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"""
import tensorflow as tf
inception_net = tf.keras.applications.MobileNetV2()
import requests
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def classify_image(inp):
inp = inp.reshape((-1, 224, 224, 3))
inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
prediction = inception_net.predict(inp).flatten()
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
gr.Interface(fn=classify_image,
inputs=gr.Image(shape=(224, 224)),
outputs=gr.Label(num_top_classes=3),
#examples=["banana.jpg", "car.jpg"]
).launch(share=True)
"""
import gradio as gr
import tensorflow as tf
from tensorflow import keras
import requests
# load pre-trained model
model_path = "/Users/chaninderrishi/Desktop/ML/projects/waste-sorting/models/prod3"
pre_trained_model = keras.models.load_model(model_path)
labels = ['compost', 'e-waste', 'recycle', 'trash']
def classify_image(input):
prediction = pre_trained_model.predict(input)
confidences = {labels[i]: float(prediction[i]) for i in range(4)}
return confidences
iface = gr.Interface(fn=classify_image,
inputs=gr.Image(shape=(224, 224)),
outputs=gr.Label(num_top_classes=3),
#examples=["banana.jpg", "car.jpg"]
)
iface.launch(share=True)
"""
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
""" |