#Uncomment detection section.

app.py

@@ -33,88 +33,88 @@ enhancement_type = st.sidebar.selectbox(
     ["Invert", "High Pass Filter", "Unsharp Masking", "Histogram Equalization", "CLAHE"]
 )
 
-# H = 224
-# W = 224
-
-# @st.cache_resource
-# def load_model():
-#     model = tf.keras.models.load_model("model-detection.h5", compile=False)
-#     model.compile(
-#         loss={
-#             "bbox": "mse",
-#             "class": "sparse_categorical_crossentropy"
-#         },
-#         optimizer=tf.keras.optimizers.Adam(),
-#         metrics={
-#             "bbox": ['mse'],
-#             "class": ['accuracy']
-#         }
-#     )
-#     return model
-
-# def preprocess_image(image):
-#     """ Preprocess the image to the required size and normalization. """
-#     image = cv2.resize(image, (W, H))
-#     image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
-#     image = np.expand_dims(image, axis=0).astype(np.float32)
-#     return image
-
-# def predict(model, image):
-#     """ Predict bounding box and label for the input image. """
-#     pred_bbox, pred_class = model.predict(image)
-#     pred_label_confidence = np.max(pred_class, axis=1)[0]
-#     pred_label = np.argmax(pred_class, axis=1)[0]
-#     return pred_bbox[0], pred_label, pred_label_confidence
-
-# def draw_bbox(image, bbox):
-#     """ Draw bounding box on the image. """
-#     h, w, _ = image.shape
-#     x1, y1, x2, y2 = bbox
-#     x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h)
-#     image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-#     return image
-
-# # st.title("Chest X-ray Disease Detection")
-
-# # st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.")
-
-# model = load_model()
-
-# uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-
-# if uploaded_file is not None:
-#     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-#     image = cv2.imdecode(file_bytes, 1)
-
-#     st.image(image, caption='Uploaded Image.', use_column_width=True)
-
-#     if st.button('Detect'):
-#         st.write("Processing...")
-#         input_image = preprocess_image(image)
-#         pred_bbox, pred_label, pred_label_confidence = predict(model, input_image)
-
-#         # Updated label mapping based on the dataset
-#         label_mapping = {
-#             0: 'Atelectasis',
-#             1: 'Cardiomegaly',
-#             2: 'Effusion',
-#             3: 'Infiltrate',
-#             4: 'Mass',
-#             5: 'Nodule',
-#             6: 'Pneumonia',
-#             7: 'Pneumothorax'
-#         }
-
-#         if pred_label_confidence < 0.2:
-#             st.write("May not detect a disease.")
-#         else:
-#             pred_label_name = label_mapping[pred_label]
-#             st.write(f"Prediction Label: {pred_label_name}")
-#             st.write(f"Prediction Bounding Box: {pred_bbox}")
-#             st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
-
-#             output_image = draw_bbox(image.copy(), pred_bbox)
-#             st.image(output_image, caption='Detected Image.', use_column_width=True)
+H = 224
+W = 224
+
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model("model-detection.h5", compile=False)
+    model.compile(
+        loss={
+            "bbox": "mse",
+            "class": "sparse_categorical_crossentropy"
+        },
+        optimizer=tf.keras.optimizers.Adam(),
+        metrics={
+            "bbox": ['mse'],
+            "class": ['accuracy']
+        }
+    )
+    return model
+
+def preprocess_image(image):
+    """ Preprocess the image to the required size and normalization. """
+    image = cv2.resize(image, (W, H))
+    image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+    return image
+
+def predict(model, image):
+    """ Predict bounding box and label for the input image. """
+    pred_bbox, pred_class = model.predict(image)
+    pred_label_confidence = np.max(pred_class, axis=1)[0]
+    pred_label = np.argmax(pred_class, axis=1)[0]
+    return pred_bbox[0], pred_label, pred_label_confidence
+
+def draw_bbox(image, bbox):
+    """ Draw bounding box on the image. """
+    h, w, _ = image.shape
+    x1, y1, x2, y2 = bbox
+    x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h)
+    image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+    return image
+
+# st.title("Chest X-ray Disease Detection")
+
+# st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.")
+
+model = load_model()
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, 1)
+
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+    if st.button('Detect'):
+        st.write("Processing...")
+        input_image = preprocess_image(image)
+        pred_bbox, pred_label, pred_label_confidence = predict(model, input_image)
+
+        # Updated label mapping based on the dataset
+        label_mapping = {
+            0: 'Atelectasis',
+            1: 'Cardiomegaly',
+            2: 'Effusion',
+            3: 'Infiltrate',
+            4: 'Mass',
+            5: 'Nodule',
+            6: 'Pneumonia',
+            7: 'Pneumothorax'
+        }
+
+        if pred_label_confidence < 0.2:
+            st.write("May not detect a disease.")
+        else:
+            pred_label_name = label_mapping[pred_label]
+            st.write(f"Prediction Label: {pred_label_name}")
+            st.write(f"Prediction Bounding Box: {pred_bbox}")
+            st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
+
+            output_image = draw_bbox(image.copy(), pred_bbox)
+            st.image(output_image, caption='Detected Image.', use_column_width=True)
     
 
 # Utility Functions