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app.py

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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
+import time
+
+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
+)
+
+# invoke the LLM
+model_kwargs={ "temperature": "0.1" }
+def invoke():
+    llm_hf = HuggingFaceEndpoint(
+        repo_id=model_id, 
+        temperature=temperature,
+        top_k = top_k,
+        top_p = top_p,
+        repetition_penalty = repetition_penalty,
+        max_new_tokens=max_tokens
+    )
+    
+    # Show spinner, while we are waiting for the response
+    with st.spinner('Invoking LLM ... '):
+        time.sleep(5)
+    st.session_state['model-response'] = llm_hf.invoke(query)
+    print(query)
+
+st.button("Invoke", on_click=invoke)