Update app.py

app.py

@@ -2,6 +2,10 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from transformers import pipeline
 import gradio as gr
+import os
+
+# Define the default file path for the example reviews.txt
+DEFAULT_FILE_PATH = "reviews.txt"
 
 # Initialize the sentiment analyzer pipeline
 sentiment_Analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
@@ -9,78 +13,65 @@ sentiment_Analyzer = pipeline("text-classification", model="distilbert/distilber
 # Function to read reviews from a text file and convert to a pandas DataFrame
 def read_reviews_to_dataframe(file_path):
     reviews = []
-    # Open and read the file line by line
-    with open(file_path, 'r') as file:
+    with open(file_path, 'r', encoding='utf-8') as file:
         for line in file:
             reviews.append(line.strip())
-    # Convert the list of reviews into a DataFrame
     df = pd.DataFrame(reviews, columns=['Review'])
     return df
 
 # Analyzer function to apply sentiment analysis on each review
 def analyzer(text):
-    # Use the sentiment analyzer pipeline to get the sentiment and score
-    output = (sentiment_Analyzer(text))[0]
-    label = output['label']
-    score = output['score']
-    return label, score
+    output = sentiment_Analyzer(text)[0]
+    return output['label'], output['score']
 
 # Function to add an 'Evaluation' column to the DataFrame based on sentiment analysis
 def evaluate_reviews(df):
-    # Apply the analyzer function to each review in the DataFrame
     df['Evaluation'] = df['Review'].apply(lambda x: analyzer(x))
-    # Split the evaluation into 'Sentiment' and 'Score' columns
     df[['Sentiment', 'Score']] = pd.DataFrame(df['Evaluation'].tolist(), index=df.index)
-    # Drop the original 'Evaluation' column
     df.drop(columns=['Evaluation'], inplace=True)
     return df
 
 # Function to create a pie chart showing the percentage of positive and negative reviews
 def create_pie_chart(df):
-    # Count the occurrences of each sentiment
     sentiment_counts = df['Sentiment'].value_counts()
     labels = sentiment_counts.index
     sizes = sentiment_counts.values
-    colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
-    fig1, ax1 = plt.subplots()
-    # Create a pie chart
-    ax1.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
-    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
-
-    # Set the title and save the plot as an image
+    colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']
+    fig, ax = plt.subplots()
+    ax.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
+    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
     plt.title('Sentiment Analysis of Reviews')
     chart_path = 'sentiment_pie_chart.png'
     plt.savefig(chart_path)
-    plt.show()
+    plt.close(fig)
     return chart_path
 
-# Function to process the uploaded file and generate the output
+# Function to process the file
 def process_reviews(file):
-    # Read reviews from the uploaded file
-    df = read_reviews_to_dataframe(file.name)
-    # Evaluate the reviews
+    file_path = file.name
+    df = read_reviews_to_dataframe(file_path)
     df = evaluate_reviews(df)
-    # Create a pie chart
     chart_path = create_pie_chart(df)
     return df, chart_path
 
 # Gradio interface function
 def gradio_interface(file):
-    # Process the uploaded file and return the DataFrame and pie chart path
-    df, chart_path = process_reviews(file)
-    return df, chart_path
+    return process_reviews(file)
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=gradio_interface,
-    inputs=gr.File(label="Upload a text file with reviews"),  # Input: File upload
+    inputs=gr.File(label="Upload a text file with reviews"),
     outputs=[
-        gr.Dataframe(label="Reviews with Evaluation"),  # Output: DataFrame
-        gr.Image(label="Sentiment Pie Chart")  # Output: Pie chart image
+        gr.Dataframe(label="Reviews with Evaluation"),
+        gr.Image(label="Sentiment Pie Chart")
     ],
-    title="Sentiment Analyzer",  # Title of the interface
-    description="Upload a text file with reviews to analyze the sentiment and visualize the results."  # Description
+    title="Sentiment Analyzer",
+    description="Upload a text file with reviews to analyze the sentiment and visualize the results.",
+    examples=[[DEFAULT_FILE_PATH]],
+    allow_flagging="never"
 )
 
 # Launch the Gradio interface
 interface.launch()
+