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import json | |
from PIL import Image | |
import numpy as np | |
import tensorflow as tf | |
from transformers import TFAutoModelForSequenceClassification, AutoTokenizer | |
from tensorflow.keras.models import load_model | |
import ipywidgets as widgets | |
from IPython.display import display | |
model_path = 'final_teath_classifier.h5' | |
model = tf.keras.models.load_model(model_path) | |
# Load the model from Hugging Face model hub | |
def preprocess_image(image: Image.Image) -> np.ndarray: | |
# Resize the image to match input size | |
image = image.resize((256, 256)) | |
# Convert image to array and preprocess input | |
img_array = np.array(image) / 255.0 | |
# Add batch dimension | |
img_array = np.expand_dims(img_array, axis=0) | |
return img_array | |
def predict_image(image_path): | |
img = Image.open(image_path) | |
# Preprocess the image | |
img_array = preprocess_image(img) | |
# Convert image array to string using base64 encoding (for text-based models) | |
#inputs = tokenizer.encode(img_array, return_tensors="tf") | |
# Make prediction | |
outputs = model(img_array) | |
predictions = tf.nn.softmax(outputs.logits, axis=-1) | |
predicted_class = np.argmax(predictions) | |
if predicted_class == 0: | |
predict_label = "Clean" | |
else: | |
predict_label = "Carries" | |
return predict_label, predictions.numpy().flatten() | |
# Create a file uploader widget | |
uploader = widgets.FileUpload(accept="image/*", multiple=False) | |
# Display the file uploader widget | |
display(uploader) | |
# Define a callback function to handle the uploaded image | |
def on_upload(change): | |
# Get the uploaded image file | |
image_file = list(uploader.value.values())[0]["content"] | |
# Save the image to a temporary file | |
with open("temp_image.jpg", "wb") as f: | |
f.write(image_file) | |
# Get predictions for the uploaded image | |
predict_label, logits = predict_image("temp_image.jpg") | |
# Create a JSON object with the predictions | |
predictions_json = { | |
"predicted_class": predict_label, | |
"evaluations": [f"{logit*100:.4f}%" for logit in logits] | |
} | |
# Print the JSON object | |
print(json.dumps(predictions_json, indent=4)) | |
# Set the callback function for when a file is uploaded | |
uploader.observe(on_upload, names="value") | |