app.py

import json
from PIL import Image
import numpy as np
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(inputs)
    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")