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README.md

@@ -9,4 +9,6 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# HF Model: Classification
+
+This folder contains files to load a Keras (.keras) image classification model on Hugging Face Inference.

app.py

@@ -0,0 +1,76 @@
+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)
+    return predictions
+
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Waste Classification",
+    description="Upload an image of waste to classify it.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

config.json

@@ -0,0 +1,19 @@
+{
+  "architectures": ["CustomKerasClassifier"],
+  "framework": "keras",
+  "image_size": 224,
+  "labels": [
+    "battery",
+    "biological",
+    "brown-glass",
+    "cardboard",
+    "clothes",
+    "green-glass",
+    "metal",
+    "paper",
+    "plastic",
+    "shoes",
+    "trash",
+    "white-glass"
+  ]
+}

inference.py

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+from typing import List, Dict, Any
+import io
+import numpy as np
+from PIL import Image
+import requests
+
+import tensorflow as tf
+
+# 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_bytes: bytes) -> np.ndarray:
+    # Mirror TS: ensure JPEG-like decode and resize 224x224, keep 0..255 range
+    image = Image.open(io.BytesIO(image_bytes))
+    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(self, inputs: bytes) -> List[Dict[str, Any]]:
+        x = _preprocess(inputs)
+        preds = self.model.predict(x)
+        if isinstance(preds, (list, tuple)):
+            preds = preds[0]
+        probs = np.asarray(preds).squeeze().tolist()
+        # Top-1 output following TS behavior
+        idx = int(np.argmax(probs))
+        return [
+            {"label": LABELS[idx], "score": float(probs[idx])},
+        ]
+
+
+def load_model(model_dir: str = ".") -> PreTrainedModel:
+    # HF Inference API convention: a top-level load entrypoint
+    return PreTrainedModel(model_path=f"{model_dir}/model/model_resnet50.keras")
+

model/model_resnet50.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93aca6d248291878520c966415f3a23a2320370e809ca4b45c6358e332518052
+size 243395061

requirements.txt

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+tensorflow==2.16.1
+numpy
+Pillow
+requests
+gradio