app.py

import gradio as gr
import joblib

# Load models
models = {
    "Logistic Regression": joblib.load("models/best_model.joblib"),
    "Random Forest": joblib.load("models/random_forest_model.joblib"),
    "SVM (Linear)": joblib.load("models/svm_model_linear.joblib"),
    "SVM (Polynomial)": joblib.load("models/svm_model_polynomial.joblib"),
    "SVM (RBF)": joblib.load("models/svm_model_rbf.joblib"),
    "KNN": joblib.load("models/trained_knn_model.joblib"),
}

# Define prediction function
def predict(review, model_name):
    model = models[model_name]
    prediction = model.predict([review])[0]
    probabilities = model.predict_proba([review])[0]
    return {
        "Predicted Class": str(prediction),
        "Class Probabilities": {
            "Class 0": probabilities[0],
            "Class 1": probabilities[1],
        },
    }

# Create Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label="Review Comment"),
        gr.Dropdown(choices=list(models.keys()), label="Model"),
    ],
    outputs=gr.JSON(label="Prediction Results"),
    title="Text Classification Models",
    description="Choose a model and provide a review to see the predicted sentiment class.",
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()