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| import streamlit as st | |
| from huggingface_hub import InferenceClient | |
| import os | |
| import sys | |
| st.title("SmallZOO-ChatBot-3B") | |
| base_url="https://api-inference.huggingface.co/models/" | |
| API_KEY = os.environ.get('HG_Interference_API_TOKEN') | |
| model_links ={ | |
| "Llama-3.2 [3B]":base_url+"meta-llama/Llama-3.2-3B-Instruct", | |
| "Qwen2.5 [3B]":base_url+"Qwen/Qwen2.5-3B-Instruct", | |
| "Phi-3.5 [3.82B]":base_url+"microsoft/Phi-3.5-mini-instruct" | |
| } | |
| model_info ={ | |
| "Llama-3.2 [3B]": | |
| {'description':"""The Llama-3.2 3B Instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \n""", | |
| 'logo':'./Meta.png'}, | |
| "Qwen2.5 [3B]": | |
| {'description':"""The Qwen2.5 3B Instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \\n""", | |
| 'logo':'./Qwen.png'}, | |
| "Phi-3.5 [3.82B]": | |
| {'description':"""The Phi-3.5 mini instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \ \n""", | |
| 'logo':'./ms.png'}, | |
| } | |
| def format_promt(message, custom_instructions=None): | |
| prompt = "" | |
| if custom_instructions: | |
| prompt += f"[INST] {custom_instructions} [/INST]" | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| def reset_conversation(): | |
| ''' | |
| Resets Conversation | |
| ''' | |
| st.session_state.conversation = [] | |
| st.session_state.messages = [] | |
| return None | |
| models =[key for key in model_links.keys()] | |
| selected_model = st.sidebar.selectbox("Select Model", models) | |
| temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5)) | |
| st.sidebar.button('Reset Chat', on_click=reset_conversation) | |
| 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 can be inaccurate, offensive or non-factual!!!*") | |
| 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() | |
| repo_id = model_links[selected_model] | |
| st.subheader(f'{selected_model}') | |
| # st.title(f'ChatBot Using {selected_model}') | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| 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}, How can I help you today?"): | |
| custom_instruction = "Act like a Human in conversation, you are helpfull assistant" | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| formated_text = format_promt(prompt, custom_instruction) | |
| with st.chat_message("assistant"): | |
| client = InferenceClient( | |
| model=model_links[selected_model],) | |
| output = client.text_generation( | |
| formated_text, | |
| temperature=temp_values,#0.5 | |
| max_new_tokens=3000, | |
| stream=True | |
| ) | |
| response = st.write_stream(output) | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |