Update app.py

app.py

@@ -11,18 +11,21 @@ model = load_model(model_path)
 
 # Inference function
 def predict(image):
-    image = image.resize((299, 299))
-    image = img_to_array(image)
-    image = np.expand_dims(image, axis=0)
-    image = image / 255.0  # normalize to match training
-
+    # === Short-Term Workaround for Inference ===
+    image = image.convert("RGB")                      # Ensure 3 color channels
+    image = image.resize((299, 299))                  # Resize to match model input
+    image = img_to_array(image).astype("float32")     # Convert to array + ensure float32
+    image = np.expand_dims(image, axis=0)             # Add batch dimension
+    image /= 255.0                                     # Normalize to [0, 1] like during training
+
+    # Model prediction
     prob = model.predict(image)[0][0]
 
-    # Based on your notebook: label 0 = Fake, label 1 = Real
-    label = "Real" if prob > 0.5 else "Fake"
-    confidence = round(float(prob if prob > 0.5 else 1 - prob), 3)
+    # 0 = Fake, 1 = Real
+    label = "Fake" if prob < 0.5 else "Real"
+    confidence = round(float(1 - prob if label == "Fake" else prob), 3)
 
-    return f"{label} ({confidence})"
+    return f"{label} ({confidence * 100:.1f}%)"
 
 # Gradio interface
 iface = gr.Interface(