updated

app.py

@@ -45,27 +45,6 @@ print("Models loaded successfully!")
 
 
 
-# ============================================================================
-# AI Detection
-# ============================================================================
-def predict_ai_content(text):
-    if not text or not text.strip():
-        return "No input provided", 0.0
-
-    try:
-        result = ai_detector_pipe(text)
-        if isinstance(result, list) and len(result) > 0:
-            res = result[0]
-            ai_content_label = res.get('label', 'Unknown')
-            ai_content_score = round(float(res.get('score', 0)) * 100, 2)
-            return ai_content_label, ai_content_score
-        else:
-            return "Invalid response", 0.0
-    except Exception as e:
-        print(f"Error in prediction: {e}")
-        return "Error", 0.0
-
-
 # ============================================================================
 # STAGE 1: PARAPHRASING WITH T5 MODEL
 # ============================================================================
@@ -358,11 +337,34 @@ def calculate_similarity(text1: str, text2: str) -> float:
         similarity = float(np.dot(embeddings[0], embeddings[1]) / (
             np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])
         ))
+        similarity = round(similarity*100, 2)
         return similarity
     except Exception as e:
         logger.error(f"Similarity calculation failed: {e}")
         return 0.0
 
+
+# ============================================================================
+# AI Detection
+# ============================================================================
+def predict_ai_content(text):
+    if not text or not text.strip():
+        return "No input provided", 0.0
+
+    try:
+        result = ai_detector_pipe(text)
+        if isinstance(result, list) and len(result) > 0:
+            res = result[0]
+            ai_content_label = res.get('label', 'Unknown')
+            ai_content_score = round(float(res.get('score', 0)) * 100, 2)
+            return ai_content_label, ai_content_score
+        else:
+            return "Invalid response", 0.0
+    except Exception as e:
+        print(f"Error in prediction: {e}")
+        return "Error", 0.0
+
+
 # ============================================================================
 # MAIN HUMANIZER FUNCTION
 # ============================================================================
@@ -487,7 +489,7 @@ def create_gradio_interface():
                     gr.Markdown("### Semantic Similarity & Status")
                 
                 with gr.Row():
-                    similarity_output = gr.Number(label="Similarity Score", precision=4)
+                    similarity_output = gr.Number(label="Content Similarity (%)", precision=2)
                     status_output = gr.Textbox(label="Status",interactive=False,lines=2, max_lines=10)
                 
                 with gr.Row():