import gradio as gr
from transformers import pipeline

# Load pre-trained content moderation model
classifier = pipeline("text-classification", 
                     model="unitary/toxic-bert",
                     top_k=None)

def analyze_content(text):
    if not text or len(text.strip()) == 0:
        return {
            "is_malicious": False,
            "risk_level": "SAFE",
            "confidence": "N/A",
            "explanation": "No text provided"
        }
    
    try:
        results = classifier(text)[0]
        
        max_score = 0
        toxic_type = "none"
        
        for result in results:
            if result['label'] != 'neutral' and result['score'] > max_score:
                max_score = result['score']
                toxic_type = result['label']
        
        is_malicious = max_score > 0.5
        risk_level = "HIGH" if max_score > 0.7 else ("MEDIUM" if is_malicious else "LOW")
        
        return {
            "is_malicious": is_malicious,
            "risk_level": risk_level,
            "confidence": f"{max_score * 100:.1f}%",
            "toxic_type": toxic_type,
            "explanation": f"Detected {toxic_type} content" if is_malicious else "Content appears safe"
        }
    
    except Exception as e:
        return {
            "error": str(e),
            "is_malicious": False,
            "risk_level": "ERROR"
        }

demo = gr.Interface(
    fn=analyze_content,
    inputs=gr.Textbox(lines=5, placeholder="Enter text to analyze...", label="Content"),
    outputs=gr.JSON(label="Analysis Results"),
    title="GenZ ContentGuard AI",
    description="AI content moderation"
)

if __name__ == "__main__":
    demo.launch()