Update app.py

app.py

@@ -4,16 +4,20 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from transformers import pipeline
 
-# Use a pipeline with a model suitable for sentiment analysis
-analyzer = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+# Use a pipeline with an appropriate model for sentiment analysis
+# Replace with a correct model suitable for your needs
+model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+analyzer = pipeline("text-classification", model=model_name)
 
 def sentiment_analyzer(review):
-    sentiment = analyzer(review)
-    return sentiment[0]['label']
+    try:
+        sentiment = analyzer(review)
+        return sentiment[0]['label']
+    except Exception as e:
+        return f"Error: {e}"
 
 def sentiment_bar_chart(df):
     sentiment_counts = df['Sentiment'].value_counts()
-    # Create a bar chart
     fig, ax = plt.subplots()
     sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', colors=['green', 'red'])
     ax.set_title('Review Sentiment Counts')
@@ -22,15 +26,15 @@ def sentiment_bar_chart(df):
     return fig
 
 def read_reviews_and_analyze_sentiment(file_object):
-    # Load the Excel file into a DataFrame
-    df = pd.read_excel(file_object)
-    # Check if 'Reviews' column is in the DataFrame
-    if 'Reviews' not in df.columns:
-        raise ValueError("Excel file must contain a 'Reviews' column.")
-    # Apply the sentiment analysis function to each review in the DataFrame
-    df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
-    chart_object = sentiment_bar_chart(df)
-    return df, chart_object
+    try:
+        df = pd.read_excel(file_object)
+        if 'Reviews' not in df.columns:
+            raise ValueError("Excel file must contain a 'Reviews' column.")
+        df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
+        chart_object = sentiment_bar_chart(df)
+        return df, chart_object
+    except Exception as e:
+        return f"Error: {e}", None
 
 gr.close_all()