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Train_food_model_by_Ahtisham / app.py

Ahtisham1583

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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()