Spaces:
Sleeping
Sleeping
| # install (if needed) | |
| #pip install tensorflow | |
| import tensorflow as tf | |
| # load SavedModel dir or .h5 | |
| #model = tf.keras.models.load_model("stone_artifact_classifier.h5") # use compile=False if custom objects | |
| # or: model = tf.keras.models.load_model("my_model.h5", compile=False) | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| # Load your model (handle any custom layers if needed) | |
| # If you have custom layers like RandomHeight, include them in custom_objects: | |
| model = tf.keras.models.load_model("stone_artifact_classifier_with_brokenshards.h5", compile=False, custom_objects={"RandomHeight": tf.keras.layers.RandomHeight, "RandomWidth": tf.keras.layers.RandomWidth}) | |
| #model = tf.keras.models.load_model("stone_artifact_classifier.h5", compile=False) | |
| # Replace with your classes | |
| class_names = ["arrowheads", "axehammerstones", "scrapers"] | |
| IMG_SIZE = (224, 224) # change this to your model's expected size | |
| from tensorflow.keras.applications.efficientnet import preprocess_input | |
| def predict(img): | |
| """Predict function for Gradio""" | |
| img = img.convert("RGB").resize(IMG_SIZE) | |
| x = np.array(img) | |
| x = preprocess_input(x) | |
| x = np.expand_dims(x, 0) | |
| preds = model.predict(x)[0] | |
| # top = np.argmax(preds) | |
| return {class_names[i]: float(preds[i]) for i in range(len(preds))} | |
| # Gradio interface | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=len(class_names)), | |
| title="Stone Artifact Classifier", | |
| description="Upload an image of an artifact like projectile points, hammer stones or scraper rocks and the model will classify it." | |
| ) | |
| iface.launch() | |