Upload tab_classify_image.py

tabs/tab_classify_image.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 13 09:52:28 2026
+
+@author: standarduser
+"""
+
+from gradio_client import Client, handle_file
+import gradio as gr
+
+def predict_from_space(image_path):
+    """Classify image using Space API."""
+    
+    client = Client("ElBeh/image-fake-detector")
+    
+    try:
+        result = client.predict(
+            image=handle_file(image_path),
+            api_name="/predict"
+        )
+        
+        # Gradio Label format: {'label': 'Fake', 'confidences': [{'label': 'Fake', 'confidence': 0.88}, ...]}
+        confidences = result['confidences']
+        
+        # Extract probabilities from confidences list
+        proba_dict = {item['label']: item['confidence'] for item in confidences}
+        proba_real = proba_dict.get('Real', 0.0)
+        proba_fake = proba_dict.get('Fake', 0.0)
+        
+        # Determine prediction
+        prediction = 1 if proba_fake > 0.5 else 0
+        label = "Fake" if prediction == 1 else "Real"
+        confidence = proba_fake if prediction == 1 else proba_real
+        
+        print(f"\nPrediction: {label}")
+        print(f"Confidence: {confidence:.4f} ({confidence*100:.2f}%)")
+        print(f"Real: {proba_real:.4f} | Fake: {proba_fake:.4f}")
+        
+        return {
+            'Real': float(proba_real),
+            'Fake': float(proba_fake)
+        }
+    
+    except Exception as e:
+        print(f"Error: {e}")
+        raise
+
+def create_tab_classify_image(tab_label):
+    with gr.TabItem(tab_label):
+            gr.Interface(
+                fn=predict_from_space,
+                inputs=[
+                gr.Image(type="filepath", label="Upload Image"),  
+            ],
+            outputs=gr.Label(num_top_classes=2, label="Prediction"),
+            title="Image Fake Detector",
+            description="Upload an image to classify it as real or fake. The detector(XGBoost) uses several image statistics to classify the image."
+        )