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Update app.py
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app.py
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from transformers import pipeline
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sentiment_result = sentiment_pipe(text)
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print("Sentiment Analysis Result:")
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print(sentiment_result)
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Generate a detailed report based on the following analysis.
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Original text:
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"{
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Sentiment analysis result:
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{sentiment_result}
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Please provide a concise summary report explaining the sentiment and key insights.
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"""
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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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# Hugging Face CLI Login
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# This will prompt you in the terminal for your Hugging Face token.
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# Ensure you have your token ready.
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subprocess.run(["huggingface-cli", "login"], shell=True)
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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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st.session_state.result = {}
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if 'timer_started' not in st.session_state:
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st.session_state.timer_started = False
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if 'timer_frozen' not in st.session_state:
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st.session_state.timer_frozen = False
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# Timer component using HTML and JavaScript
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def timer():
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return """
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<div id="timer" style="font-size:16px;color:#666;margin-bottom:10px;">β±οΈ Elapsed: 00:00</div>
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<script>
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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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return;
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}
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var elapsed = Date.now() - start;
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var minutes = Math.floor(elapsed / 60000);
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var seconds = Math.floor((elapsed % 60000) / 1000);
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timerElement.innerHTML = 'β±οΈ Elapsed: ' +
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(minutes < 10 ? '0' : '') + minutes + ':' +
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(seconds < 10 ? '0' : '') + seconds;
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}, 1000);
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})();
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</script>
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"""
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st.set_page_config(page_title="Sentiment & Report Generator", page_icon="π")
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st.header("Sentiment Analysis & Report Generation with Gemma")
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# Load models with caching to avoid reloading on every run
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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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gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it")
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return sentiment_pipe, gemma_pipe
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sentiment_pipe, gemma_pipe = load_models()
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# User input: a text area for the text to analyze
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user_input = st.text_area("Enter your text for sentiment analysis and report generation:")
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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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Original text:
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"{user_input}"
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Sentiment analysis result:
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{sentiment_result}
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Please provide a concise summary report explaining the sentiment and key insights.
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"""
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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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