Update pages/🎈_object_detection.py

pages/🎈_object_detection.py

@@ -8,97 +8,3 @@ st.image('elements/object_banner.png')
 
 st.image('elements/commingsoon.gif',use_column_width=True)
 
-# st.sidebar.subheader('Folder Format')
-# st.sidebar.subheader(':red[(Data should be in yolo format)]')
-
-# st.sidebar.code('''custom_dataset/
-# ├── train/
-# │   ├── image/
-# │   ├── label/
-# │   ├── ...
-# ├── val/
-# │   ├── image/
-# │   ├── label/
-# │   ├── ...
-# ''')
-
-# from io import StringIO
-# import sys
-
-# # Redirect stdout to capture print statements
-# captured_output = StringIO()
-# sys.stdout = captured_output
-
-# from print import my_function
-
-# # Run the function to capture the output
-# if st.button('print'):
-#     my_function()
-
-# # Reset stdout to its original state
-#     sys.stdout = sys.__stdout__
-
-#     # Display captured output in Streamlit
-#     st.sidebar.code("Printed output:")
-#     st.sidebar.code(captured_output.getvalue())
-
-# st.header("1. Upload Image Dataset")
-# uploaded_file = st.file_uploader("Choose a ZIP file containing your image dataset", type=["zip"])
-# dataset_path = True
-
-# # Sidebar to select model and other options
-# st.header("2. Select Model and Options")
-# model_name = st.selectbox("Select a pre-trained model:", ["MobileNetV2", "ResNet50", "InceptionV3"])
-# epochs = st.slider("Number of Epochs", min_value=1, max_value=50, value=10)
-# batch_size = st.slider("Batch Size", min_value=1, max_value=32, value=8)
-
-
-# if uploaded_file:
-#     with st.spinner("Extracting dataset..."):
-#         # You should write code here to extract and prepare the dataset.
-
-#         # For example:
-#         # dataset_path = extract_dataset(uploaded_file)
-#         dataset_path = 'jjjj'
-
-#         st.success("Dataset extraction complete!")
-
-# # Training and Evaluation
-# if dataset_path:
-#     st.header("3. Choose Model and Train")
-#     if st.button("Train Model"):
-#         with st.spinner("Training model..."):
-#             # You should write code here to load the dataset, build the selected model, train it, and save the model.
-
-#             # For example:
-#             # model = build_model(model_name)
-#             # train_model(model, dataset_path, epochs, batch_size)
-#             # model.save("custom_classification_model.h5")
-
-#             st.success("Training complete!")
-
-#     st.header("4. Evaluate Model")
-#     if st.button("Evaluate Model"):
-#         with st.spinner("Evaluating model..."):
-#             # You should write code here to load the trained model, evaluate its performance, and display metrics.
-
-#             # For example:
-#             # trained_model = tf.keras.models.load_model("custom_classification_model.h5")
-#             # test_data, test_labels = load_test_data(dataset_path)
-#             # predictions = trained_model.predict(test_data)
-#             # report = classification_report(np.argmax(test_labels, axis=1), np.argmax(predictions, axis=1))
-#             # confusion = confusion_matrix(np.argmax(test_labels, axis=1), np.argmax(predictions, axis=1))
-
-#             st.text("Classification Report:")
-#             # st.text(report)
-#             df = pd.DataFrame(np.random.randn(2, 2), columns=("col %d" % i for i in range(2)))
-
-#             st.table(df)
-
-#             st.text("Confusion Matrix:")
-#             # st.write(confusion)
-#             df = pd.DataFrame(np.random.randn(2, 2), columns=("col %d" % i for i in range(2)))
-
-#             st.table(df)
-
-# # Helper functions for dataset extraction, model building, and training can be defined separately.