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import streamlit as st
import pandas as pd
import numpy as np
import os, zipfile, shutil
from utils.image_classification import classifier
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay,accuracy_score,classification_report
import matplotlib.pyplot as plt
import splitfolders
from io import StringIO
import sys
# import tensorflow as tf
# from tensorflow.keras.preprocessing.image import ImageDataGenerator
# from tensorflow.keras.applications import MobileNetV2
# from tensorflow.keras.layers import Input, GlobalAveragePooling2D, Dense
# from tensorflow.keras.models import Model
# from sklearn.metrics import classification_report, confusion_matrix
# import matplotlib.pyplot as plt
# from io import BytesIO
# import numpy as np
# from PIL import Image
c=classifier()
def delete_directory(directory_path):
try:
shutil.rmtree(directory_path)
print(f"Directory '{directory_path}' successfully deleted.")
except Exception as e:
print(f"Error deleting directory '{directory_path}': {e}")
# Example usage:
# st.subheader('This is a subheader with a divider', divider='blue')
st.title('Image Classification Model Training')
st.image('elements/image_banner.jpeg')
# Page title
st.title("Custom Image Classification Trainer")
st.sidebar.subheader('Folder Format')
st.sidebar.code('''custom_dataset/
├── train/
│ ├── class1/
│ ├── class2/
│ ├── ...
├── val/
│ ├── class1/
│ ├── class2/
│ ├── ...
├── test/
│ ├── class1/
│ ├── class2/
│ ├── ...
''')
# Upload image dataset
st.header("1. Upload Image Dataset")
uploaded_file = st.file_uploader("Choose a ZIP file containing your image dataset", type=["zip"])
dataset_path = 'datasets'
split_path = 'split_dir'
agree = st.checkbox('Select if your data is not splited in given format')
if uploaded_file:
if st.button('Extract Data'):
if not agree:
with st.spinner("Extracting dataset..."):
# You should write code here to extract and prepare the dataset.
file_name = uploaded_file.name
# st.write(f"Uploaded file name: {file_name.split('.')[0]}")
extract_dir = dataset_path
os.makedirs(extract_dir, exist_ok=True)
# Extract the zip file
with zipfile.ZipFile(uploaded_file, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
st.success("Dataset extraction complete!")
else:
with st.spinner("Extracting dataset..."):
# You should write code here to extract and prepare the dataset.
file_name = uploaded_file.name
# st.write(f"Uploaded file name: {file_name.split('.')[0]}")
extract_dir = split_path
os.makedirs(extract_dir, exist_ok=True)
# Extract the zip file
with zipfile.ZipFile(uploaded_file, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
splitfolders.ratio(extract_dir,seed=1337, output=dataset_path, ratio=(0.8, 0.1, 0.1))
directory_to_delete = extract_dir
delete_directory(directory_to_delete)
st.success("Dataset extraction complete!")
# Sidebar to select model and other options
st.header("2. Select Model and Options")
model_name = st.selectbox("Select a pre-trained model:", ['EfficientNet_B0','EfficientNet_B1','MnasNet0_5','MnasNet0_75','MnasNet1_0','MobileNet_v2',
'MobileNet_v3_small','MobileNet_v3_large','RegNet_y_400mf','ShuffleNet_v2_x0_5','ShuffleNet_v2_x1_0','ShuffleNet_v2_x1_5',
'SqueezeNet 1_0','SqueezeNet 1_1'])
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 st.button('jjjj'):
# if uploaded_file==True:
# st.text('fine')
# else:
# st.success('Please Upload Data First')
# Training and Evaluation
# if uploaded_file:
st.header("3. Choose Model and Train")
if st.button("Train Model"):
if uploaded_file:
with st.spinner("Processing Data..."):
c.data_loader(dataset_path,batch_size)
with st.spinner("Training model..."):
# captured_output = StringIO()
# sys.stdout = captured_output
model = c.train_model(model_name,epochs)
# sys.stdout = sys.__stdout__
# # Display captured output in Streamlit
# st.sidebar.code("Printed output:")
# st.sidebar.code(captured_output.getvalue())
# 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. Evaluation')
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))
y_true, y_pred, y_pred_prob=c.pytorch_predict(model)
# st.text([y_true, y_pred])
col1, col2 = st.columns([3,2])
col1.subheader("Classification Report")
# col1.text(classification_report(y_true, y_pred))
report_dict = classification_report(y_true, y_pred, target_names=c.class_names,output_dict=True)
accuracy = report_dict['accuracy']
del report_dict['accuracy']
df = pd.DataFrame(report_dict).transpose()
col1.text(f'Accuracy of the Model: {round(accuracy,1)}')
col1.dataframe(df)
col2.subheader("Confusion Matrix")
cm = confusion_matrix(y_true, y_pred)
fig = ConfusionMatrixDisplay(cm, display_labels=c.class_names).plot(cmap='Blues')
plt.savefig('elements/confusion_matrix.png')
# fig_array = np.array(fig.figure_.canvas.renderer.buffer_rgba())
col2.image('elements/confusion_matrix.png')
# 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)
with open('model.pt', "rb") as model_file:
st.download_button(
label="Download Model",
data=model_file,
file_name=f"model_{model_name}.pt",
key="model_download",
)
st.balloons()
else:
st.warning("Please Upload Data First")
# 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))
# y_true, y_pred, y_pred_prob=c.pytorch_predict()
# st.text(y_true, y_pred)
# 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.
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