Update app.py

app.py

@@ -11,19 +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)
 
-# Placeholder image for invalid / non-fundus uploads
-invalid_img = Image.new("RGB", (224, 224), color=(200, 200, 200))
-
-# -----------------------
 # 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))
@@ -42,56 +38,8 @@ transform = transforms.Compose([
                          [0.229, 0.224, 0.225])
 ])
 
-# -----------------------
-# Helper: soft fundus check
-# -----------------------
-def looks_like_fundus(image):
-    """
-    Very lightweight heuristic to guess if an image looks like a retinal fundus scan.
-    NOT a medical-grade classifier – only used to suppress predictions on
-    obviously wrong images.
-    """
-    img = np.array(image.convert("L").resize((224, 224)))
-
-    # Central square vs border
-    center = img[40:184, 40:184]
-
-    border_mask = np.ones_like(img, dtype=bool)
-    border_mask[40:184, 40:184] = False
-    border_pixels = img[border_mask]
-
-    if border_pixels.size == 0:
-        return True
-
-    center_mean = center.mean()
-    border_mean = border_pixels.mean()
-
-    border_dark_ratio = np.mean(border_pixels < 40)
-    center_bright_ratio = np.mean(center > 80)
-
-    cond_contrast = center_mean - border_mean > 15
-    cond_border_dark = border_dark_ratio > 0.3
-    cond_center_bright = center_bright_ratio > 0.25
-
-    return cond_contrast and cond_border_dark and cond_center_bright
-
-
-# -----------------------
 # Predict and save
-# -----------------------
 def predict_retinopathy(image):
-    is_fundus = looks_like_fundus(image)
-
-    # If it's clearly not a fundus image: DO NOT give a DR/NoDR decision
-    if not is_fundus:
-        return (
-            invalid_img,
-            "⚠️ Image not recognised as a retinal fundus scan.\n\n"
-            "No Diabetic Retinopathy assessment has been generated. "
-            "Please upload a valid ophthalmic retinal fundus image."
-        )
-
-    # Normal pipeline for likely fundus images
     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)
@@ -107,10 +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)
 
@@ -120,15 +65,12 @@ def predict_retinopathy(image):
 
     return cam_pil, f"{label} (Confidence: {confidence:.2f})"
 
-
-# -----------------------
 # Gradio app
-# -----------------------
-demo = gr.Interface(
+gr.Interface(
     fn=predict_retinopathy,
     inputs=gr.Image(type="pil", label="Upload Retinal Image"),
     outputs=[
-        gr.Image(type="pil", label="Grad-CAM / Status"),
+        gr.Image(type="pil", label="Grad-CAM Heatmap"),
         gr.Text(label="Prediction")
     ],
     title="OpthaDetect – AI Retinal Screening",
@@ -138,10 +80,6 @@ demo = gr.Interface(
     ),
     article=(
         "⚕️ **OpthaDetect** is an AI-powered ophthalmic decision-support tool. "
-        "It highlights retinal risk regions using Grad-CAM for better clinical interpretability. "
-        "Outputs should always be reviewed alongside clinical judgement."
+        "It highlights retinal risk regions using Grad-CAM for better clinical interpretability."
     )
-)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+).launch()