Update app.py

app.py

@@ -2,28 +2,49 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load Hugging Face AI detector model
+# 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_prob = round(probs[0][1].item() * 100, 2)
-    human_prob = round(probs[0][0].item() * 100, 2)
-    return f"AI-generated: {ai_prob}%\nHuman-written: {human_prob}%"
-
-# Interface
-interface = gr.Interface(
-    fn=detect_ai,
-    inputs=gr.Textbox(lines=10, placeholder="Enter text to analyze..."),
-    outputs="text",
-    title="AI Detector",
-    description="Detect if a piece of text was written by AI or a human using a Hugging Face model.",
-    theme="default"
-)
-
-interface.launch()
+    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()