initial commit

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+# HuggingFace Spaces uses port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from azure.storage.blob import BlobServiceClient
+import torch
+import torch.nn as nn
+from PIL import Image
+from torchvision import transforms
+import torchvision.models as models
+import io
+import os
+
+app = FastAPI()
+
+# Allow your React app to call this API
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global variables
+model = None
+transform = None
+ASL_CLASSES = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 
+               'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 
+               'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']
+
+class ASLEfficientNet(nn.Module):
+    """EfficientNet-B3 - matches your uploaded model"""
+    def __init__(self, num_classes=29):
+        super(ASLEfficientNet, self).__init__()
+        
+        self.model = models.efficientnet_b3(weights=None)
+        
+        in_features = self.model.classifier[1].in_features
+        self.model.classifier = nn.Sequential(
+            nn.Dropout(0.3),
+            nn.Linear(in_features, 512),
+            nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.Linear(512, num_classes)
+        )
+    
+    def forward(self, x):
+        return self.model(x)
+
+
+@app.on_event("startup")
+async def load_model():
+    global model, transform
+    
+    print("Downloading model from Azure...")
+    
+    # Get connection string from environment variable
+    connection_string = os.getenv("AZURE_STORAGE_CONNECTION_STRING")
+    
+    # Download model
+    blob_service_client = BlobServiceClient.from_connection_string(connection_string)
+    blob_client = blob_service_client.get_blob_client(
+        container="models",
+        blob="deep_model.pth"
+    )
+    
+    # Save to temp file
+    with open("/tmp/model.pth", "wb") as f:
+        download_stream = blob_client.download_blob()
+        f.write(download_stream.readall())
+    
+    print("Loading model...")
+    
+    # Load checkpoint
+    checkpoint = torch.load("/tmp/model.pth", map_location="cpu")
+    
+    # Initialize model
+    model = ASLEfficientNet(num_classes=29)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+    
+    # Set up preprocessing
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    
+    print("Model loaded successfully!")
+
+
+@app.get("/")
+def root():
+    return {"message": "ASL API is running"}
+
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    
+    try:
+        # Read image
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
+        
+        # Preprocess
+        input_tensor = transform(image).unsqueeze(0)
+        
+        # Predict
+        with torch.no_grad():
+            output = model(input_tensor)
+            probabilities = torch.softmax(output, dim=1)
+            confidence, predicted_idx = probabilities.max(1)
+            
+            # Top 5
+            top5_prob, top5_idx = probabilities.topk(5, dim=1)
+        
+        # Convert to letter
+        predicted_class = predicted_idx.item()
+        predicted_letter = ASL_CLASSES[predicted_class]
+        
+        return {
+            "predicted_class": predicted_class,
+            "predicted_letter": predicted_letter,
+            "confidence": confidence.item(),
+            "top5_predictions": [
+                {
+                    "class": int(top5_idx[0][i]),
+                    "letter": ASL_CLASSES[int(top5_idx[0][i])],
+                    "confidence": float(top5_prob[0][i])
+                }
+                for i in range(5)
+            ]
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirements.txt

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+fastapi==0.104.1
+uvicorn[standard]==0.24.0
+python-multipart==0.0.6
+azure-storage-blob==12.19.0
+torch==2.1.0
+torchvision==0.16.0
+pillow==10.1.0