Spaces:
Runtime error
Runtime error
File size: 1,958 Bytes
159fb0f f7d5b45 e845a5d c1c9c3a 742b795 cc9f68a 6040ac9 159fb0f c1c9c3a a1507f1 3039e58 c47223a 3039e58 06ee487 3039e58 47a795d 3062159 47a795d 0cfd3a9 c1c9c3a 159fb0f c1c9c3a a1507f1 24c337c c47223a 4bfddf6 159fb0f |
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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow_addons as tfa
import os
import numpy as np
# labels= {'Burger King': 0, 'KFC': 1,'McDonalds': 2,'Other': 3,'Starbucks': 4,'Subway': 5}
HEIGHT,WIDTH=224,224
NUM_CLASSES=6
model=load_model('best_model.h5')
# def classify_image(inp):
# np.random.seed(143)
# inp = inp.reshape((-1, HEIGHT,WIDTH, 3))
# inp = tf.keras.applications.nasnet.preprocess_input(inp)
# prediction = model.predict(inp)
# ###label = dict((v,k) for k,v in labels.items())
# predicted_class_indices=np.argmax(prediction,axis=1)
# result = {}
# for i in range(len(predicted_class_indices)):
# if predicted_class_indices[i] < NUM_CLASSES:
# result[labels[predicted_class_indices[i]]]= float(predicted_class_indices[i])
# return result
def classify_image(inp):
np.random.seed(143)
labels = {'Burger King': 0, 'KFC': 1, 'McDonalds': 2, 'Other': 3, 'Starbucks': 4, 'Subway': 5}
NUM_CLASSES = 6
inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
inp = tf.keras.applications.nasnet.preprocess_input(inp)
prediction = model.predict(inp)
predicted_class_indices = np.argmax(prediction, axis=1)
result = {}
# for i in range(len(predicted_class_indices)):
# if predicted_class_indices[i] < NUM_CLASSES:
# try:
# label = labels[predicted_class_indices[i]]
# result[label] = float(predicted_class_indices[i])
# except KeyError:
# print(f"KeyError: Label not found for index {predicted_class_indices[i]}")
result = [f"{label}: {prediction:.2f}" for label, prediction in zip(labels, prediction)]
return ", ".join(result)
image = gr.Image(shape=(HEIGHT,WIDTH),label='Input')
label = gr.Textbox()
gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Brand Logo Detection').launch(debug=False)
|