Delete utils.py

utils.py

@@ -1,45 +0,0 @@
-# utils.py
-
-import torch
-from torchvision import transforms
-from PIL import Image
-
-# Character-to-Index Mapping (should be the same as used during training)
-amharic_chars = list(' ሀሁሂሃሄህሆለሉሊላሌልሎሐሑሒሓሔሕሖመሙሚማሜምሞሰሱሲሳስሶረሩሪራሬርሮሠሡሢሣሤሥሦሸሹሺሻሼሽሾቀቁቂቃቄቅቆበቡቢባቤብቦተቱቲታቴትቶቸቹቺቻቼችቾኀኃነኑኒናኔንኖኘኙኚኛኜኝኞአኡኢኣኤእኦከኩኪካኬክኮኸኹኺኻኼኽኾወዉዊዋዌውዎዐዑዒዓዔዕዖዘዙዚዛዜዝዞዠዡዢዣዤዥዦየዩዪያዬይዮደዱዲዳዴድዶጀጁጂጃጄጅጆገጉጊጋጌግጎጠጡጢጣጤጥጦጨጩጪጫጬጭጮጰጱጲጳጴጵጶጸጹጺጻጼጽጾፀፁፂፃፄፅፆፈፉፊፋፌፍፎፐፑፒፓፔፕፖቨቩቪቫቬቭቮ0123456789፥፣()-ሏሟሷሯሿቧቆቈቋቷቿኗኟዟዧዷጇጧጯጿፏኳኋኧቯጐጕጓ።')
-
-char_to_idx = {char: idx + 1 for idx, char in enumerate(amharic_chars)}  # Start indexing from 1
-char_to_idx['<UNK>'] = len(amharic_chars) + 1  # Unknown characters
-idx_to_char = {idx: char for char, idx in char_to_idx.items()}
-idx_to_char[0] = '<blank>'  # CTC blank token
-
-def preprocess_image(image: Image.Image) -> torch.Tensor:
-    """
-    Preprocess the input image for the model.
-    """
-    transform = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                             std=[0.229, 0.224, 0.225]),
-    ])
-    return transform(image)
-
-def decode_predictions(preds: torch.Tensor) -> str:
-    """
-    Decode the model's predictions using Best Path Decoding.
-    """
-    preds = torch.argmax(preds, dim=2).transpose(0, 1)  # [batch_size, H*W]
-    decoded_texts = []
-    
-    for pred in preds:
-        pred = pred.cpu().numpy()
-        decoded = []
-        previous = 0  # Assuming blank index is 0
-        for p in pred:
-            if p != previous and p != 0:
-                decoded.append(idx_to_char.get(p, '<UNK>'))
-            previous = p
-        recognized_text = ''.join(decoded)
-        decoded_texts.append(recognized_text)
-    
-    return decoded_texts