Fix prediction error

app.py

@@ -27,17 +27,24 @@ def detect_forgery(image):
     img = img / 255.0  # Example normalization; adapt as needed
 
     # Run inference
-    predictions = model.predict(img)[0]  # e.g., [Real_prob, Fake_prob]
+    predictions = model.predict(img)[0]  
+    
+    pred_value = float(predictions)  # or pred_value.item()
     # Suppose predictions = [prob_real, prob_fake]
-    prob_real = predictions[0]
-    prob_fake = predictions[1]
+
 
     # Format output
     # You can return a dictionary or a string. For example:
-    if prob_fake > prob_real:
-        return f"Forged (Fake) with confidence {prob_fake:.2f}"
+    if pred_value >= 0.5:
+        # It's real, use p as the confidence
+        confidence = pred_value
+        label = "REAL"
     else:
-        return f"Real with confidence {prob_real:.2f}"
+        # It's fake, confidence is (1 - p)
+        confidence = 1.0 - pred_value
+        label = "FAKE"
+
+    return f"{label} with confidence {confidence:.2f}"
 
 # 3. Build your Gradio interface
 demo = gr.Interface(
@@ -50,4 +57,5 @@ demo = gr.Interface(
 
 # 4. Launch the app (Gradio handles the rest)
 if __name__ == "__main__":
+    _ = model.predict(np.zeros((1, 160, 160, 3), dtype=np.float32))
     demo.launch()