Add application file

Dockerfile

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+FROM python:3.10-slim
+
+# Set up a new user to avoid root permission issues
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:${PATH}"
+
+WORKDIR /app
+
+COPY --chown=user . /app
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Port 7860 is the default for HF Spaces
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, File
+from PIL import Image
+import torch
+from torchvision import models, transforms
+import io
+
+app = FastAPI()
+
+# 1. Load the pre-trained MobileNetV2 model
+model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.DEFAULT)
+model.eval()
+
+# 2. Define the image transformation
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# 3. Load ImageNet labels (simplified helper)
+import json
+from urllib.request import urlopen
+LABELS_URL = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_class_index.json"
+labels = json.load(urlopen(LABELS_URL))
+
+@app.get("/")
+def home():
+    return {"message": "MobileNet API is running! Send a POST request to /predict"}
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    # Read image
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    
+    # Preprocess and Infer
+    input_tensor = preprocess(image).unsqueeze(0)
+    with torch.no_grad():
+        output = model(input_tensor)
+    
+    # Get top prediction
+    percentage = torch.nn.functional.softmax(output, dim=1)[0] * 100
+    _, index = torch.max(output, 1)
+    label = labels[str(index.item())][1]
+    confidence = percentage[index[0]].item()
+
+    return {"prediction": label, "confidence": f"{confidence:.2f}%"}

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+torchvision
+fastapi
+uvicorn
+pillow
+python-multipart