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# streamlit_app.py
import streamlit as st
from fastai.vision.all import *
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Function to get the label from the file name
def GetLabel(fileName):
return fileName.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 load the pre-trained model
def load_pretrained_model():
model_path = "export.pkl" # Update with the correct path to your export.pkl
return load_learner(model_path)
# Streamlit app
def main():
st.title("Food Classifier Streamlit App")
# Sidebar options
options = ["Upload Image", "Test Random Images", "Confusion Matrix"]
choice = st.sidebar.selectbox("Choose an option", options)
if choice == "Upload Image":
st.subheader("Upload Your Own Images")
model = load_pretrained_model()
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 = model.predict(img)
st.image(img, caption=f"This is a {label}.")
st.write(f"{label}: {probs[1].item():.6f}")
st.write(f"{label}: {probs[0].item():.6f}")
elif choice == "Test Random Images":
st.subheader("Test Using Images in Dataset")
model = load_pretrained_model()
food_path = Path("~/.fastai/data/food-101/food-101").expanduser()
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 = model.predict(img)
st.image(img, caption=f"Predicted label: {label}")
elif choice == "Confusion Matrix":
st.subheader("Confusion Matrix")
model = load_pretrained_model()
interp = ClassificationInterpretation.from_learner(model)
st.pyplot(interp.plot_confusion_matrix())
# Run the Streamlit app
if __name__ == "__main__":
main()
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