Update app.py

app.py

@@ -8,35 +8,35 @@ from transformers import pipeline
 # model_path = ("../Models/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english"
 #               "/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
 
-# 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"]))
+# analyzer = pipeline("text-classification",
+#                 model=model_path)
 
 
+
+# print(analyzer(["This production is good", "This product was quite expensive"]))
+
 def sentiment_analyzer(review):
     sentiment = analyzer(review)
     return sentiment[0]['label']
 
-def sentiment_pie_chart(df):
+def sentiment_bar_chart(df):
     sentiment_counts = df['Sentiment'].value_counts()
 
-    # Create a pie chart
+    # Create a bar chart
     fig, ax = plt.subplots()
-    pie = ax.pie(sentiment_counts, autopct='%1.1f%%', colors=['green', 'red'], labels=sentiment_counts.index)
-    
-    # Add count values to each slice
-    for i, p in enumerate(pie[0]):
-        count = f'({sentiment_counts[i]})'
-        ax.annotate(count, xy=p.get_xy(), xytext=(0, 0.5), textcoords='offset points', ha='center', fontsize=10)
-
+    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
 
+
 def read_reviews_and_analyze_sentiment(file_object):
     # Load the Excel file into a DataFrame
     df = pd.read_excel(file_object)
@@ -47,16 +47,19 @@ def read_reviews_and_analyze_sentiment(file_object):
 
     # Apply the get_sentiment function to each review in the DataFrame
     df['Sentiment'] = df['Reviews'].apply(sentiment_analyzer)
-    chart_object = sentiment_pie_chart(df)
+    chart_object = sentiment_bar_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 reviews file")],
+                    inputs=[gr.File(file_types=["xlsx"], label="Upload your review comment file")],
                     outputs=[gr.Dataframe(label="Sentiments"), gr.Plot(label="Sentiment Analysis")],
-                    title="Sentiment Analyzer",
-                    description="Analyze the sentiment based on file uploaded (Excel file .xlsx must contain a 'Reviews' column)")
+                    title="@GenAILearniverse Project 3: Sentiment Analyzer",
+                    description="THIS APPLICATION WILL BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
 demo.launch()