Spaces:
Runtime error
Runtime error
File size: 1,950 Bytes
159fb0f f7d5b45 e845a5d c1c9c3a 742b795 cc9f68a 6040ac9 159fb0f c1c9c3a a1507f1 3039e58 c47223a 3039e58 06ee487 3039e58 47a795d e015e39 3062159 47a795d e015e39 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 62 |
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 labels.items()]
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)
|