Create app.py

app.py

@@ -0,0 +1,86 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from transformers import pipeline
+import gradio as gr
+
+# Initialize the sentiment analyzer pipeline
+sentiment_Analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# 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:
+        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
+
+# 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
+    plt.title('Sentiment Analysis of Reviews')
+    chart_path = 'sentiment_pie_chart.png'
+    plt.savefig(chart_path)
+    plt.show()
+    return chart_path
+
+# Function to process the uploaded file and generate the output
+def process_reviews(file):
+    # Read reviews from the uploaded file
+    df = read_reviews_to_dataframe(file.name)
+    # Evaluate the reviews
+    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
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.File(label="Upload a text file with reviews"),  # Input: File upload
+    outputs=[
+        gr.Dataframe(label="Reviews with Evaluation"),  # Output: DataFrame
+        gr.Image(label="Sentiment Pie Chart")  # Output: Pie chart image
+    ],
+    title="Sentiment Analyzer",  # Title of the interface
+    description="Upload a text file with reviews to analyze the sentiment and visualize the results."  # Description
+)
+
+# Launch the Gradio interface
+interface.launch()