Update app.py

app.py

@@ -2,7 +2,12 @@ import os
 import numpy as np
 import pandas as pd
 import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import (
+    pipeline,
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    AutoModelForSeq2SeqLM
+)
 from keybert import KeyBERT
 
 # ─── Sentiment & Keyword Models ─────────────────────────────────────────────
@@ -10,7 +15,10 @@ from keybert import KeyBERT
 def load_sentiment_pipeline():
     model_name = "mayf/amazon_reviews_bert_ft"
     tok = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
-    mdl = AutoModelForSequenceClassification.from_pretrained(model_name, use_auth_token=True)
+    mdl = AutoModelForSequenceClassification.from_pretrained(
+        model_name,
+        use_auth_token=True
+    )
     return pipeline(
         "sentiment-analysis",
         model=mdl,
@@ -60,13 +68,16 @@ def main():
     generation_pipeline = load_flant5_pipeline()
     progress.progress(20)
 
-    # Sentiment
+    # Sentiment Analysis
     progress.text("Analyzing sentiment...")
     raw_scores = sentiment_pipeline(review)[0]
-    sentiment_results = {LABEL_MAP[item['label']]: float(item['score']) for item in raw_scores}
+    sentiment_results = {
+        LABEL_MAP[item['label']]: float(item['score'])
+        for item in raw_scores
+    }
     progress.progress(40)
 
-    # Keywords
+    # Keyword Extraction
     progress.text("Extracting keywords...")
     keywords = kw_model.extract_keywords(
         review,
@@ -76,7 +87,7 @@ def main():
     )
     progress.progress(60)
 
-    # Display
+    # Display Results
     col1, col2 = st.columns(2)
     with col1:
         st.subheader("Sentiment Scores")
@@ -86,16 +97,24 @@ def main():
         for kw, score in keywords:
             st.write(f"• {kw} ({score:.4f})")
 
-    # Chart
+    # Bar Chart
     progress.text("Rendering chart...")
-    df_scores = pd.DataFrame.from_dict(sentiment_results, orient='index', columns=['score'])
+    df_scores = pd.DataFrame.from_dict(
+        sentiment_results,
+        orient='index',
+        columns=['score']
+    )
     df_scores.index.name = 'Sentiment'
     st.bar_chart(df_scores)
     progress.progress(80)
 
-    # Highlight highest sentiment
-    max_label, max_score = max(sentiment_results.items(), key=lambda x: x[1])
-    st.markdown(f"**Highest Sentiment:** **{max_label}** ({max_score:.4f})")
+    # Highlight Highest Sentiment
+    max_label, max_score = max(
+        sentiment_results.items(), key=lambda x: x[1]
+    )
+    st.markdown(
+        f"**Highest Sentiment:** **{max_label}** ({max_score:.4f})"
+    )
 
     # FLAN-T5 Analysis & Suggestions
     progress.text("Generating insights...")
@@ -109,13 +128,17 @@ Tasks:
 1. Analysis: Write a concise paragraph (3 sentences) interpreting customer sentiment by combining the scores and keywords.
 2. Recommendations: Three separate paragraphs with actionable suggestions (max 30 words each).
 """
-    output = generation_pipeline(prompt, max_length=200, do_sample=False)[0]['generated_text']
+    output = generation_pipeline(
+        prompt,
+        max_length=200,
+        do_sample=False
+    )[0]['generated_text']
     st.markdown(output)
 
+    # Done
     progress.progress(100)
     progress.text("Done!")
 
 
 if __name__ == "__main__":
     main()
-