Update app.py

app.py

@@ -1,17 +1,12 @@
-import json
 from PIL import Image
+import tensorflow as tf
 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'
+#from google.colab import files
 
+#model_path = 'final_teath_classifier.h5'
+# Load the model
 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))
@@ -25,41 +20,21 @@ 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)
+    predictions = model.predict(img_array)
     predicted_class = np.argmax(predictions)
     if predicted_class == 0:
-        predict_label = "Clean"
+      predict_label = "Clean"
     else:
-        predict_label = "Carries"
-
-    return predict_label, predictions.numpy().flatten()
+      predict_label = "Carries"
 
-# Create a file uploader widget
-uploader = widgets.FileUpload(accept="image/*", multiple=False)
 
-# Display the file uploader widget
-display(uploader)
+    return predict_label,predictions.flatten()
+# Upload the image
+#uploaded = files.upload()
 
-# 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))
+# Get the uploaded image file name
+#image_path = list(uploaded.keys())[0]
 
-# Set the callback function for when a file is uploaded
-uploader.observe(on_upload, names="value")
+predict_label, logits = predict_image(image_path)
+print("Predicted class:", predict_label)
+print("Evaluate:", ', '.join(f"{logits*100:.4f}%" for logits in logits))