Update app.py

app.py

@@ -2,22 +2,21 @@ import os
 import nest_asyncio
 nest_asyncio.apply()
 import streamlit as st
-from sentence_transformers import CrossEncoder
 from transformers import pipeline
 from huggingface_hub import login
 from streamlit.components.v1 import html
 import pandas as pd
 
-# Retrieve the token from environment variables 
+# 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()
 
-# Login with the token 
+# Login with the token
 login(token=hf_token)
 
-# Initialize session state for timer and results 
+# Initialize session state for timer and results
 if 'result' not in st.session_state:
     st.session_state.result = {}
 if 'timer_started' not in st.session_state:
@@ -25,7 +24,7 @@ if 'timer_started' not in st.session_state:
 if 'timer_frozen' not in st.session_state:
     st.session_state.timer_frozen = False
 
-# Timer component using HTML and JavaScript 
+# Timer component using HTML and JavaScript
 def timer():
     return """
     <div id="timer" style="font-size:16px;color:#666;margin-bottom:10px;">⏱️ Elapsed: 00:00</div>
@@ -57,20 +56,20 @@ 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 the sentiment of your text and generate a detailed report explaining the 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 
+# Load models with caching to avoid reloading on every run
 @st.cache_resource
 def load_models():
-    # Load the sentiment model (CrossEncoder) for ranking sentiment labels.
-    sentiment_model = CrossEncoder("mixedbread-ai/mxbai-rerank-base-v1")
+    # Load the sentiment analysis 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)
-    return sentiment_model, gemma_pipe
+    return sentiment_pipe, gemma_pipe
 
-sentiment_model, gemma_pipe = load_models()
+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"])
@@ -98,12 +97,10 @@ if st.button("Generate Report"):
         status_text = st.empty()
         progress_bar = st.progress(0)
         try:
-            # Stage 1: Sentiment Analysis using CrossEncoder ranking
+            # Stage 1: Sentiment Analysis using pipeline
             status_text.markdown("**🔍 Running sentiment analysis...**")
             progress_bar.progress(0)
-            # Use sentiment analysis as ranking over sentiment labels.
-            labels = ["positive", "neutral", "negative"]
-            sentiment_result = sentiment_model.rank(user_input, labels, return_documents=True, top_k=1)
+            sentiment_result = sentiment_pipe(user_input)
             progress_bar.progress(50)
             
             # Stage 2: Generate Report using Gemma