Update app.py

app.py

@@ -6,16 +6,15 @@ import pandas as pd
 import gradio as gr
 
 # ----------------------------------------------------
-# LOAD CAUSAL LM (Memory-safe model)
+# LOAD CAUSAL LM (distilGPT2 = FAST + LIGHT)
 # ----------------------------------------------------
-MODEL_NAME = "EleutherAI/gpt-neo-2.7B"
+MODEL_NAME = "distilgpt2"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.float16 if device.type == "cuda" else torch.float32
+    MODEL_NAME
 ).to(device).eval()
 
 
@@ -29,17 +28,17 @@ def sentence_split(text):
 
 
 # ----------------------------------------------------
-# PERPLEXITY FUNCTION
+# PERPLEXITY FUNCTION (very fast on distilgpt2)
 # ----------------------------------------------------
 def perplexity(sentence):
     inputs = tokenizer(sentence, return_tensors="pt").to(device)
     with torch.no_grad():
         out = model(**inputs, labels=inputs["input_ids"])
-    return torch.exp(out.loss).item()
+    return float(torch.exp(out.loss))
 
 
 # ----------------------------------------------------
-# SIMPLE TEXT PERTURBATION
+# SIMPLE TEXT PERTURBATION (DetectGPT trick)
 # ----------------------------------------------------
 def perturb(text):
     words = text.split()
@@ -65,12 +64,13 @@ def detectgpt_score(sentence, perturbations=5):
         try:
             pert_scores.append(perplexity(p))
         except:
-            pass
+            continue
 
     if not pert_scores:
         return 0
 
-    return np.mean(pert_scores) - base
+    # DetectGPT signal
+    return float(np.mean(pert_scores) - base)
 
 
 # ----------------------------------------------------
@@ -90,13 +90,19 @@ def classify_text(text):
         scores.append(score)
 
         label = "AI" if score > 0 else "Human"
+
         results.append([s, label, f"{abs(score):.4f}"])
 
         if label == "AI":
-            highlighted.append(f"<p style='color:red;font-weight:bold'>{s}</p>")
+            highlighted.append(
+                f"<p style='color:red;font-weight:bold'>{s}</p>"
+            )
         else:
-            highlighted.append(f"<p style='color:green;font-weight:bold'>{s}</p>")
+            highlighted.append(
+                f"<p style='color:green;font-weight:bold'>{s}</p>"
+            )
 
+    # Document-level score (Turnitin-like scaling)
     avg = np.mean(scores)
     doc_ai = max(0, min(100, (avg + 1) * 50))
 
@@ -110,7 +116,7 @@ def classify_text(text):
 # GRADIO UI
 # ----------------------------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 Writenix DetectGPT (GPT-Neo 2.7B Version)")
+    gr.Markdown("## 🧠 Writenix DetectGPT (distilgpt2 — Ultra Fast Version)")
 
     text_input = gr.Textbox(
         label="Enter text",