| import gradio as gr | |
| from joblib import dump, load | |
| model_names = ['Logistic_Regression', 'LDA', 'QDA', 'Naive_Bayes', 'SVC'] | |
| models = {} | |
| vectorizer = load('vectorizer.joblib') | |
| for name in model_names: | |
| models[name] = load(name + '.joblib') | |
| def predict(text, method): | |
| prediction = models[method].predict_proba(vectorizer.transform([text]).toarray())[0] | |
| return {'Negative Comment':prediction[0], 'Positive Comment':prediction[1]} | |
| input_module1 = gr.Textbox(label='Review Comment') | |
| input_module2 = gr.Dropdown(choices=model_names, label = "method") | |
| output_module = gr.Label(num_top_classes=2,label = "Predicted Probabilities") | |
| gr.Interface(fn=predict, inputs=[input_module1,input_module2], outputs=output_module).launch(debug=True) |