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9b67ef7 33692ee 196151b 33692ee 9b67ef7 b971c9d 7e3d02c 33692ee 9b67ef7 | 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 63 64 65 66 67 68 69 70 | #!/usr/bin/env python
# coding: utf-8
# #### Gradio Comparing Transfer Learning Models
import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import requests
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
mobile_net = tf.keras.applications.MobileNetV2()
inception_net = tf.keras.applications.InceptionV3()
# In[2]:
def classify_image_with_mobile_net(im):
im = Image.fromarray(im.astype('uint8'), 'RGB')
im = im.resize((224, 224))
arr = np.array(im).reshape((-1, 224, 224, 3))
arr = tf.keras.applications.mobilenet.preprocess_input(arr)
prediction = mobile_net.predict(arr).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
# In[3]:
def classify_image_with_inception_net(im):
# Resize the image to
im = Image.fromarray(im.astype('uint8'), 'RGB')
im = im.resize((299, 299))
arr = np.array(im).reshape((-1, 299, 299, 3))
arr = tf.keras.applications.inception_v3.preprocess_input(arr)
prediction = inception_net.predict(arr).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
# In[4]:
imagein = gr.inputs.Image()
label = gr.outputs.Label(num_top_classes=3)
sample_images = [
]
# In[6]:
gr.Interface(
fn = classify_image_with_mobile_net,
inputs=imagein,
outputs=label,
title="MobileNet",examples=sample_images).launch()
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