app.py

#!/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()