#Comment for detection section.

app.py

@@ -26,88 +26,88 @@ bucket_result = storage_client.bucket(bucket_name)
 bucket_name_load = "da-ml-models"
 bucket_load = storage_client.bucket(bucket_name_load)
 
-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