Delete app.py

app.py

@@ -1,140 +0,0 @@
-# Requirements.txt
-import gradio as gr
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from torch import cuda
-from utils import convert_ans_to_token, convert_ques_to_token, rotate, convert_token_to_ques, convert_token_to_answer
-from modeling import LaTr_for_pretraining, LaTr_for_finetuning, LaTrForVQA
-from dataset import load_json_file, get_specific_file, resize_align_bbox, get_tokens_with_boxes, create_features
-import torch.nn as nn
-from PIL import Image, ImageDraw
-import pytesseract
-import pandas as pd
-from tqdm.auto import tqdm
-import numpy as np
-import json
-import os
-
-
-# install PyTesseract
-os.system('pip install -q pytesseract')
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-# Default Library import
-
-
-# For the purpose of displaying the progress of map function
-tqdm.pandas()
-
-# Visualization libraries
-
-# Specific libraries of LaTr
-
-# Setting the hyperparameters as well as primary configurations
-
-PAD_TOKEN_BOX = [0, 0, 0, 0]
-max_seq_len = 512
-batch_size = 2
-target_size = (500, 384)
-t5_model = "t5-base"
-
-
-device = 'cuda' if cuda.is_available() else 'cpu'
-
-
-# Configuration for the model
-config = {
-    't5_model': 't5-base',
-    'vocab_size': 32128,
-    'hidden_state': 768,
-    'max_2d_position_embeddings': 1001,
-    'classes': 32128,  # number of tokens
-    'seq_len': 512
-}
-
-tokenizer = T5Tokenizer.from_pretrained(t5_model)
-latr = LaTrForVQA(config, max_steps=max_steps)
-url = 'https://www.kaggleusercontent.com/kf/99663112/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..tGHcmnLDazeyRNWAxV-KDQ.6unLNRwl7AyVy0Qz3ONE1m_mRNmgC-8VGyS61PdkSeBMV7PpG2B1cD5liuLlok5LQiYrGujrULdtIXKTqCUU_PA3MMSRhi1VKkGMdtrzJLMvzA4jxlWh_qak8P89w4ir4LENyuPCan24M0MOLXYjrm4d1iiy4Hg8pp2o5zWgs0OrVYoh_AJNazOD7pRIjLEAqnM-Pa0LSmvJkfN7j3Zn_Fu9jJ7Pq3Z0rWVtEb-PbeY06f9t-0QK6-JU8K2LdQjuBaCxjgB3BlufgFhKuhU3CZXsJitG7tDnwMSl4JImGfMmBntE2kn9-0dl_aANxaQd2Lsy8KGUDNAdQ2vBpowGQ0-tgDT_w7DpG6DzmUlmzIegqJF1-JyurCO0TrX_RatoPa7jGzuqA5vUT4263-MkoAlR0Xuulq4_pwGV-WnJsrcLuuDtEKFVsYjQvikWM3c9Arw0MsXchYCQkl_OZ6ZqYZ6TZrYxujHE2B6nHxu0F-5xj33vQ2ojaMpHtDplTnqCe4TdmzRWV6LhopfL4x1NXIXry8we4IqgPPwnIy3G2lZVR39nPmNR-8IGjbvweVr6Ci6y1COdbLR4JiTMVc_Nvf2glVKRjppTdcEwLv-j1YR8JsZpZvjaOEokrNkyCG7J0PLJAHlY8iX-pRdBG4vivbSHxnKl3Qppa689VH0RARpOsOBYv-IF-rM1nSmKq7Ci.tXi1B0oNQFlUtxesMcma3w/models/epoch=0-step=34602.ckpt'
-
-
-try:
-    latr = latr.load_from_checkpoint(url)
-    print("Checkpoint loaded successfully")
-except:
-    print("Checkpoint not loaded")
-    pass
-
-
-image = gr.inputs.Image(type="pil")
-question = gr.inputs.Textbox(label="Question")
-answer = gr.outputs.Textbox(label="Predicted answer")
-examples = [["remote.jpg", "what number is the button near the top left?"]]
-
-
-def answer_question(image, question):
-    image.save('sample_img.jpg')
-
-    # Extracting features from the image
-    img, boxes, tokenized_words = create_features(image_path='sample_img.jpg',
-                                                  tokenizer=tokenizer,
-                                                  target_size=target_size,
-                                                  max_seq_length=max_seq_length,
-                                                  use_ocr=True
-                                                  )
-
-    ## Converting the boxes as per the format required for model input
-    boxes = torch.as_tensor(boxes, dtype=torch.int32)
-    width = (boxes[:, 2] - boxes[:, 0]).view(-1, 1)
-    height = (boxes[:, 3] - boxes[:, 1]).view(-1, 1)
-    boxes = torch.cat([boxes, width, height], axis = -1)
-
-    ## Clamping the value,as some of the box values are out of bound
-    boxes[:, 0] = torch.clamp(boxes[:, 0], min = 0, max = 0)
-    boxes[:, 2] = torch.clamp(boxes[:, 2], min = 1000, max = 1000)
-    boxes[:, 4] = torch.clamp(boxes[:, 4], min = 1000, max = 1000)
-    
-    boxes[:, 1] = torch.clamp(boxes[:, 1], min = 0, max = 0)
-    boxes[:, 3] = torch.clamp(boxes[:, 3], min = 1000, max = 1000)
-    boxes[:, 5] = torch.clamp(boxes[:, 5], min = 1000, max = 1000)
-
-    ## Tensor tokenized words
-    tokenized_words = torch.as_tensor(tokenized_words, dtype=torch.int32)
-    
-    img = transforms.ToTensor()(img)
-    question = convert_ques_to_token(question = question, tokenizer = tokenizer)
-
-    ## Expanding the dimension for inference
-    img = img.unsqueeze(0)
-    boxes = boxes.unsqueeze(0)
-    tokenized_words = tokenized_words.unsqueeze(0)
-    question = question.unsqueeze(0)
-
-    encoding = {'img': img, 'boxes': boxes, 'tokenized_words': tokenized_words, 'question': question}
-    
-    with torch.no_grad():
-        logits = latr.forward(encoding)
-        logits = logits.squeeze(0)
-
-    _, preds = torch.max(logits, dim = 1)
-    preds = preds.detach().cpu()
-    mask = torch.clamp(preds, min = 0, max = 1)
-    last_non_zero_argument = (mask != 0).nonzero()[1][-1]
-    
-    predicted_ans = convert_token_to_ques(individual_ans_pred[:last_non_zero_argument], tokenizer)
-    return predicted_ans
-
-
-# Taken from here: https://huggingface.co/spaces/nielsr/vilt-vqa/blob/main/app.py
-title = "Interactive demo: laTr (Layout Aware Transformer) for VQA"
-description = "Gradio Demo for LaTr (Layout Aware Transformer),trained on TextVQA Dataset. To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.12494' target='_blank'>LaTr: Layout-aware transformer for scene-text VQA,a novel multimodal architecture for Scene Text Visual Question Answering (STVQA)</a> | <a href='https://github.com/uakarsh/latr' target='_blank'>Github Repo</a></p>"
-
-interface = gr.Interface(fn=answer_question,
-                         inputs=[image, question],
-                         outputs=answer,
-                         examples=examples,
-                         title=title,
-                         description=description,
-                         article=article,
-                         enable_queue=True)
-interface.launch(debug=True)