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import numpy as np | |
import tensorflow as tf | |
import gradio as gr | |
# Load your trained model | |
best_model = tf.keras.models.load_model("best_EffiB0.keras") | |
# Define your class names | |
class_names = ["cardboard", "glass", "metal", "paper", "plastic", "trash"] | |
num_classes = len(class_names) | |
IMAGE_SIZE = (124, 124) # | |
def classify_image(img): | |
img = tf.image.resize(img, IMAGE_SIZE)[None, ...] | |
preds = best_model.predict(img) | |
return {class_names[i]: float(preds[0, i]) for i in range(num_classes)} | |
custom_footer = """ | |
<p style="text-align: center;"> | |
Developed with ❤️ by <strong>Adhiraj</strong> | |
</p> | |
<p style="text-align: center;"> | |
<a href="https://www.linkedin.com/in/akathedeveloper/" target="_blank" style="display: inline-block; margin: 0 10px;"> | |
<img src="https://cdn-icons-png.flaticon.com/512/174/174857.png" alt="LinkedIn" width="30"> | |
</a> | |
<a href="https://github.com/akathedeveloper" target="_blank" style="display: inline-block; margin: 0 10px;"> | |
<img src="https://cdn-icons-png.flaticon.com/512/25/25231.png" alt="GitHub" width="30"> | |
</a> | |
</p> | |
""" | |
demo = gr.Interface( | |
fn=classify_image, | |
inputs=gr.Image(type="numpy"), | |
outputs=gr.Label(num_top_classes=3), | |
title="Garbage Classifier", | |
description="Classify images into cardboard, glass, metal, paper, plastic, or trash.", | |
article=custom_footer | |
) | |
demo.launch() | |