initiated

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+# from PIL import Image, ImageDraw
+# import imutils
+# import easyocr
+# import os
+from fastai.vision.all import *
+import pathlib
+import platform
+import os
+# import shutil
+from src.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
+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_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')
+
+        prob_idx = model.dls.vocab.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()