Create app.py

app.py

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+import torch
+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# Use a pipeline as a high-level helper
+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 is good", "This product is quite expensive"]))
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label'], sentiment[0]['score']  # Return sentiment label and confidence score
+
+def create_sentiment_pie_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Style the pie chart
+    colors = ['#e67e22', '#3498db']  # Green for Positive, Red for Negative
+    explode = [0.05, 0]  # Slightly separate the positive slice for emphasis
+
+    fig, ax = plt.subplots(figsize=(6, 6))
+    sentiment_counts.plot(
+        kind='pie',
+        colors=colors,
+        autopct='%1.1f%%',
+        startangle=140,
+        explode=explode,
+        ax=ax,
+        textprops={'fontsize': 10, 'color': 'black'},
+        radius=0.7 # Decrease radius to make the pie smaller
+    )
+    ax.set_title("Sentiment Distribution (Pie Chart)", fontsize=10, fontweight='bold', color='#34495e')
+    ax.set_ylabel("")  # Remove default 'Count' label
+    plt.tight_layout()
+
+    return fig
+
+# Create a styled bar chart
+def create_sentiment_bar_chart(df):
+    sentiment_counts = df['Sentiment'].value_counts()
+
+    # Use Seaborn for stylish bar plots
+    fig, ax = plt.subplots(figsize=(8, 5))
+    # Use a categorical variable for hue to avoid the warning
+    sns.barplot(
+        x=sentiment_counts.index,
+        y=sentiment_counts.values,
+        # for hue warning which doesn't effect code
+        hue=sentiment_counts.index,  # Use 'Sentiment' as the hue variable
+        palette=['#e67e22', '#3498db'],  # Green for Positive, Red for Negative
+        ax=ax,
+        legend=False,  # We don't need a legend for this plot
+        width = 0.5
+    )
+
+    # Add value annotations on top of each bar
+    for idx, value in enumerate(sentiment_counts.values):
+        ax.text(idx, value + 0.5, str(value), ha='center', fontsize=10, color='black', fontweight='bold')
+
+    ax.set_title("Sentiment Distribution (Bar Chart)", fontsize=12, fontweight='bold', color='#34495e')
+    ax.set_xlabel("Sentiment", fontsize=10, fontweight='bold', color='#34495e')
+    ax.set_ylabel("Count", fontsize=10, fontweight='bold', color='#34495e')
+    ax.spines['top'].set_visible(False)  # Clean up borders
+    ax.spines['right'].set_visible(False)
+
+    plt.xticks(fontsize=10, fontweight='bold', color='#34495e')
+    plt.yticks(fontsize=10, color='#34495e')
+    plt.tight_layout()
+
+    return fig
+
+
+# Define the main function to process the Excel file
+def read_reviews_and_analyze_sentiment(file_object):
+    # Read the Excel file into a DataFrame
+    df = pd.read_excel(file_object)
+
+    # Ensure the column containing reviews exists
+    if 'Reviews' not in df.columns:
+        raise ValueError("The Excel file must contain a column named 'Reviews'.")
+
+    # Apply the sentiment analyzer and split into two columns
+    df[['Sentiment', 'Confidence']] = df['Reviews'].apply(
+        lambda x: pd.Series(sentiment_analyzer(x))
+    )
+
+    # Generate the pie and bar charts
+    pie_chart = create_sentiment_pie_chart(df)
+    bar_chart = create_sentiment_bar_chart(df)
+
+    return df, pie_chart, bar_chart
+
+
+
+# Example usage:
+# df = process_reviews("reviews.xlsx")
+# print(df)
+
+# result = read_reviews_and_analyze_sentiment("../Files/Updated_Reviews_Sample.xlsx")
+# print(result)
+
+import gradio as gr
+
+# Custom CSS for styling the title
+custom_css = """
+.title { font-size: 24px; font-weight: bold; color: #34495e; }
+.gradio-label, .gradio-description {
+    font-size: 18px;  /* Increase font size */
+    font-weight: bold;  /* Make text bold */
+}
+"""
+
+# Interface
+interface = gr.Interface(
+    fn=read_reviews_and_analyze_sentiment,
+    inputs=gr.File(file_types=[".xlsx"], label="Upload Excel File"),  # Accept only Excel files
+    outputs=[
+        gr.Dataframe(
+            headers=["Reviews", "Sentiment", "Confidence"],  # Custom headers for output
+            label="Sentiment Analysis Results"
+        ),
+        gr.Plot(label="Sentiment Distribution (Pie Chart)"),
+        gr.Plot(label="Sentiment Distribution (Bar Chart)")
+    ],
+    title="Sentiment Analyzer with Confidence Scores",
+    description=(
+        "Upload an Excel file with a 'Reviews' column to analyze sentiments and "
+        "visualize the distribution in both pie and bar charts."
+    ),
+    theme="default",  # Align components vertically
+    css=custom_css  # Apply the custom CSS
+)
+
+interface.launch()
+