Update app.py

app.py

@@ -47,29 +47,30 @@ async def predict(image: UploadFile = File(...)):
     
     image_data = await image.read()
     pil_img = Image.open(io.BytesIO(image_data)).convert("RGB")
-    profile_img = profile_image_for_cnn_predict(pil_img, crop_size=512)
-    if isinstance(profile_img, str):
-            return (f"Error processing image: {profile_img}")
+    
+    predictions=[]
+    cnnPred = CNNPredict(pil_img)
+    if isinstance(cnnPred, str):
+        # An error occurred during CNN prediction
+        print("CNN preprocessing error:", cnnPred)
+        predictions.append(PredictionEntry(model="CNN", error=cnnPred, predicted_class=-1, confidence=0.0))
     else:
-        print(f"Profile image shape: {profile_img.shape}")
-        cnnPred = CNNPredict(profile_img)
-        resnetPred = ResnetPredict(profile_img)
-        clipPred = clip_predict(pil_img, crop_size=512)
-        #print(f"CNN Prediction (Real prob): {cnnPred:.4f}")
-        #print(f"ResNet Prediction (Real prob): {resnetPred:.4f}")
-        #print(f"CLIP Prediction (AI prob): {clipPred:.4f}")
-        resnet_class = 1 if resnetPred >= 0.5 else 0
         cnn_class = 1 if cnnPred >= 0.5 else 0
-        clip_class = 0 if clipPred > 0.5 else 1
-        resnet_conf = resnetPred if resnetPred >= 0.5 else 1 - resnetPred
         cnn_conf = cnnPred if cnnPred >= 0.5 else 1 - cnnPred
-        clip_conf = clipPred if clipPred > 0.5 else 1 - clipPred
-        #Predicted classes 1 is Real, 0 is AI
-        predictions = [
-            PredictionEntry(model="CNN", predicted_class=cnn_class, confidence=round(float(cnn_conf), 4)),
-            PredictionEntry(model="ResNet", predicted_class=resnet_class, confidence=round(float(resnet_conf), 4)),
-            PredictionEntry(model="CLIP", predicted_class=clip_class, confidence=round(float(clip_conf), 4)),
-        ]
+        predictions.append(  PredictionEntry(model="CNN", predicted_class=cnn_class, confidence=round(float(cnn_conf), 4)))
+    clipPred = CLIPPredict(pil_img)
+    if isinstance(clipPred, str):
+        # An error occurred during CLIP prediction
+        print("CLIP error:", clipPred)
+        predictions.append(PredictionEntry(model="CLIP", error=clipPred, predicted_class=-1, confidence=0.0))
+    else:
+        clip_class = 1 if clipPred > 0.5 else 0
+        clip_conf = clipPred if clipPred >= 0.5 else 1 - clipPred
+        predictions.append(  PredictionEntry(model="CLIP", predicted_class=clip_class, confidence=round(float(clip_conf), 4)))
+    #print(f"CNN Prediction (AI prob): {cnnPred:.4f}")
+    #print(f"ResNet Prediction (AI prob): {resnetPred:.4f}")
+    #print(f"CLIP Prediction (AI prob): {clipPred:.4f}")
+    #Predicted classes 1 is Real, 0 is AI
     return ImagePredictionResponse(predictions=predictions)
 
 @app.post(