import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model
model_name = "roberta-base-openai-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Detection logic
def detect_ai(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
    ai_score = round(probs[0][1].item() * 100, 2)
    human_score = round(probs[0][0].item() * 100, 2)

    if ai_score > 80:
        verdict = "⚠️ Likely AI-generated"
    elif human_score > 80:
        verdict = "✅ Likely Human-written"
    else:
        verdict = "❓ Unclear — Mixed Content"

    return {
        "AI-generated (%)": ai_score,
        "Human-written (%)": human_score,
        "Verdict": verdict
    }

# Build UI with Blocks
with gr.Blocks(css="footer {display: none !important;}") as demo:
    gr.Markdown("""
    # 🔍 AI Text Detector  
    **Check if a text was written by AI or a human**  
    Using `roberta-base-openai-detector` — Powered by 🤗 Hugging Face + Gradio
    """)

    with gr.Row():
        input_box = gr.Textbox(label="Paste your text", placeholder="Enter at least 40 words...", lines=10)
    
    with gr.Row():
        analyze_btn = gr.Button("🚀 Analyze Text")
    
    output_json = gr.JSON(label="📊 Detection Result")

    analyze_btn.click(fn=detect_ai, inputs=input_box, outputs=output_json)

demo.launch()