Create inference.py

inference.py

@@ -0,0 +1,47 @@
+import torch
+import torchvision.transforms as T
+from PIL import Image
+
+# Load the YOLOv5 model
+model = torch.hub.load('niki-stha/asl-detection-yolov5', 'yolov5s')
+
+# Set the device (GPU if available, otherwise CPU)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device).eval()
+
+# Define the image transformation
+transform = T.Compose([
+    T.Resize((416, 416)),
+    T.ToTensor(),
+])
+
+# Inference function
+def run_inference(image):
+    # Preprocess the image
+    image = transform(image).unsqueeze(0).to(device)
+
+    # Perform inference
+    results = model(image)
+
+    # Post-process the results
+    # (You can customize this part based on your specific requirements)
+    predictions = results.pandas().xyxy[0]
+
+    return predictions
+
+# Example API endpoint
+def inference_api(request):
+    # Get the image from the request (you may need to adapt this based on your API framework)
+    image_data = request.files['image'].read()
+    image = Image.open(io.BytesIO(image_data))
+
+    # Run inference
+    predictions = run_inference(image)
+
+    # Convert predictions to JSON or any other desired format
+    # (You may need to adapt this based on your API framework)
+    response = {
+        'predictions': predictions.to_dict(orient='records')
+    }
+
+    return jsonify(response)