Update app.py

app.py

@@ -11,16 +11,15 @@ from pytorch_grad_cam.utils.image import show_cam_on_image
 import os
 import datetime
 
-# -----------------------
 # Setup
-# -----------------------
 device = torch.device("cpu")
 save_dir = "/home/user/app/saved_predictions"
+if not os.path.exists(save_dir):
+    os.makedirs(save_dir)
+    print("📁 Folder created:", save_dir)
 os.makedirs(save_dir, exist_ok=True)
 
-# -----------------------
 # Load model
-# -----------------------
 model = models.resnet50(weights=None)
 model.fc = torch.nn.Linear(model.fc.in_features, 2)
 model.load_state_dict(torch.load("resnet50_dr_classifier.pth", map_location=device))
@@ -39,18 +38,8 @@ transform = transforms.Compose([
                          [0.229, 0.224, 0.225])
 ])
 
-# -----------------------
 # Predict and save
-# -----------------------
 def predict_retinopathy(image):
-    # Validate image first
-    if not looks_like_fundus(image):
-        return (
-            invalid_img,
-            "⚠️ The uploaded image does not appear to be a retinal fundus scan.\n\n"
-            "Please upload a valid ophthalmic retinal image for Diabetic Retinopathy assessment."
-        )
-
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     img = image.convert("RGB").resize((224, 224))
     img_tensor = transform(img).unsqueeze(0).to(device)
@@ -66,10 +55,7 @@ def predict_retinopathy(image):
     # Grad-CAM
     rgb_img_np = np.array(img).astype(np.float32) / 255.0
     rgb_img_np = np.ascontiguousarray(rgb_img_np)
-    grayscale_cam = cam(
-        input_tensor=img_tensor,
-        targets=[ClassifierOutputTarget(pred)]
-    )[0]
+    grayscale_cam = cam(input_tensor=img_tensor, targets=[ClassifierOutputTarget(pred)])[0]
     cam_image = show_cam_on_image(rgb_img_np, grayscale_cam, use_rgb=True)
     cam_pil = Image.fromarray(cam_image)
 
@@ -77,14 +63,9 @@ def predict_retinopathy(image):
     filename = f"{timestamp}_{label}_{confidence:.2f}.png"
     cam_pil.save(os.path.join(save_dir, filename))
 
-    return (
-        cam_pil,
-        f"{label} (Confidence: {confidence:.2f})"
-    )
+    return cam_pil, f"{label} (Confidence: {confidence:.2f})"
 
-# -----------------------
 # Gradio app
-# -----------------------
 gr.Interface(
     fn=predict_retinopathy,
     inputs=gr.Image(type="pil", label="Upload Retinal Image"),
@@ -101,4 +82,4 @@ gr.Interface(
         "⚕️ **OpthaDetect** is an AI-powered ophthalmic decision-support tool. "
         "It highlights retinal risk regions using Grad-CAM for better clinical interpretability."
     )
-).launch()
+).launch()