Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Demo.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Icb8zeoaudyTDOKM1QySNay1cXzltRAp
+"""
+
+!pip install -q -U gradio peft
+
+import gradio as gr
+from tqdm.notebook import tqdm
+from PIL import Image
+import re
+
+import torch
+import torch.nn as nn
+from warnings import simplefilter
+
+simplefilter('ignore')
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Seting up the model
+from peft import PeftConfig, PeftModel
+numeric_lora_config = PeftConfig.from_pretrained("Edgar404/donut-sroie-lora-r8-x3")
+
+from transformers import VisionEncoderDecoderConfig
+
+image_size = [720,960]
+max_length = 512
+
+config = VisionEncoderDecoderConfig.from_pretrained(numeric_lora_config.base_model_name_or_path)
+config.encoder.image_size = image_size
+config.decoder.max_length = max_length
+
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+model = VisionEncoderDecoderModel.from_pretrained(numeric_lora_config.base_model_name_or_path ,config = config )
+numeric_processor = DonutProcessor.from_pretrained("Edgar404/donut-sroie-lora-r8-x3")
+
+model.config.pad_token_id = numeric_processor.tokenizer.pad_token_id
+model.config.decoder_start_token_id = numeric_processor.tokenizer.convert_tokens_to_ids(['<s_cord-v2>'])[0]
+model.decoder.resize_token_embeddings(len(numeric_processor.tokenizer))
+
+model = PeftModel.from_pretrained(model, model_id = "Edgar404/donut-sroie-lora-r8-x3", adapter_name = 'numeric')
+model.to(device)
+
+# Handwritten setting
+
+hand_processor = DonutProcessor.from_pretrained("Edgar404/donut-lora-r8-x2")
+
+
+def resize_token_handwritten():
+
+  try :
+    model.load_adapter("Edgar404/donut-lora-r8-x2" ,'handwritten')
+
+  except Exception :
+    # resizing the handwritten embedding layer
+    embedding_layer  = model.decoder.model.decoder.embed_tokens.modules_to_save.handwritten
+    old_num_tokens, old_embedding_dim = embedding_layer.weight.shape
+
+    new_embeddings = nn.Embedding(
+            len(hand_processor.tokenizer), old_embedding_dim
+    )
+
+    new_embeddings.to(
+        embedding_layer.weight.device,
+        dtype=embedding_layer.weight.dtype,
+    )
+
+    model.decoder.model.decoder.embed_tokens.modules_to_save.handwritten = new_embeddings
+
+
+    # Resizing the handwritten lm_head layer
+    lm_layer = model.decoder.lm_head.modules_to_save.handwritten
+
+    old_num_tokens, old_input_dim = lm_layer.weight.shape
+
+    new_lm_head = nn.Linear(
+            old_input_dim, len(hand_processor.tokenizer),
+            bias = False
+    )
+
+    new_lm_head.to(
+        lm_layer.weight.device,
+        dtype=lm_layer.weight.dtype,
+    )
+    model.decoder.lm_head.modules_to_save.handwritten = new_lm_head
+
+
+resize_token_handwritten()
+
+model.load_adapter("Edgar404/donut-lora-r8-x2" ,'handwritten')
+
+
+def process_image(image , mode = 'numeric' ):
+    """ Function that takes an image and perform an OCR using the model DonUT via the task document
+    parsing
+
+    parameters
+    __________
+    image : a machine readable image of class PIL or numpy"""
+
+    model.set_adapter(mode)
+    processor = numeric_processor if mode == 'numeric' else hand_processor
+
+
+
+    task_prompt = "<s_cord-v2>"
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_position_embeddings,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+
+    sequence = processor.batch_decode(outputs.sequences)[0]
+    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
+    output = processor.token2json(sequence)
+
+    return output
+
+import gradio as gr
+
+def image_classifier(image , mode):
+    return process_image(image , mode)
+
+
+
+examples_list = [['./test_images/TRAIN_00001.jpg' ,"handwritten"] ,
+                 ['./test_images/001.jpg','numeric'],
+                 ['./test_images/TEST_0019.jpg' ,"handwritten"],
+                 ['./test_images/005.jpg','numeric'],
+                 ['./test_images/007.jpg','numeric'],
+                 ['./test_images/VALIDATION_0011.jpg' ,"handwritten"],
+                 ['./test_images/VALIDATION_0022.jpg' ,"handwritten"],
+                 ['./test_images/062.jpg','numeric'],
+                 ['./test_images/119.jpg','numeric'],
+                 ['./test_images/150.jpg','numeric']
+                 ]
+
+demo = gr.Interface(fn=image_classifier, inputs=["image",
+                                                 gr.Radio(["handwritten", "numeric"], label="mode")],
+                     outputs="text",
+                    examples = examples_list )
+
+demo.launch(share = True)