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from tensorflow.keras.models import load_model
import numpy as np
import gradio as gr
import tensorflow as tf
from io import StringIO
from PIL import Image
import subprocess
url = "https://drive.usercontent.google.com/download?id=1T5HGnk9Mxlb5G6FTxp26BWSrTpzbjtP2&export=download&authuser=0"
subprocess.run(["curl", "-o", "models.h5", "-L", url])
labels = []
model = load_model('models.h5')
with open("name of the animals.txt") as f:
for line in f:
labels.append(line.replace('\n', ''))
def classify_image(inp):
# Create a copy of the input array to avoid reference issues
inp_copy = np.copy(inp)
# Resize the input image to the expected shape (224, 224)
inp_copy = Image.fromarray(inp_copy)
inp_copy = inp_copy.resize((224, 224))
inp_copy = np.array(inp_copy)
inp_copy = inp_copy.reshape((-1, 224, 224, 3))
inp_copy = tf.keras.applications.efficientnet.preprocess_input(inp_copy)
prediction = model.predict(inp_copy).flatten()
confidences = {labels[i]: float(prediction[i]) for i in range(90)}
return confidences
demo = gr.Interface(classify_image, gr.Image(), gr.Label(num_top_classes=3))
if __name__ == "__main__":
demo.launch()
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