model/pothole_model.py

# 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


import tensorflow as tf
from PIL import Image, ImageOps
import numpy as np
import requests
from io import BytesIO
from keras.models import load_model



def load_image_model(image):
    # Disable scientific notation for clarity
    np.set_printoptions(suppress=True)

    # Load the model from the URL
    model = load_model("keras_model.h5", compile=False)

    # 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(BytesIO(image)).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)
    return class_name