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18742db | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
from typing import List, Dict, Any
import io
# Labels must mirror src/classification-model/index.ts
LABELS: List[str] = [
"battery",
"biological",
"brown-glass",
"cardboard",
"clothes",
"green-glass",
"metal",
"paper",
"plastic",
"shoes",
"trash",
"white-glass",
]
def _load_image_to_rgb(image: Image.Image) -> np.ndarray:
if image.mode != "RGB":
image = image.convert("RGB")
return np.asarray(image)
def _resize_224(img_rgb: np.ndarray) -> np.ndarray:
im = Image.fromarray(img_rgb)
im = im.resize((224, 224), Image.NEAREST)
return np.asarray(im)
def _preprocess(image: Image.Image) -> np.ndarray:
rgb = _load_image_to_rgb(image)
rgb224 = _resize_224(rgb)
# shape [1,224,224,3], float32 in 0..255
arr = rgb224.astype("float32")
return np.expand_dims(arr, axis=0)
class PreTrainedModel:
def __init__(self, model_path: str = "model/model_resnet50.keras") -> None:
self.model = tf.keras.models.load_model(model_path)
def predict_image(self, image: Image.Image) -> Dict[str, float]:
x = _preprocess(image)
preds = self.model.predict(x)
if isinstance(preds, (list, tuple)):
preds = preds[0]
probs = np.asarray(preds).squeeze().tolist()
return {label: score for label, score in zip(LABELS, probs)}
model = PreTrainedModel()
def predict(image):
predictions = model.predict_image(image)
probs_percent = {label: round(p * 100, 2)
for label, p in predictions.items()}
max_label = max(probs_percent, key=probs_percent.get)
return {
"label": max_label,
"percentage": probs_percent[max_label],
"probabilities": probs_percent,
}
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.JSON(),
title="Waste Classification",
description="Upload an image of waste to classify it.",
)
if __name__ == "__main__":
iface.launch()
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