Update app.py

app.py

@@ -8,32 +8,27 @@ from transformers import pipeline
 # model_path = ("../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
 #               "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
 
-analyzer = pipeline("text-classification",
-                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
-
-# analyzer = pipeline("text-classification",
-#                 model=model_path)
+# analyzer = pipeline("text-classification", model=model_path)
 
 
+analyzer = pipeline("text-classification",
+                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
 
 # print(analyzer(["This production is good", "This product was quite expensive"]))
 
+
 def sentiment_analyzer(review):
     sentiment = analyzer(review)
     return sentiment[0]['label']
 
-def sentiment_bar_chart(df):
+def sentiment_pie_chart(df):
     sentiment_counts = df['Sentiment'].value_counts()
 
-    # Create a bar chart
+    # Create a pie chart
     fig, ax = plt.subplots()
     sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', color=['green', 'red'])
     ax.set_title('Review Sentiment Counts')
-    ax.set_xlabel('Sentiment')
-    ax.set_ylabel('Count')
-    # ax.set_xticklabels(['Positive', 'Negative'], rotation=0)
 
-    # Return the figure object
     return fig
 
 
@@ -43,23 +38,19 @@ def read_reviews_and_analyze_sentiment(file_object):
 
     # Check if 'Review' column is in the DataFrame
     if 'Reviews' not in df.columns:
-        raise ValueError("Excel file must contain a 'Review' column.")
+        raise ValueError("Excel file must contain a 'Reviews' column.")
 
     # Apply the get_sentiment function to each review in the DataFrame
     df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
-    chart_object = sentiment_bar_chart(df)
+    chart_object = sentiment_pie_chart(df)
     return df, chart_object
 
 # result = read_reviews_and_analyze_sentiment("../Files/Prod-review.xlsx")
 # print(result)
-# Example usage:
-# df = read_reviews_and_analyze_sentiment('path_to_your_excel_file.xlsx')
-# print(df)
-
 
 demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
-                    inputs=[gr.File(file_types=["xlsx"], label="Upload your review comment file")],
+                    inputs=[gr.File(file_types=["xlsx"], label="Upload your reviews file")],
                     outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
-                    title="@GenAILearniverse Project 3: Sentiment Analyzer",
-                    description="THIS APPLICATION WILL BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
+                    title="Sentiment Analyzer",
+                    description="Analyze the sentiment based on file uploaded (Excel file .xlsx must contain a 'Reviews' column)")
 demo.launch()