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| import gradio as gr | |
| from transformers import pipeline | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from wordcloud import WordCloud | |
| import plotly.express as px | |
| # Initialize sentiment analysis pipeline | |
| sentiment_pipeline = pipeline("sentiment-analysis", model='distilbert-base-uncased-finetuned-sst-2-english') | |
| def categorize_sentiment(score): | |
| emoji_mapping = { | |
| 'Highly Dissatisfied': '😡', | |
| 'Dissatisfied': '😞', | |
| 'Neutral': '😐', | |
| 'Satisfied': '😊', | |
| 'Highly Satisfied': '😄' | |
| } | |
| if score >= 0.8: | |
| return 'Highly Satisfied', emoji_mapping['Highly Satisfied'] | |
| elif score >= 0.1: | |
| return 'Satisfied', emoji_mapping['Satisfied'] | |
| elif score >= -0.1: | |
| return 'Neutral', emoji_mapping['Neutral'] | |
| elif score >= -0.8: | |
| return 'Dissatisfied', emoji_mapping['Dissatisfied'] | |
| else: | |
| return 'Highly Dissatisfied', emoji_mapping['Highly Dissatisfied'] | |
| def analyze_sentiment(input_text): | |
| result = sentiment_pipeline(input_text)[0] | |
| label = result['label'] | |
| score = result['score'] | |
| if label == "NEGATIVE": | |
| score = -score # Make the score negative if the label is negative | |
| emoji = "😊" if label == "POSITIVE" else "😞" | |
| sentiment_label_with_emoji = f'{label} {emoji}' | |
| sentiment_category, emoji = categorize_sentiment(score) | |
| return str(score), sentiment_label_with_emoji, sentiment_category | |
| def analyze_sentiments_from_excel(file): | |
| try: | |
| df = pd.read_excel(file.name) | |
| except Exception as e: | |
| return str(e), None, None, None, None # Return error message if file cannot be read | |
| results = [] | |
| for text in df['Text']: | |
| result = sentiment_pipeline(text)[0] | |
| label = result['label'] | |
| score = result['score'] | |
| if label == "POSITIVE": | |
| score = abs(score) | |
| else: | |
| score = -abs(score) | |
| sentiment_label, emoji = categorize_sentiment(score) | |
| results.append((text, score, label, sentiment_label, emoji)) | |
| results_df = pd.DataFrame(results, columns=['Text', 'Sentiment Score', 'Sentiment Label', 'Sentiment Category', 'Emoji']) | |
| # Calculate percentages for each sentiment category | |
| sentiment_counts = results_df['Sentiment Category'].value_counts(normalize=True) * 100 | |
| # Create pie chart for sentiment labels | |
| fig_labels = px.pie(results_df, names='Sentiment Label', title='Sentiment Label Distribution') | |
| # Create histogram for sentiment scores | |
| fig_scores = px.histogram(results_df, x='Sentiment Score', title='Sentiment Score Distribution') | |
| # Generate Word Cloud | |
| text = " ".join(results_df['Text'].tolist()) | |
| wordcloud = WordCloud(width=800, height=400, random_state=21, max_font_size=110).generate(text) | |
| plt.figure(figsize=(12, 8)) | |
| plt.imshow(wordcloud, interpolation="bilinear") | |
| plt.axis('off') | |
| plt.title('Word Cloud') | |
| plt.tight_layout() | |
| wordcloud_fig = plt | |
| return results_df, sentiment_counts, fig_labels, fig_scores, wordcloud_fig | |
| def switch_app(app_name): | |
| if app_name == "Sentiment Analysis": | |
| iface1.show() | |
| iface2.hide() | |
| else: | |
| iface1.hide() | |
| iface2.show() | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text = gr.Textbox(lines=5, label="Input Text", placeholder="Kindly type in your product review") | |
| output_text = [ | |
| gr.Textbox(label="Sentiment Score"), | |
| gr.Textbox(label="Sentiment Label"), | |
| gr.HTML(label="Sentiment Category") | |
| ] | |
| iface1 = gr.Interface(analyze_sentiment, | |
| inputs=input_text, | |
| outputs=output_text, | |
| title='<span style="color: yellow">Sentiment Analysis</span>', | |
| description='Explore the Emotions in Text. Experience the power of sentiment analysis with our tool. Get precise sentiment scores, emotive emojis, and categorized sentiments for any text.', | |
| theme='dark', | |
| css="""body {background-color: #121212; font-family: Arial, sans-serif;} | |
| .gradio {box-shadow: none; border-radius: 10px;} | |
| .input {background-color: #1E1E1E; color: #FFFFFF; border: none;} | |
| .output {background-color: #1E1E1E; color: #FFFFFF; border: none;} | |
| .output p {margin: 5px;} | |
| .output div {margin: 5px;} | |
| .output .highlight {padding: 5px; border-radius: 5px;}""") | |
| with gr.Column(): | |
| file = gr.File(label="Upload Excel file") | |
| output_text = [ | |
| gr.Dataframe(label="Text Analysis"), | |
| gr.Textbox(label="Sentiment Category"), | |
| gr.Plot(label="Sentiment Label"), | |
| gr.Plot(label="Sentiment Score"), | |
| gr.Plot(label="Word Cloud") | |
| ] | |
| iface2 = gr.Interface(analyze_sentiments_from_excel, | |
| inputs=file, | |
| outputs=output_text, | |
| title='<span style="color: yellow">Sentiment Analysis from Excel</span>', | |
| description='Upload an Excel file with sentiment texts and predict sentiment scores and labels. Analyze the sentiment distribution.', | |
| theme='dark', | |
| css="""body {background-color: #121212; font-family: Arial, sans-serif;} | |
| .gradio {box-shadow: none; border-radius: 10px;} | |
| .input {background-color: #1E1E1E; color: #FFFFFF; border: none;} | |
| .output {background-color: #1E1E1E; color: #FFFFFF; border: none;} | |
| .output p {margin: 5px;} | |
| .output div {margin: 5px;} | |
| .output .highlight {padding: 5px; border-radius: 5px;}""") | |
| with gr.Row(): | |
| switch_button = gr.Button("Switch App") | |
| switch_button.click(switch_app, inputs=gr.Dropdown(["Sentiment Analysis", "Sentiment Analysis from Excel"], label="Select App"), outputs=None) | |
| demo.launch() |