inference.py

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)