Create app.py

app.py

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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")