # ''' | |
| # #------------------------------------------- | |
| API_CALL_LIMIT = 10 # Define the limit | |
| if 'api_call_count' not in st.session_state: | |
| st.session_state.api_call_count = 0 | |
| st.session_state.remaining_calls = API_CALL_LIMIT | |
| model_links_hf ={ | |
| "Gemma-3-27B-it":{ | |
| "inf_point":"https://router.huggingface.co/nebius/v1", | |
| "link":"google/gemma-3-27b-it-fast", | |
| }, | |
| "Meta-Llama-3.1-8B":{ | |
| "inf_point":"https://router.huggingface.co/nebius/v1", | |
| "link":"meta-llama/Meta-Llama-3.1-8B-Instruct-fast", | |
| }, | |
| "Mistral-7B":{ | |
| "inf_point":"https://router.huggingface.co/together/v1", | |
| "link":"mistralai/Mistral-7B-Instruct-v0.3", | |
| }, | |
| "Gemma-2-27B-it":{ | |
| "inf_point":"https://router.huggingface.co/nebius/v1", | |
| "link":"google/gemma-2-27b-it-fast", | |
| }, | |
| "Gemma-2-2B-it":{ | |
| "inf_point":"https://router.huggingface.co/nebius/v1", | |
| "link":"google/gemma-2-2b-it-fast", | |
| }, | |
| "Zephyr-7B-Ξ²":{ | |
| "inf_point":"https://router.huggingface.co/hf-inference/models/HuggingFaceH4/zephyr-7b-beta/v1", | |
| "link":"HuggingFaceH4/zephyr-7b-beta", | |
| }, | |
| } | |
| model_links_groq ={ | |
| "Gemma-2-9B-it":{ | |
| "inf_point":"https://api.groq.com/openai/v1", | |
| "link":"gemma2-9b-it", | |
| }, | |
| "Meta-Llama-3.1-8B":{ | |
| "inf_point":"https://api.groq.com/openai/v1", | |
| "link":"llama-3.1-8b-instant", | |
| }, | |
| } | |
| #Pull info about the model to display | |
| model_info ={ | |
| "Mistral-7B": | |
| {'description':"""The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over **7 billion parameters.** \n""", | |
| 'logo':'https://cdn-avatars.huggingface.co/v1/production/uploads/62dac1c7a8ead43d20e3e17a/wrLf5yaGC6ng4XME70w6Z.png'}, | |
| "Gemma-2-27B-it": | |
| {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **27 billion parameters.** \n""", | |
| 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
| "Gemma-3-27B-it": | |
| {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **27 billion parameters.** \n""", | |
| 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
| "Gemma-2-2B-it": | |
| {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **2 billion parameters.** \n""", | |
| 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
| "Gemma-2-9B-it": | |
| {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **9 billion parameters.** \n""", | |
| 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
| "Zephyr-7B": | |
| {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nFrom Huggingface: \n\ | |
| Zephyr is a series of language models that are trained to act as helpful assistants. \ | |
| [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\ | |
| is the third model in the series, and is a fine-tuned version of google/gemma-7b \ | |
| that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""", | |
| 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'}, | |
| "Zephyr-7B-Ξ²": | |
| {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nFrom Huggingface: \n\ | |
| Zephyr is a series of language models that are trained to act as helpful assistants. \ | |
| [Zephyr-7B-Ξ²](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\ | |
| is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \ | |
| that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""", | |
| 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'}, | |
| "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'}, | |
| "Meta-Llama-3.1-8B": | |
| {'description':"""The Llama (3.1) 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':'Llama3_1_logo.png'}, | |
| } | |
| #Random dog images for error message | |
| random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg", | |
| "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg", | |
| "526590d2-8817-4ff0-8c62-fdcba5306d02.jpg", | |
| "1326984c-39b0-492c-a773-f120d747a7e2.jpg", | |
| "42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg", | |
| "8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg", | |
| "ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg", | |
| "027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg", | |
| "08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg", | |
| "0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg", | |
| "0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg", | |
| "6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg", | |
| "bfb9e165-c643-4993-9b3a-7e73571672a6.jpg"] | |
| def reset_conversation(): | |
| ''' | |
| Resets Conversation | |
| ''' | |
| st.session_state.conversation = [] | |
| st.session_state.messages = [] | |
| return None | |
| # --- Sidebar Setup --- | |
| st.sidebar.title("Chatbot Settings") | |
| #Define model clients | |
| client_names = ["Provided API Call", "HF-Token"] | |
| client_select = st.sidebar.selectbox("Select Model Client", client_names) | |
| if "HF-Token" in client_select: | |
| try: | |
| if "API_token" not in st.session_state: | |
| st.session_state.API_token = None | |
| st.session_state.API_token = st.sidebar.text_input("Enter your Hugging Face Access Token", type="password") | |
| model_links = model_links_hf | |
| except Exception as e: | |
| st.sidebar.error(f"Credentials Error:\n\n {e}") | |
| elif "Provided API Call" in client_select: | |
| try: | |
| if "API_token" not in st.session_state: | |
| st.session_state.API_token = None | |
| st.session_state.API_token = os.environ.get('GROQ_API_TOKEN')#Should be like os.environ.get('HUGGINGFACE_API_TOKEN') | |
| model_links = model_links_groq | |
| except Exception as e: | |
| st.sidebar.error(f"Credentials Error:\n\n {e}") | |
| # 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) | |
| #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, type="primary") #Reset button | |
| st.sidebar.divider() # Add a visual separator | |
| # Create model description | |
| st.sidebar.subheader(f"About {selected_model}") | |
| 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.*") | |
| st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).") | |
| st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.") | |
| 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] | |
| # initialize the client | |
| client = OpenAI( | |
| base_url=model_links[selected_model]["inf_point"],#"https://api-inference.huggingface.co/v1", | |
| api_key=st.session_state.API_token#os.environ.get('HUGGINGFACE_API_TOKEN')#"hf_xxx" # Replace with your token | |
| ) | |
| 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"]) | |
| 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) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| if st.session_state.api_call_count >= API_CALL_LIMIT: | |
| # Add the warning to the displayed messages, but not to the history sent to the model | |
| response = f"LIMIT REACHED: Sorry, you have reached the API call limit for this session." | |
| # st.write(response) | |
| st.warning(f"Sorry, you have reached the API call limit for this session.") | |
| st.session_state.messages.append({"role": "assistant", "content": response }) | |
| else: | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| try: | |
| st.session_state.api_call_count += 1 | |
| # Add a spinner for better UX while waiting | |
| with st.spinner(f"Asking {selected_model}..."): | |
| stream = client.chat.completions.create( | |
| model=model_links[selected_model]["link"], | |
| 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) | |
| remaining_calls = (API_CALL_LIMIT) - st.session_state.api_call_count | |
| st.markdown(f"\n\n <span style='float: right; font-size: 0.8em; color: gray;'>API calls:({remaining_calls}/{API_CALL_LIMIT})</span>", unsafe_allow_html=True) | |
| except Exception as e: | |
| response = "π΅βπ« Looks like someone unplugged something!\ | |
| \n Either the model space is being updated or something is down.\ | |
| \n\ | |
| \n Try again later. \ | |
| \n\ | |
| \n Here's a random pic of a πΆ:" | |
| 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}) |