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Update app.py
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app.py
CHANGED
@@ -1,14 +1,27 @@
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# Demonstrates the basic usage
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#
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import streamlit as st
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import os
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import time
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from langchain_community.llms import HuggingFaceHub
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from langchain_community.llms import HuggingFaceEndpoint
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# Title
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st.title('Try out the model')
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@@ -76,10 +89,10 @@ max_tokens = st.sidebar.number_input(
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value=50
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)
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#
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def
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repo_id=model_id,
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temperature=temperature,
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top_k = top_k,
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@@ -87,11 +100,14 @@ def invoke():
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repetition_penalty = repetition_penalty,
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max_new_tokens=max_tokens
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)
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# Show spinner, while we are waiting for the response
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with st.spinner('Invoking LLM ... '):
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print(query)
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st.button("Invoke", on_click=invoke)
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# Demonstrates the basic usage
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# 1. Select model
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# 2. Provide a query
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# 3. Invoke the model
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import streamlit as st
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from dotenv import load_dotenv
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import os
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# from langchain_community.llms import HuggingFaceHub
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from langchain_community.llms import HuggingFaceEndpoint
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# Load the API keys, if running locally
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# CHANGE the path to the env file
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# If HF space is used then set the env var HUGGINGFACEHUB_API_TOKEN in the settings
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try:
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load_dotenv('C:\\Users\\raj\\.jupyter\\.env')
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except:
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print("Environment file not found !! MUST find the env var HUGGINGFACEHUB_API_TOKEN to work.")
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# Title
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st.title('Try out the model')
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value=50
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)
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# chached
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@st.cache_resource
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def get_llm(model_id):
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return HuggingFaceEndpoint(
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repo_id=model_id,
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temperature=temperature,
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top_k = top_k,
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repetition_penalty = repetition_penalty,
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max_new_tokens=max_tokens
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)
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# invoke the LLM
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def invoke():
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llm_hf = get_llm(model_id)
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# Show spinner, while we are waiting for the response
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with st.spinner('Invoking LLM ... '):
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st.session_state['model-response'] = llm_hf.invoke(query)
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st.button("Invoke", on_click=invoke)
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