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| import gradio as gr | |
| import joblib | |
| import preprocess | |
| # Load the trained model | |
| try: | |
| model = joblib.load("sentiment_model_best.pkl") | |
| print("Model loaded successfully.") | |
| except FileNotFoundError: | |
| print("Error: Model file 'sentiment_model_best.pkl' not found. Please run train_model.py first.") | |
| model = None | |
| def analyze_sentiment(text): | |
| if model is None: | |
| return "Model not loaded." | |
| # Preprocess | |
| clean_text = preprocess.preprocess_text(text) | |
| # Predict | |
| # The pipeline handles vectorization | |
| prediction = model.predict([clean_text])[0] | |
| # Get confidence scores if possible (LinearSVC uses decision_function, not predict_proba by default, | |
| # but for simplicity we rely on the label. | |
| # If we wanted proba, we'd need CalibratedClassifierCV or use LogisticRegression) | |
| return prediction | |
| # Custom CSS for a nicer look | |
| custom_css = """ | |
| body {background-color: #f0f2f5;} | |
| .gradio-container {max-width: 700px !important; margin-top: 50px !important;} | |
| h1 {text-align: center; color: #333;} | |
| """ | |
| with gr.Blocks(css=custom_css, title="Sentiment Analyzer") as demo: | |
| gr.Markdown("# 📊 Sentiment Analysis System") | |
| gr.Markdown("Enter a review or sentence below to analyze its sentiment (Positive, Negative, or Neutral).") | |
| with gr.Row(): | |
| input_text = gr.Textbox( | |
| label="Input Text", | |
| placeholder="Type something here... (e.g., 'The product is amazing!')", | |
| lines=3 | |
| ) | |
| with gr.Row(): | |
| analyze_btn = gr.Button("Analyze Sentiment", variant="primary") | |
| with gr.Row(): | |
| output_label = gr.Label(label="Predicted Sentiment") | |
| analyze_btn.click(fn=analyze_sentiment, inputs=input_text, outputs=output_label) | |
| gr.Markdown("---") | |
| gr.Markdown("### Examples") | |
| gr.Examples( | |
| examples=[ | |
| ["I absolutely love this! It's fantastic."], | |
| ["This is the worst experience I've ever had."], | |
| ["It's average, nothing special."], | |
| ], | |
| inputs=input_text | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=False) | |