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| # streamlit_app.py | |
| import streamlit as st | |
| from fastai.vision.all import * | |
| import shutil | |
| import os | |
| # Function to get the label from the file name | |
| def get_label(file_name): | |
| return file_name.split('-')[0] | |
| # Function to prepare data (similar to your code) | |
| def prepare_data(food_path, label_a, label_b): | |
| for img in get_image_files(food_path): | |
| if label_a in str(img): | |
| img.rename(f"{img.parent}/{label_a}-{img.name}") | |
| elif label_b in str(img): | |
| img.rename(f"{img.parent}/{label_b}-{img.name}") | |
| else: | |
| os.remove(img) | |
| # Function to train the model | |
| def train_model(food_path, label_func): | |
| dls = ImageDataLoaders.from_name_func( | |
| food_path, get_image_files(food_path), valid_pct=0.2, seed=420, | |
| label_func=label_func, item_tfms=Resize(230) | |
| ) | |
| learn = cnn_learner(dls, resnet34, metrics=error_rate, pretrained=True) | |
| learn.fine_tune(epochs=1) | |
| return learn | |
| # ... (previous code) | |
| # ... (previous code) | |
| # Streamlit app | |
| def main(): | |
| st.title("Food Classifier Streamlit App") | |
| # Sidebar options | |
| options = ["Train Model", "Upload Image", "Test Random Images", "Confusion Matrix"] | |
| choice = st.sidebar.selectbox("Choose an option", options) | |
| if choice == "Train Model": | |
| st.subheader("Training the Model") | |
| food_path = Path("~/.fastai/data/food-101/food-101").expanduser() | |
| if not food_path.exists(): | |
| try: | |
| food_path = untar_data(URLs.FOOD) | |
| except FileExistsError: | |
| st.warning("Data directory already exists. Skipping download.") | |
| label_a = st.text_input("Enter label A:", "samosa") | |
| label_b = st.text_input("Enter label B:", "hot_and_sour_soup") | |
| prepare_data(food_path, label_a, label_b) | |
| learn = train_model(food_path, get_label) | |
| st.session_state.model = learn # Save the model to session state | |
| st.success("Model trained successfully!") | |
| # ... (rest of the code remains unchanged) | |
| elif choice == "Upload Image": | |
| st.subheader("Upload Your Own Images") | |
| if "model" not in st.session_state: | |
| st.warning("Please train the model first.") | |
| else: | |
| uploaded_files = st.file_uploader("Choose images", type=["jpg", "jpeg", "png"], accept_multiple_files=True) | |
| if uploaded_files: | |
| for img in uploaded_files: | |
| img = PILImage.create(img) | |
| label, _, probs = st.session_state.model.predict(img) | |
| st.image(img, caption=f"This is a {label}.") | |
| st.write(f"{label_a}: {probs[1].item():.6f}") | |
| st.write(f"{label_b}: {probs[0].item():.6f}") | |
| if uploaded_files: | |
| for img in uploaded_files: | |
| img = PILImage.create(img) | |
| label, _, probs = st.session_state.model.predict(img) | |
| st.image(img, caption=f"This is a {label}.") | |
| st.write(f"{label_a}: {probs[1].item():.6f}") | |
| st.write(f"{label_b}: {probs[0].item():.6f}") | |
| elif choice == "Test Random Images": | |
| st.subheader("Test Using Images in Dataset") | |
| if "model" not in st.session_state: | |
| st.warning("Please train the model first.") | |
| else: | |
| for i in range(0, 5): # Change 5 to the number of images you want to display | |
| random_index = random.randint(0, len(get_image_files(food_path)) - 1) | |
| img_path = get_image_files(food_path)[random_index] | |
| img = mpimg.imread(img_path) | |
| label, _, probs = st.session_state.model.predict(img) | |
| st.image(img, caption=f"Predicted label: {label}") | |
| elif choice == "Confusion Matrix": | |
| st.subheader("Confusion Matrix") | |
| if "model" not in st.session_state: | |
| st.warning("Please train the model first.") | |
| else: | |
| interp = ClassificationInterpretation.from_learner(st.session_state.model) | |
| st.pyplot(interp.plot_confusion_matrix()) | |
| # Run the Streamlit app | |
| if __name__ == "__main__": | |
| main() | |