Delete handler.py

handler.py

@@ -1,58 +0,0 @@
-import torch
-from torchvision import transforms
-from huggingface_hub import hf_hub_download
-import json
-import io
-import base64
-from PIL import Image
-from omegaconf import OmegaConf
-
-from model import Generator
-
-
-class EndpointHandler:
-
-    def __init__(self, path=''):
-        self.transform = transforms.Compose(
-            [
-                transforms.ToImage(),
-                transforms.ToDtype(torch.float32, scale=True),
-                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
-            ]
-        )
-
-        repo_id = "Kiwinicki/sat2map-generator"
-        generator_path = hf_hub_download(repo_id=repo_id, filename="generator.pth")
-        config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
-        model_path = hf_hub_download(repo_id=repo_id, filename="model.py")
-
-        with open(config_path, "r") as f:
-            config_dict = json.load(f)
-        cfg = OmegaConf.create(config_dict)
-
-        self.generator = Generator(cfg)
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.generator.load_state_dict(torch.load(generator_path, map_location=self.device))
-        self.generator.eval()
-
-
-    def __call__(self, data: dict[str, any]) -> dict[str, str]:
-        base64_image = data.get('inputs')
-        input_tensor = self._decode_base64_image(base64_image)
-        # print('Input tensor shape: ' + str(input_tensor.shape))
-        output_tensor = self.generator(input_tensor.to(self.device))
-        output_tensor = output_tensor.squeeze(0)
-        output_image = transforms.ToPILImage()(output_tensor)
-        output_image = output_image.convert('RGB')
-        output_buffer = io.BytesIO()
-        output_image.save(output_buffer, format="png")
-        base64_output = base64.b64encode(output_buffer.getvalue()).decode('utf-8')
-        return {"output": base64_output}
-
-
-    def _decode_base64_image(self, base64_image: str) -> torch.Tensor:
-        image_decoded = base64.b64decode(base64_image)
-        image = Image.open(io.BytesIO(image_decoded)).convert('RGB')
-        image_tensor: torch.Tensor = self.transform(image)
-        image_tensor = image_tensor.unsqueeze(0)
-        return image_tensor