Add application file

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, File
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import requests
+import os
+
+MODEL_URL = "https://huggingface.co/blaxx14/cat-vs-dog-inceptionv3/resolve/main/cat_dog_inception_v3.h5"
+MODEL_PATH = "cat_dog_inception_v3.h5"
+
+app = FastAPI()
+
+def download_model():
+    if not os.path.exists(MODEL_PATH):
+        print("Downloading model...")
+        response = requests.get(MODEL_URL)
+        with open(MODEL_PATH, "wb") as f:
+            f.write(response.content)
+        print("Model downloaded!")
+
+download_model()
+
+print("Loading model...")
+model = tf.keras.models.load_model(MODEL_PATH)
+print("Model loaded!")
+
+def preprocess_image(image):
+    img = Image.open(io.BytesIO(image)).convert("RGB")
+    img = img.resize((150, 150))  
+    img = np.array(img) / 255.0   
+    img = np.expand_dims(img, axis=0)  
+    return img
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    image = await file.read()
+    processed_img = preprocess_image(image)
+    prediction = model.predict(processed_img)
+    return {"prediction": prediction.tolist()}

cat_dog_inception_v3.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5706614a14c05b6be091aecb989b31cb236bbeece2377bac949da33c37f9157b
+size 505377176

requirements.txt

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+fastapi
+pillow
+tensorflow==2.18.0
+uvicorn[standard]