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Update app.py
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app.py
CHANGED
@@ -1,18 +1,20 @@
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import nest_asyncio
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nest_asyncio.apply()
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import streamlit as st
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import asyncio
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import subprocess
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from transformers import pipeline
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from streamlit.components.v1 import html
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#
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# Initialize session state for timer and results
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if 'result' not in st.session_state:
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@@ -30,10 +32,8 @@ def timer():
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(function() {
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var start = Date.now();
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var timerElement = document.getElementById('timer');
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// Remove any existing freeze flag
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localStorage.removeItem("freezeTimer");
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var interval = setInterval(function() {
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// If freeze flag is set, stop the timer
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if(localStorage.getItem("freezeTimer") === "true"){
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clearInterval(interval);
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timerElement.style.color = '#00cc00';
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@@ -57,7 +57,8 @@ st.header("Sentiment Analysis & Report Generation with Gemma")
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@st.cache_resource
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def load_models():
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sentiment_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1")
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return sentiment_pipe, gemma_pipe
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sentiment_pipe, gemma_pipe = load_models()
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@@ -69,27 +70,22 @@ if st.button("Generate Report"):
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if not user_input.strip():
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st.error("Please enter some text!")
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else:
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# Start the timer if not already started
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if not st.session_state.timer_started and not st.session_state.timer_frozen:
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st.session_state.timer_started = True
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html(timer(), height=50)
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# Initialize status message and progress bar
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status_text = st.empty()
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progress_bar = st.progress(0)
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try:
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# ---------------------------
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# Stage 1: Sentiment Analysis
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status_text.markdown("**π Running sentiment analysis...**")
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progress_bar.progress(0)
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sentiment_result = sentiment_pipe(user_input)
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progress_bar.progress(50)
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# ---------------------------
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# Stage 2: Generate Report using Gemma
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status_text.markdown("**π Generating report with Gemma...**")
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# Build a prompt that includes the original text and the sentiment analysis result.
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prompt = f"""
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Generate a detailed report based on the following analysis.
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@@ -104,17 +100,13 @@ Please provide a concise summary report explaining the sentiment and key insight
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report = gemma_pipe(prompt, max_length=200)
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progress_bar.progress(100)
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status_text.success("**β
Generation complete!**")
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# Freeze the timer immediately upon completion.
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html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
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st.session_state.timer_frozen = True
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# Display the results
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st.write("**Sentiment Analysis Result:**", sentiment_result)
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st.write("**Generated Report:**", report[0]['generated_text'])
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except Exception as e:
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# Remove timer element if an error occurs
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html("<script>document.getElementById('timer').remove();</script>")
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status_text.error(f"**β Error:** {str(e)}")
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progress_bar.empty()
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import os
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import nest_asyncio
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nest_asyncio.apply()
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import streamlit as st
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from transformers import pipeline
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from huggingface_hub import login
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from streamlit.components.v1 import html
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# Retrieve the token from environment variables
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hf_token = os.environ.get("HF_TOKEN")
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if not hf_token:
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st.error("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
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st.stop()
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# Login with the token
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login(token=hf_token)
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# Initialize session state for timer and results
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if 'result' not in st.session_state:
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(function() {
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var start = Date.now();
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var timerElement = document.getElementById('timer');
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localStorage.removeItem("freezeTimer");
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var interval = setInterval(function() {
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if(localStorage.getItem("freezeTimer") === "true"){
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clearInterval(interval);
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timerElement.style.color = '#00cc00';
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@st.cache_resource
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def load_models():
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sentiment_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1")
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# Pass the token to the Gemma pipeline
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gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it", use_auth_token=hf_token)
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return sentiment_pipe, gemma_pipe
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sentiment_pipe, gemma_pipe = load_models()
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if not user_input.strip():
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st.error("Please enter some text!")
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else:
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if not st.session_state.timer_started and not st.session_state.timer_frozen:
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st.session_state.timer_started = True
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html(timer(), height=50)
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status_text = st.empty()
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progress_bar = st.progress(0)
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try:
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# Stage 1: Sentiment Analysis
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status_text.markdown("**π Running sentiment analysis...**")
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progress_bar.progress(0)
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sentiment_result = sentiment_pipe(user_input)
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progress_bar.progress(50)
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# Stage 2: Generate Report using Gemma
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status_text.markdown("**π Generating report with Gemma...**")
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prompt = f"""
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Generate a detailed report based on the following analysis.
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report = gemma_pipe(prompt, max_length=200)
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progress_bar.progress(100)
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status_text.success("**β
Generation complete!**")
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html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
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st.session_state.timer_frozen = True
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st.write("**Sentiment Analysis Result:**", sentiment_result)
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st.write("**Generated Report:**", report[0]['generated_text'])
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except Exception as e:
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html("<script>document.getElementById('timer').remove();</script>")
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status_text.error(f"**β Error:** {str(e)}")
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progress_bar.empty()
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