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# from PIL import Image
# from io import BytesIO
# from transformers import AutoImageProcessor, AutoModelForImageClassification
# # Load model
# processor = AutoImageProcessor.from_pretrained("taroii/pothole-detection-model")
# model = AutoModelForImageClassification.from_pretrained("taroii/pothole-detection-model")
# # Function to predict if an image contains a pothole
# def predict_pothole(image_url):
# image = Image.open(BytesIO(image_url))
# inputs = processor(images=image, return_tensors="pt")
# # Perform inference
# outputs = model(**inputs)
# logits = outputs.logits
# probabilities = logits.softmax(dim=1)
# # Get predicted class (0: No pothole, 1: Pothole)
# predicted_class = probabilities.argmax().item()
# confidence = probabilities[0, predicted_class].item()
# return predicted_class
from keras.models import load_model # TensorFlow is required for Keras to work
from PIL import Image, ImageOps # Install pillow instead of PIL
import numpy as np
def load_image_model(image):
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Load the model
model = load_model("https://huggingface.co/spaces/Soham0708/pothole_detect/blob/main/keras_model.h5")
# Load the labels
class_names = open("labels.txt", "r").readlines()
# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image
image = Image.open("<IMAGE_PATH>").convert("RGB")
# resizing the image to be at least 224x224 and then cropping from the center
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
# turn the image into a numpy array
image_array = np.asarray(image)
# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
# Load the image into the array
data[0] = normalized_image_array
# Predicts the model
prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]
# Print prediction and confidence score
print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)
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