Update app.py

app.py

@@ -8,13 +8,13 @@ from streamlit.components.v1 import html
 import pandas as pd
 
 # Retrieve the token from environment variables
-#hf_token = os.environ.get("HF_TOKEN")
-#if not hf_token:
-    #st.error("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
-    #st.stop()
+hf_token = os.environ.get("HF_TOKEN")
+if not hf_token:
+    st.error("Hugging Face token not found. Please set the HF_TOKEN environment variable.")
+    st.stop()
 
 # Login with the token
-login(token=HF_TOKEN)
+login(token=hf_token)
 
 # Initialize session state for timer and results
 if 'result' not in st.session_state:
@@ -56,14 +56,14 @@ st.header("Sentiment Analysis & Report Generation with Gemma")
 # Introduction for the Hugging Face interface
 st.write("""
 Welcome to the Sentiment Analysis & Report Generator app!
-This tool leverages Hugging Face’s models to analyze the sentiment of your text and generate a detailed report explaining the key insights.
+This tool leverages Hugging Face’s models to analyze your text and generate a detailed report explaining key insights.
 You can either paste your review text directly into the text area or upload a CSV file containing your reviews.
 """)
 
 # Load models with caching to avoid reloading on every run
 @st.cache_resource
 def load_models():
-    # Load the sentiment analysis model via pipeline.
+    # Load the "reranker" model via pipeline.
     sentiment_pipe = pipeline("text-classification", model="mixedbread-ai/mxbai-rerank-base-v1")
     # Load the Gemma text generation pipeline.
     gemma_pipe = pipeline("text-generation", model="google/gemma-3-1b-it", use_auth_token=hf_token)
@@ -73,7 +73,7 @@ sentiment_pipe, gemma_pipe = load_models()
 
 # Provide two options for input: file upload (CSV) or text area
 uploaded_file = st.file_uploader("Upload Review File (CSV format)", type=["csv"])
-user_input = st.text_area("Or, enter your text for sentiment analysis and report generation:")
+user_input = st.text_area("Or, enter your text for analysis and report generation:")
 
 if uploaded_file is not None:
     try:
@@ -97,28 +97,28 @@ if st.button("Generate Report"):
         status_text = st.empty()
         progress_bar = st.progress(0)
         try:
-            # Stage 1: Sentiment Analysis using pipeline
-            status_text.markdown("**🔍 Running sentiment analysis...**")
+            # Stage 1: Reranking analysis using the sentiment pipeline
+            status_text.markdown("**🔍 Running reranking analysis...**")
             progress_bar.progress(0)
-            sentiment_result = sentiment_pipe(user_input)
+            rerank_result = sentiment_pipe(user_input)
             progress_bar.progress(50)
             
-            # Stage 2: Generate Report using Gemma
+            # Stage 2: Generate Report using Gemma, using the rerank result
             status_text.markdown("**📝 Generating report with Gemma...**")
             prompt = f"""
 Generate a detailed report based on the following analysis.
 Original text:
 "{user_input}"
-Sentiment analysis result:
-{sentiment_result}
-Please provide a concise summary report explaining the sentiment and key insights.
+Reranking analysis result:
+{rerank_result}
+Please provide a concise summary report explaining the insights derived from this analysis.
 """
             report = gemma_pipe(prompt, max_length=200)
             progress_bar.progress(100)
             status_text.success("**✅ Generation complete!**")
             html("<script>localStorage.setItem('freezeTimer', 'true');</script>", height=0)
             st.session_state.timer_frozen = True
-            st.write("**Sentiment Analysis Result:**", sentiment_result)
+            st.write("**Reranking Analysis Result:**", rerank_result)
             st.write("**Generated Report:**", report[0]['generated_text'])
         except Exception as e:
             html("<script>document.getElementById('timer').remove();</script>")