| import gradio as gr | |
| import joblib | |
| # Load your trained model from the model file | |
| model = joblib.load("model.pkl") | |
| # Define a function to use with the Gradio interface | |
| def predict_sentiment(text): | |
| prediction = model.predict([text])[0] | |
| probabilities = model.predict_proba([text])[0] | |
| return { | |
| "Prediction": prediction, | |
| "Probabilities": dict(zip(model.classes_, probabilities)) | |
| } | |
| # Create a Gradio interface | |
| interface = gr.Interface( | |
| fn=predict_sentiment, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."), | |
| outputs=gr.JSON(label="Prediction and Probabilities"), | |
| title="Sentiment Analysis App", | |
| description="This app predicts sentiment responses using a trained logistic regression model." | |
| ) | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| interface.launch() | |