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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app files
+COPY app.py .
+COPY models/ models/
+
+# Expose port 7860 (HF Spaces default)
+EXPOSE 7860
+
+# Run the API
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+"""
+FastAPI for Pneumonia Detection - Hugging Face Spaces Deployment
+"""
+
+import io
+import time
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+# =============================================================================
+# Configuration
+# =============================================================================
+
+IMAGE_SIZE = 224
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+CLASS_NAMES = ["NORMAL", "PNEUMONIA"]
+MODEL_PATH = Path("models/best_model.pt")
+
+# =============================================================================
+# Model Definition
+# =============================================================================
+
+class PneumoniaClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.backbone = models.efficientnet_b0(weights=None)
+        in_features = self.backbone.classifier[1].in_features
+        self.backbone.classifier = nn.Sequential(
+            nn.Dropout(p=0.3, inplace=True),
+            nn.Linear(in_features, 1)
+        )
+
+    def forward(self, x):
+        return self.backbone(x)
+
+# =============================================================================
+# Response Models
+# =============================================================================
+
+class HealthResponse(BaseModel):
+    status: str
+    model_loaded: bool
+
+class PredictionResponse(BaseModel):
+    prediction: str
+    confidence: float
+    probability: float
+    processing_time_ms: float
+
+# =============================================================================
+# App Setup
+# =============================================================================
+
+app = FastAPI(
+    title="Pneumonia Detection API",
+    description="Deep learning API for detecting pneumonia from chest X-rays",
+    version="1.0.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# =============================================================================
+# Model Loading
+# =============================================================================
+
+model = None
+device = None
+
+@app.on_event("startup")
+async def load_model():
+    global model, device
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    if not MODEL_PATH.exists():
+        print(f"Warning: Model not found at {MODEL_PATH}")
+        return
+
+    model = PneumoniaClassifier()
+    checkpoint = torch.load(MODEL_PATH, map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.to(device)
+    model.eval()
+    print("Model loaded successfully")
+
+# =============================================================================
+# Helper Functions
+# =============================================================================
+
+def get_transforms():
+    return transforms.Compose([
+        transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
+    ])
+
+async def read_image(file: UploadFile) -> Image.Image:
+    contents = await file.read()
+    return Image.open(io.BytesIO(contents)).convert("RGB")
+
+def predict(image: Image.Image):
+    transform = get_transforms()
+    img_tensor = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(img_tensor)
+        prob = torch.sigmoid(output).item()
+
+    pred_class = CLASS_NAMES[1] if prob > 0.5 else CLASS_NAMES[0]
+    confidence = prob if prob > 0.5 else 1 - prob
+    return pred_class, confidence, prob
+
+# =============================================================================
+# Endpoints
+# =============================================================================
+
+@app.get("/")
+async def root():
+    return {"message": "Pneumonia Detection API", "docs": "/docs"}
+
+@app.get("/health", response_model=HealthResponse)
+async def health():
+    return HealthResponse(
+        status="healthy" if model else "model_not_loaded",
+        model_loaded=model is not None
+    )
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict_endpoint(file: UploadFile = File(...)):
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="File must be an image")
+
+    image = await read_image(file)
+
+    start_time = time.time()
+    pred_class, confidence, prob = predict(image)
+    processing_time = (time.time() - start_time) * 1000
+
+    return PredictionResponse(
+        prediction=pred_class,
+        confidence=confidence,
+        probability=prob,
+        processing_time_ms=round(processing_time, 2)
+    )

requirements.txt

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+torch>=2.0.0
+torchvision>=0.15.0
+fastapi>=0.100.0
+uvicorn>=0.23.0
+python-multipart>=0.0.6
+pillow>=10.0.0
+numpy>=1.24.0