import gradio as gr
import numpy as np

print("✅ App starting without TensorFlow...")

def predict_intrusion(features_input):
    try:
        features = [float(x.strip()) for x in features_input.split(',') if x.strip()]
        
        if len(features) != 119:
            return {"Prediction": "ERROR", "Confidence": "0%", "Message": f"Need 119 features, got {len(features)}"}
        
        # Simple simulation (remove when TensorFlow works)
        avg_value = sum(features) / len(features)
        
        if avg_value > 0.4:
            return {
                "Prediction": "🚨 ATTACK DETECTED", 
                "Confidence": "85%", 
                "Message": "Potential threat detected (SIMULATION MODE)"
            }
        else:
            return {
                "Prediction": "✅ NORMAL TRAFFIC", 
                "Confidence": "92%", 
                "Message": "Traffic appears normal (SIMULATION MODE)"
            }
            
    except Exception as e:
        return {"Prediction": "ERROR", "Confidence": "0%", "Message": f"Error: {str(e)}"}

# Create interface
with gr.Blocks() as demo:
    gr.Markdown("# 🔒 Network Intrusion Detection System")
    gr.Markdown("**Simulation Mode - TensorFlow installing...**")
    
    with gr.Row():
        with gr.Column():
            features = gr.Textbox(label="Enter 119 features (comma-separated)", lines=5)
            btn = gr.Button("Analyze Traffic")
        
        with gr.Column():
            result = gr.Label(label="Prediction Result")
    
    btn.click(fn=predict_intrusion, inputs=features, outputs=result)

demo.launch()