app.py

import gradio as gr
import os
import sys
import json

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 json.dumps({"error": "please enter some text"}, indent=2)
    
    if not models_loaded:
        return json.dumps({"error": "models are loading for the first time, this will take 20-30 minutes. please wait..."}, indent=2)
    
    try:
        result = predict_review(reviewText)
        print(f"raw result: {result}", flush=True)
        
        if "error" in result and result["prediction"] == "error":
            return json.dumps({"error": result['error']}, indent=2)
        
        return json.dumps(result, indent=2)
        
    except Exception as e:
        return json.dumps({"error": str(e)}, indent=2)

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="SentinelCheck API",
    description="ensemble model (bert + roberta + distilbert) for detecting fake reviews targeting small businesses"
)

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)