Update app.py

app.py

@@ -2,19 +2,10 @@ import torch
 import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
-
-# Use a pipeline as a high-level helper
 from transformers import pipeline
 
-analyzer = pipeline("text-classification",
-                model="dicta-il/dictalm2.0")
-
-# analyzer = pipeline("text-classification",
-#                 model=model_path)
-
-
-
-# print(analyzer(["This production is good", "This product was quite expensive"]))
+# Use a pipeline with a model suitable for sentiment analysis
+analyzer = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
 
 def sentiment_analyzer(review):
     sentiment = analyzer(review)
@@ -22,42 +13,30 @@ def sentiment_analyzer(review):
 
 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%%', color=['green', 'red'])
+    sentiment_counts.plot(kind='pie', ax=ax, autopct='%1.1f%%', colors=['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)
-
-    # Check if 'Review' column is in the DataFrame
+    # Check if 'Reviews' column is in the DataFrame
     if 'Reviews' not in df.columns:
-        raise ValueError("Excel file must contain a 'Review' column.")
-
-    # Apply the get_sentiment function to each review in the DataFrame
+        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
 
-# 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)
-
+gr.close_all()
 
 demo = gr.Interface(fn=read_reviews_and_analyze_sentiment,
                     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="THIS APPLICATION WILL BE USED TO ANALYZE THE SENTIMENT BASED ON FILE UPLAODED.")
-demo.launch()
+                    description="This application will be used to analyze the sentiment based on the uploaded file.")
+demo.launch()