Update app.py

app.py

@@ -8,25 +8,28 @@ 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
+import datetime
+import sqlite3
 
-# Set device
+# === Setup paths and model ===
 device = torch.device("cpu")
+ADMIN_KEY = "Diabetes_Detection"
+image_folder = "collected_images"
+os.makedirs(image_folder, exist_ok=True)
 
-# Load model
+# === 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))
 model.to(device)
 model.eval()
 
-# Grad-CAM setup
+# === Grad-CAM setup ===
 target_layer = model.layer4[-1]
 cam = GradCAM(model=model, target_layers=[target_layer])
 
-# Image preprocessing
+# === Image transform ===
 transform = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.ToTensor(),
@@ -34,21 +37,35 @@ transform = transforms.Compose([
                          [0.229, 0.224, 0.225])
 ])
 
-# Logging setup
-log_path = "prediction_logs.csv"
-
-def log_prediction(filename, prediction, confidence):
-    timestamp = datetime.datetime.now().isoformat()
-    row = [timestamp, filename, prediction, f"{confidence:.4f}"]
-
-    print("⏺ Logging prediction:", row)  # 🔍 Add this line
-
-    with open(log_path, mode='a', newline='') as file:
-        writer = csv.writer(file)
-        writer.writerow(row)
-
-# Prediction function
+# === SQLite setup ===
+def init_db():
+    conn = sqlite3.connect("logs.db")
+    cursor = conn.cursor()
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS predictions (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            timestamp TEXT,
+            filename TEXT,
+            prediction TEXT,
+            confidence REAL
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+def log_to_db(timestamp, filename, prediction, confidence):
+    conn = sqlite3.connect("logs.db")
+    cursor = conn.cursor()
+    cursor.execute("INSERT INTO predictions (timestamp, filename, prediction, confidence) VALUES (?, ?, ?, ?)",
+                   (timestamp, filename, prediction, confidence))
+    conn.commit()
+    conn.close()
+
+init_db()  # ✅ Initialize table
+
+# === Prediction Function ===
 def predict_retinopathy(image):
+    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)
 
@@ -65,22 +82,32 @@ def predict_retinopathy(image):
     rgb_img_np = np.ascontiguousarray(rgb_img_np)
     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)
 
-    # Logging
-    filename = getattr(image, "filename", "uploaded_image")
-    log_prediction(filename, label, confidence)
+    # Save image and log
+    filename = f"{timestamp}_{label.replace(' ', '_')}.png"
+    image_path = os.path.join(image_folder, filename)
+    image.save(image_path)
+
+    log_to_db(timestamp, image_path, label, confidence)
 
-    cam_pil = Image.fromarray(cam_image)
     return cam_pil, f"{label} (Confidence: {confidence:.2f})"
 
-# Gradio interface
-gr.Interface(
-    fn=predict_retinopathy,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Image(type="pil", label="Grad-CAM"),
-        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."
-).launch()
+# === Gradio Interface ===
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 DR Detection with Grad-CAM + SQLite Logging")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload Retinal Image")
+        cam_output = gr.Image(type="pil", label="Grad-CAM")
+
+    prediction_output = gr.Text(label="Prediction")
+    run_button = gr.Button("Submit")
+
+    run_button.click(
+        fn=predict_retinopathy,
+        inputs=image_input,
+        outputs=[cam_output, prediction_output]
+    )
+
+demo.launch()