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import streamlit as st
import cv2
import numpy as np
from PIL import Image
# import imutils
# import easyocr
# import os
from fastai.vision.all import *
import pathlib
import platform
import os
# import shutil
from fruit_classifier.config.configuration import ConfigurationManager
system_platform = platform.system()
if system_platform == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
config_manager = ConfigurationManager()
config = config_manager.get_training_config()
MODEL_ROOT = config.trained_model_path
MODEL_NAME = config.params_model_name + '.pkl'
MODEL_PATH = os.path.join(MODEL_ROOT, MODEL_NAME)
def main():
st.title("Fruit Classifier")
# Use st.camera to capture images from the user's camera
img_file_buffer = st.camera_input(label='Please, take a photo of a fruit', key='fruit')
# Check if an image is captured
if img_file_buffer is not None:
# Convert the image to a NumPy array
image = Image.open(img_file_buffer)
image.save('fruit_image.jpg')
# image_np = np.array(image)
# resized_image = cv2.resize(image_np, (640, 640))
# resized_image = resized_image.astype(np.uint8)
# resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
# image = cv2.imread(img_file_buffer)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# cv2.imwrite('fruit_image.jpg', image)
model = load_learner(MODEL_PATH)
model_output = model.predict('fruit_image.jpg')
category_list = [cat for cat in model.dls.vocab]
prob_idx = category_list.index(model_output[0])
st.write(f'{model_output[0].title()} is depicted in the photo with {model_output[-1][prob_idx]:.4f} confidence.')
st.session_state.pop("fruit")
if __name__ == "__main__":
main() |