update with fixes

app.py

@@ -1,40 +1,77 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load zero-shot classification pipeline
-classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
-
-# Labels to classify as AI-written or Human-written
-labels = ["AI-generated text", "Human-written text"]
-
-def detect_ai_content(text):
-    result = classifier(text, labels)
-    scores = dict(zip(result["labels"], result["scores"]))
-    ai_score = scores["AI-generated text"]
-    human_score = scores["Human-written text"]
-    
-    if ai_score > human_score:
-        verdict = "⚠️ This text looks AI-Generated"
-    else:
-        verdict = "✅ This text looks Human-Written"
-
-    return {
-        "AI Probability": f"{ai_score:.2%}",
-        "Human Probability": f"{human_score:.2%}",
-        "Verdict": verdict
-    }
-
-# Gradio Interface
-demo = gr.Interface(
-    fn=detect_ai_content,
-    inputs=gr.Textbox(lines=10, placeholder="Paste text here..."),
-    outputs=[
-        gr.Label(num_top_classes=2, label="Probabilities"),
-        gr.Textbox(label="Verdict")
-    ],
-    title="AI Content Detector",
-    description="Detect whether the given text is AI-generated or Human-written."
-)
+# 🔹 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!")
 
+def detect_text(text: str):
+    """Detect whether the text is AI-generated or Human-written."""
+    if not 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
+
+    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**.
+        """
+    )
+
+    with gr.Row():
+        text_input = gr.Textbox(
+            label="✍️ Input Text",
+            lines=10,
+            placeholder="Paste text here..."
+        )
+
+    with gr.Row():
+        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],
+    )
+
+    # Clear action
+    clear_btn.click(
+        fn=lambda: ("", {}, ""),
+        inputs=[],
+        outputs=[text_input, output_probs, output_verdict],
+    )
+
+# 🔹 Launch app
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1,3 +1,5 @@
-gradio
-transformers
-torch
+gradio==4.36.1
+transformers==4.43.3
+torch>=2.0.0
+accelerate>=0.33.0
+safetensors>=0.4.2