Spaces:
Runtime error
Runtime error
| # 1. User selects the LLM | |
| # 2. User adjusts the model parameters (optional) | |
| # 3. User provide a query | |
| # 4. Selected model is invoked | |
| # 5. Result is shown to the user | |
| import streamlit as st | |
| from dotenv import load_dotenv | |
| import os | |
| # from langchain_community.llms import HuggingFaceHub | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| # Load the API keys, if running locally | |
| # CHANGE the path to the env file | |
| # If HF space is used then set the env var HUGGINGFACEHUB_API_TOKEN in the settings | |
| try: | |
| load_dotenv('C:\\Users\\raj\\.jupyter\\.env') | |
| except: | |
| print("Environment file not found !! MUST find the env var HUGGINGFACEHUB_API_TOKEN to work.") | |
| # Title | |
| st.title('Try out the model') | |
| # Models select box | |
| models = [ | |
| 'mistralai/Mistral-7B-Instruct-v0.2', | |
| 'google/flan-t5-xxl', | |
| 'tiiuae/falcon-40b-instruct', | |
| 'microsoft/phi-2' | |
| ] | |
| model_id = st.sidebar.selectbox( | |
| 'Select model', | |
| options=tuple(models) | |
| ) | |
| # Read the API key from environment - switch key for different providers | |
| api_token = os.environ.get('HUGGINGFACEHUB_API_TOKEN') | |
| if 'model-response' not in st.session_state: | |
| st.session_state['model-response'] = '<provide query & click on invoke>' | |
| # draw the box for model response | |
| st.text_area('Response', value = st.session_state['model-response'], height=400) | |
| # draw the box for query | |
| query = st.text_area('Query', placeholder='provide query & invoke', value='who was the president of the USA in 2023?') | |
| # Model parameter controls | |
| # https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html | |
| # Temperature | |
| temperature = st.sidebar.slider( | |
| label='Temperature', | |
| min_value=0.01, | |
| max_value=1.0 | |
| ) | |
| # Top p | |
| top_p = st.sidebar.slider( | |
| label='Top p', | |
| min_value=0.01, | |
| max_value=1.0, | |
| value=0.01 | |
| ) | |
| # Top k | |
| top_k = st.sidebar.slider( | |
| label='Top k', | |
| min_value=1, | |
| max_value=50, | |
| value=10 | |
| ) | |
| repetition_penalty = st.sidebar.slider( | |
| label='Repeatition penalty', | |
| min_value=0.0, | |
| max_value=5.0, | |
| value=1.0 | |
| ) | |
| # Maximum token | |
| max_tokens = st.sidebar.number_input( | |
| label='Max tokens', | |
| value=50 | |
| ) | |
| # chached | |
| def get_llm(model_id): | |
| return HuggingFaceEndpoint( | |
| repo_id=model_id, | |
| temperature=temperature, | |
| top_k = top_k, | |
| top_p = top_p, | |
| repetition_penalty = repetition_penalty, | |
| max_new_tokens=max_tokens | |
| ) | |
| # invoke the LLM | |
| def invoke(): | |
| llm_hf = get_llm(model_id) | |
| # Show spinner, while we are waiting for the response | |
| with st.spinner('Invoking LLM ... '): | |
| st.session_state['model-response'] = llm_hf.invoke(query) | |
| st.button("Invoke", on_click=invoke) |