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| from transformers import pipeline # For loading Hugging Face pre-trained models | |
| import gradio as gr # For creating the user interface | |
| import pandas as pd # For handling CSV and Excel files | |
| import matplotlib.pyplot as plt # For plotting the pie chart | |
| # Load the pre-trained sentiment-analysis model from Hugging Face | |
| # This is a binary sentiment model (Positive/Negative) | |
| sentiment_analyzer = pipeline( | |
| "sentiment-analysis", | |
| model="distilbert-base-uncased-finetuned-sst-2-english" # Model fine-tuned on SST-2 for sentiment analysis | |
| ) | |
| # Function to analyze sentiment of a single text input | |
| def analyze_sentiment(text): | |
| # Run the text through the sentiment analyzer | |
| result = sentiment_analyzer(text) | |
| # Return the sentiment label (Positive/Negative) | |
| return result[0]['label'] | |
| # Function to analyze a file (CSV or Excel) and return sentiment results + pie chart | |
| def analyze_file(file): | |
| # Read the uploaded file based on its extension (CSV or Excel) | |
| if file.name.endswith(".csv"): | |
| df = pd.read_csv(file.name) # Read as CSV | |
| elif file.name.endswith(".xlsx"): | |
| df = pd.read_excel(file.name) # Read as Excel | |
| else: | |
| return "Unsupported file format", None # Return error message if the file format is unsupported | |
| # Check if 'Text' column is present in the file | |
| if "Text" not in df.columns: | |
| return "Missing 'Text' column", None # Return error if 'Text' column is missing | |
| # Predict sentiment for each row in the 'Text' column | |
| sentiments = [sentiment_analyzer(t)[0]['label'] for t in df["Text"].fillna("")] # Handle empty cells with fillna("") | |
| # Create a DataFrame with 'Text' and corresponding 'Sentiment' | |
| df_result = pd.DataFrame({ | |
| "Text": df["Text"], # Original text column | |
| "Sentiment": sentiments # Sentiment predictions | |
| }) | |
| # Generate a pie chart to show the distribution of sentiments | |
| sentiment_counts = df_result["Sentiment"].value_counts() # Count the occurrences of each sentiment | |
| fig, ax = plt.subplots() # Create a figure and axis for plotting | |
| ax.pie( | |
| sentiment_counts.values, # Data for the pie chart | |
| labels=sentiment_counts.index, # Sentiment labels (e.g., 'POSITIVE', 'NEGATIVE') | |
| autopct="%1.1f%%", # Show percentage on the pie chart | |
| colors=["#66bb6a", "#ef5350", "#ffee58"][:len(sentiment_counts)] # Color palette for the pie chart | |
| ) | |
| ax.set_title("Sentiment Distribution") # Title of the pie chart | |
| return df_result, fig # Return the DataFrame and the pie chart figure | |
| # Creating the Gradio interface with two tabs: one for text input and one for file upload | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🧠 Text Sentiment Analyzer with Pie Chart") # Title | |
| gr.Markdown("Enter text manually or upload a file with a **Text** column.") # Description | |
| # Tab for analyzing a single text input | |
| with gr.Tab("🔤 Single Text"): | |
| txt = gr.Textbox(label="Text", lines=4) # Textbox for text input | |
| out = gr.Textbox(label="Sentiment") # Textbox to show sentiment output | |
| btn = gr.Button("Analyze") # Button to trigger analysis | |
| btn.click(analyze_sentiment, inputs=txt, outputs=out) # Trigger analysis when button is clicked | |
| # Tab for uploading a file (CSV or Excel) | |
| with gr.Tab("📁 Upload File"): | |
| file_input = gr.File(label="Upload CSV or Excel File") # File uploader | |
| file_output = gr.Dataframe(label="Sentiment Results") # Table to display the sentiment results | |
| chart_output = gr.Plot(label="Sentiment Distribution Pie Chart") # Pie chart to display sentiment distribution | |
| btn2 = gr.Button("Analyze File") # Button to trigger file analysis | |
| btn2.click(analyze_file, inputs=file_input, outputs=[file_output, chart_output]) # Trigger file analysis when button is clicked | |
| # Launch the app | |
| demo.launch() |