Update app.py

app.py

@@ -1,21 +1,21 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from datasets import load_dataset
 
-# 1. Load model & tokenizer
-MODEL = "microsoft/deberta-v3-small"  # you can fine-tune later on BiScope_Data
+# Load your fine-tuned model from HF Hub
+MODEL = "Yagnik12/biscope-detector"  # ✅ use fine-tuned model
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
 
-# 2. Build pipeline
 detector = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True)
 
-# 3. Detection function
+# Load BiScope dataset (for demo examples)
+biscope = load_dataset("HanxiGuo/BiScope_Data", split="test[:20]")
+
 def detect_ai(text):
     results = detector(text)[0]
-    # Assuming label 0 = Human, 1 = AI
     human_score = [r["score"] for r in results if r["label"] in ["LABEL_0", "0"]][0]
     ai_score = [r["score"] for r in results if r["label"] in ["LABEL_1", "1"]][0]
-
     prediction = "🧑 Human" if human_score > ai_score else "🤖 AI"
     return {
         "Prediction": prediction,
@@ -23,15 +23,17 @@ def detect_ai(text):
         "AI Probability": round(ai_score * 100, 2)
     }
 
-# 4. Gradio UI
-demo = gr.Interface(
-    fn=detect_ai,
-    inputs=gr.Textbox(lines=5, placeholder="Enter text here..."),
-    outputs="json",
-    title="AI vs Human Text Detector",
-    description="Demo AI text detection using Hugging Face Transformers.\n"
-                "Trained/Fine-tuned models can be swapped in for better accuracy on BiScope_Data."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# AI vs Human Text Detector (BiScope)")
+    
+    with gr.Row():
+        inp = gr.Textbox(lines=5, placeholder="Enter text here...")
+        out = gr.JSON()
+    btn = gr.Button("Detect")
+    btn.click(fn=detect_ai, inputs=inp, outputs=out)
+
+    gr.Markdown("### 🔎 Try BiScope Dataset Examples")
+    examples = [biscope[i]["text"] for i in range(len(biscope))]
+    gr.Examples(examples=examples, inputs=inp)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()