import gradio as gr
import joblib

# Load models and vectorizer from the models folder
logistic_model = joblib.load("models/best_logistic_model.pkl")
svm_model = joblib.load("models/best_svc_model.pkl")
random_forest_model = joblib.load("models/best_rf_model.pkl")
knn_model = joblib.load("models/best_knn_model.pkl")
vectorizer = joblib.load("models/vectorizer.pkl")

# Model selection mapping
models = {
    "Logistic Regression": logistic_model,
    "SVM": svm_model,
    "Random Forest": random_forest_model,
    "KNN": knn_model,
}

# Prediction function
def predict_sentiment(review, model_name):
    try:
        if not review.strip():
            return "Error: Review cannot be empty", None

        if model_name not in models:
            return "Error: Invalid model selected", None

        # Preprocess the text
        text_vector = vectorizer.transform([review])

        # Predict using the selected model
        model = models[model_name]
        prediction = model.predict(text_vector)[0]
        probabilities = model.predict_proba(text_vector)[0] if hasattr(model, "predict_proba") else None

        # Format the output
        sentiment = "Positive Feedback" if prediction == 1 else "Negative Feedback"
        probabilities_output = (
            {
                "Positive": probabilities[1],  # Raw probability (0.0 - 1.0)
                "Negative": probabilities[0],  # Raw probability (0.0 - 1.0)
            }
            if probabilities is not None
            else "Probabilities not available"
        )

        return sentiment, probabilities_output
    except Exception as e:
        # Log the error to the console for debugging
        print(f"Error in prediction: {e}")
        return f"Error: {str(e)}", None

# Create Gradio Interface
inputs = [
    gr.Textbox(label="Review Comment", placeholder="Enter your review here..."),
    gr.Dropdown(choices=["Logistic Regression", "SVM", "Random Forest", "KNN"], label="Model"),
]

outputs = [
    gr.Textbox(label="Predicted Sentiment Class"),
    gr.Label(label="Predicted Probability"),
]

# Launch Gradio App
gr.Interface(
    fn=predict_sentiment,
    inputs=inputs,
    outputs=outputs,
    title="Sentiment Analysis",
    description="Enter a review and select a model to predict sentiment.",
).launch()