app.py

"""
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()
"""