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(reviewText):
    from api.predict import models_loaded
    
    if not reviewText or len(reviewText.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(reviewText)
        print(f"raw result: {result}", flush=True)
        
        if "error" in result and result["prediction"] == "error":
            return f"error: {result['error']}"
        
        output = f"""prediction: {result['prediction']}
confidence: {result['confidence']:.2%}

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

model agreement: {result['model_agreement']:.1f}%
is fake: {result['is_fake']}
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("preloading models...", flush=True)
    try:
        from api.predict import loadResources
        loadResources()
        print("models preloaded successfully", flush=True)
    except Exception as e:
        print(f"error preloading models: {str(e)}", flush=True)
    demo.launch(server_name="0.0.0.0", server_port=7860)