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handler.py

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+import torch
+import string
+from PIL import Image
+from torchvision import transforms
+import io
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # TorchScript model load ho raha hai
+        self.model = torch.jit.load(f"{path}/master_brain_2026_final.pt")
+        self.model.eval()
+        self.chars = string.ascii_uppercase + string.ascii_lowercase + string.digits + "@#$%&="
+        self.blank_index = len(self.chars)
+        self.preprocess = transforms.Compose([
+            transforms.Resize((35, 142)),
+            transforms.ToTensor(),
+            transforms.Normalize((0.5,), (0.5,))
+        ])
+
+    def __call__(self, data):
+        inputs = data.pop("inputs", data)
+        # Base64 ya raw bytes ko handle karne ke liye
+        if isinstance(inputs, str):
+            import base64
+            inputs = Image.open(io.BytesIO(base64.b64decode(inputs))).convert('RGB')
+        else:
+            inputs = Image.open(io.BytesIO(inputs)).convert('RGB')
+        
+        img_tensor = self.preprocess(inputs).unsqueeze(0)
+        with torch.no_grad():
+            logits = self.model(img_tensor)
+            max_indices = torch.argmax(logits, dim=2).squeeze()
+            res = []
+            prev = -1
+            for idx in max_indices:
+                val = idx.item()
+                if val != self.blank_index and val != prev:
+                    res.append(self.chars[val])
+                prev = val
+        return {"prediction": "".join(res).strip()}

requirements.txt

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+torch
+torchvision
+Pillow