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.gitattributes

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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+__pycache__/
+*.pyc
+env/

README.md

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----
-title: Tftest01
-emoji: 😻
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.30.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Tftest01
+emoji: 😻
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.30.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# --- Config ---
+MODEL_PATH = "./model/best_model.h5"
+LABELS = ["class1", "class2", "class3", "class4"]  # ← update to your labels
+IMG_SIZE = 512
+
+# --- Load model ---
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# --- Preprocessing ---
+def preprocess(image: Image.Image):
+    img = image.resize((IMG_SIZE, IMG_SIZE)).convert("RGB")
+    arr = np.array(img)
+    arr = np.expand_dims(arr, 0)
+    return tf.keras.applications.mobilenet_v3.preprocess_input(arr)
+
+# --- Prediction function ---
+def predict(image):
+    """
+    Gradio accepts PIL image. Returns (label, confidence).
+    """
+    arr = preprocess(image)
+    preds = model.predict(arr)[0]
+    idx = int(np.argmax(preds))
+    label = LABELS[idx] if idx < len(LABELS) else "Unknown"
+    confidence = float(preds[idx])
+    return {label: confidence}
+
+# --- Launch Gradio Interface ---
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=1),
+    title="My TF Classifier",
+    description="Upload an image and get back its class and confidence."
+)
+
+if __name__ == "__main__":
+    iface.launch()

model/best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cff76e67424c71f57bff508290b8561ae7b407036b9b335c9e03775ed693d7d6
+size 12733416

render.yaml

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+services:
+  - type: web
+    name: tf-classifier-api
+    env: python
+    plan: free
+    buildCommand: ""
+    startCommand: gunicorn app:app

requirements.txt

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+gradio
+tensorflow-cpu==2.12.0
+Pillow
+numpy