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848ad3f 26ae7d6 848ad3f 427167a 848ad3f 427167a 848ad3f 427167a 26ae7d6 848ad3f 26ae7d6 427167a 26ae7d6 848ad3f 26ae7d6 848ad3f 26ae7d6 427167a 26ae7d6 848ad3f 26ae7d6 848ad3f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import gradio as gr
import numpy as np
import tensorflow as tf
from PIL import Image
# Downloaded the image classification model we trained in lab 6.1 and uploaded it to the huggingface space
MODEL_PATH = "cats-vs-dogs-finetuned.keras"
# the input size the model expects to see
IMAGE_SIZE = (180, 180)
# Load model
model = tf.keras.models.load_model(MODEL_PATH)
def predict_image(img: Image.Image):
#if there is no input image, the probability of cat and dog is 50-50
if img is None:
return {"Cat": 0.5, "Dog": 0.5}
# Preprocess
x = img.convert("RGB").resize(IMAGE_SIZE)
x = np.asarray(x, dtype=np.float32) / 255.0
x = np.expand_dims(x, 0) # (1, H, W, 3)
# Predict: model outputs shape (1,1) with sigmoid for "Dog" probability
p_dog = float(model.predict(x)[0, 0])
return {"Cat": 1.0 - p_dog, "Dog": p_dog}
demo = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil", label="Upload a Cat or Dog"),
outputs=gr.Label(num_top_classes=2, label="Prediction"),
title="Cats vs Dogs (Keras, single-logit)",
description="keras image classification model for cat-vs-dog images"
)
if __name__ == "__main__":
demo.launch()
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