Update app.py

app.py

@@ -8,10 +8,6 @@ from pytorch_grad_cam import GradCAM
 from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 from pytorch_grad_cam.utils.image import show_cam_on_image
 
-import csv
-import datetime
-import os
-
 # Set device
 device = torch.device("cpu")
 
@@ -34,25 +30,6 @@ transform = transforms.Compose([
                          [0.229, 0.224, 0.225])
 ])
 
-# Logging setup
-log_path = "prediction_logs.csv"
-
-# Create the CSV file with headers if it doesn't exist
-if not os.path.exists(log_path):
-    with open(log_path, mode='w', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(["timestamp", "image_name", "prediction", "confidence"])
-
-def log_prediction(filename, prediction, confidence):
-    timestamp = datetime.datetime.now().isoformat()
-    row = [timestamp, filename, prediction, f"{confidence:.4f}"]
-
-    print("⏺ Logging prediction:", row)
-
-    with open(log_path, mode='a', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(row)
-
 # Prediction function
 def predict_retinopathy(image):
     img = image.convert("RGB").resize((224, 224))
@@ -72,10 +49,6 @@ def predict_retinopathy(image):
     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)
 
-    # Logging
-    filename = getattr(image, "filename", "uploaded_image")
-    log_prediction(filename, label, confidence)
-
     cam_pil = Image.fromarray(cam_image)
     return cam_pil, f"{label} (Confidence: {confidence:.2f})"
 
@@ -88,5 +61,5 @@ gr.Interface(
         gr.Text(label="Prediction")
     ],
     title="Diabetic Retinopathy Detection",
-    description="Upload a retinal image to classify DR and view Grad-CAM heatmap. All predictions are logged for analysis."
+    description="Upload a retinal image to classify DR and view Grad-CAM heatmap."
 ).launch()