import gradio as gr
import requests
import os
import time

HF_TOKEN = os.environ.get("HF_TOKEN")
API_URL = "https://router.huggingface.co/hf-inference/models/Hate-speech-CNERG/dehatebert-mono-english"

def query(text):
    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
    for i in range(3):
        response = requests.post(API_URL, headers=headers, json={
            "inputs": text,
            "options": {"wait_for_model": True}
        })
        if response.status_code == 200:
            return response.json()
        time.sleep(3)
    return {"error": f"Status: {response.status_code}, Response: {response.text}"}

def predict(comment):
    if not comment.strip():
        return "⚠️ Please enter some text.", ""
    
    try:
        result = query(comment)
        
        if isinstance(result, dict) and "error" in result:
            return f"API Error: {result['error']}", ""
        
        if isinstance(result, list):
            result = result[0][0]
            label = result["label"]
            score = result["score"]
            
            if label == "HATE":
                prediction = "🚨 Hate Speech Detected"
                probability = round(score * 100, 2)
            else:
                prediction = "✅ No Hate Speech"
                probability = round((1 - score) * 100, 2)
            
            return prediction, f"{probability}%"
        
        return f"Unexpected: {result}", ""
    
    except Exception as e:
        return f"Exception: {str(e)}", ""

demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=4, placeholder="Enter a comment...", label="Comment"),
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Textbox(label="Confidence")
    ],
    title="🛡️ Hate Speech Detector",
    description="Enter any text to check if it contains hate speech."
)

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