app.py

import gradio as gr
import os
import sys

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from api.predict import predict_review, models_loaded

def analyze_review(text):
    if not text or len(text.strip()) == 0:
        return "error: please enter some text"
    
    if not models_loaded:
        return "models are loading for the first time, this will take 20-30 minutes. please wait..."
    
    try:
        result = predict_review(text)
        
        if "error" in result and result["prediction"] == "error":
            return f"error: {result['error']}"
        
        prediction = result['prediction']
        confidence = result['confidence']
        is_fake = result['is_fake']
        
        status = "FAKE" if is_fake else "GENUINE"
        
        output = f"""prediction: {status}
confidence: {confidence:.2%}

fake probability: {result['fake_probability']:.2%}
genuine probability: {result['genuine_probability']:.2%}

model agreement: {result['model_agreement']:.1f}%
length category: {result['length_category']}
token count: {result['token_count']}"""
        
        return output
        
    except Exception as e:
        return f"error: {str(e)}"

demo = gr.Interface(
    fn=analyze_review,
    inputs=gr.Textbox(
        lines=5,
        placeholder="paste review text here...",
        label="review text"
    ),
    outputs=gr.Textbox(
        lines=10,
        label="analysis"
    ),
    title="review classifier",
    description="ensemble model for detecting fake reviews"
)

if __name__ == "__main__":
    print("starting gradio interface", flush=True)
    print("models will load on first prediction request", flush=True)
    demo.launch(server_name="0.0.0.0", server_port=7860)