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app.py

@@ -1,77 +1,41 @@
 import gradio as gr
 from transformers import pipeline
 
-# 🔹 Load model once and cache it
-# This runs only at startup, not on every request
-print("🔄 Loading AI detection model...")
-pipe = pipeline("text-classification", model="roberta-base-openai-detector")
-print("✅ Model loaded successfully!")
+# Load state-of-the-art AI text detector
+text_pipe = pipeline("text-classification", model="wangkevin02/AI_Detect_Model")
 
-def detect_text(text: str):
-    """Detect whether the text is AI-generated or Human-written."""
-    if not text.strip():
+def detect_text(input_text: str):
+    if not input_text.strip():
         return {}, "❌ Please enter some text."
 
     try:
-        results = pipe(text)
-
-        # Convert results into a probability dictionary
-        probs = {r["label"]: float(r["score"]) for r in results}
-
-        # Get label with highest probability
-        verdict = str(max(probs, key=probs.get))
-
-        # Make verdict more user-friendly
-        if verdict.upper() in ["LABEL_0", "FAKE", "AI"]:
-            verdict_message = "🤖 This looks AI-generated"
-        elif verdict.upper() in ["LABEL_1", "REAL", "HUMAN"]:
-            verdict_message = "📝 This looks Human-written"
-        else:
-            verdict_message = f"⚠️ Unknown verdict: {verdict}"
-
-        return probs, verdict_message
-
+        outputs = text_pipe(input_text)
+        # Outputs look like: [{'label':'0'/'1','score':...}]
+        label = outputs[0]["label"]
+        score = float(outputs[0]["score"])
+        
+        prob = {"Human-written": 1 - score, "AI-generated": score}
+        verdict = "🤖 This looks AI-generated" if label == "1" else "📝 This looks Human-written"
+        return prob, verdict
     except Exception as e:
         return {}, f"❌ Error: {str(e)}"
 
-
 with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        # 🕵️ AI Content Detector  
-        Paste some text below and check if it's **AI-generated or Human-written**.
-        """
-    )
+    gr.Markdown("#  AI Content Detector (Upgraded)\nFaster and more reliable detection using an advanced classifier.")
 
     with gr.Row():
-        text_input = gr.Textbox(
-            label="✍️ Input Text",
-            lines=10,
-            placeholder="Paste text here..."
-        )
+        text_input = gr.Textbox(lines=10, placeholder="Paste text here...", label="Enter Text to Analyze")
 
     with gr.Row():
-        output_probs = gr.Label(label="📊 Probabilities")
-        output_verdict = gr.Textbox(label="✅ Verdict", interactive=False)
+        output_probs = gr.Label(label="Probabilities")
+        output_verdict = gr.Textbox(label="Verdict", interactive=False)
 
     with gr.Row():
-        submit_btn = gr.Button("🔎 Submit", variant="primary")
-        clear_btn = gr.Button("🧹 Clear")
-
-    # Submit action
-    submit_btn.click(
-        fn=detect_text,
-        inputs=text_input,
-        outputs=[output_probs, output_verdict],
-    )
+        submit_btn = gr.Button("Check Text", variant="primary")
+        clear_btn = gr.Button("Clear")
 
-    # Clear action
-    clear_btn.click(
-        fn=lambda: ("", {}, ""),
-        inputs=[],
-        outputs=[text_input, output_probs, output_verdict],
-    )
+    submit_btn.click(detect_text, inputs=text_input, outputs=[output_probs, output_verdict])
+    clear_btn.click(lambda: ("", {}, ""), inputs=[], outputs=[text_input, output_probs, output_verdict])
 
-# 🔹 Launch app
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -3,3 +3,4 @@ transformers==4.43.3
 torch>=2.0.0
 accelerate>=0.33.0
 safetensors>=0.4.2
+pillow>=10.0.0