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# Demonstrates the basic usage
# 1. Select model
# 2. Provide a query
# 3. Invoke the model
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
@st.cache_resource
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) |