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tc-ha
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Parent(s):
bc94088
Add application file
Browse files
app.py
ADDED
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import streamlit as st
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import torch
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from PIL import Image
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import json
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from tqdm import tqdm
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import hydra
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from transformers import AutoModelForQuestionAnswering, LayoutLMv2Processor, AutoTokenizer
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from data_loader.data_loaders import DataLoader
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from utils.util import predict_start_first
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class Config():
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def __init__(self):
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self.data_dir = "/opt/ml/input/data/"
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self.model = "layoutlmv2"
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self.device = "cpu"
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self.checkpoint = "microsoft/layoutlmv2-base-uncased"
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self.use_ocr_library = False
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self.debug = False
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self.batch_data = 1
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self.num_proc = 1
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self.shuffle = True
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self.lr = 5e-6
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self.seed = 42
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self.batch = 1
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self.max_len = 512
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self.epochs = 1000
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self.fuzzy = False
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self.model_name = ''
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config = Config()
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# Define function to make predictions
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def predict(config, model, image, question):
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processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
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encoding = processor(image, question, return_tensors="pt")
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# print(encoding.word_ids(i))
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# word_ids = [[-1 if id is None else id for id in encoding.word_ids(i)] for i in range(len(question))]
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# model
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with torch.no_grad():
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output = model(
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input_ids=encoding['input_ids'],
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attention_mask=encoding['attention_mask'],
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token_type_ids=encoding['token_type_ids'],
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bbox=encoding['bbox'], image=encoding['image']
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)
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predicted_start_idx, predicted_end_idx = predict_start_first(output)
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answer = processor.tokenizer.decode(encoding['input_ids'][0, predicted_start_idx[0]:predicted_end_idx[0]+1])
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# for batch_idx in range(1):
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# answer = ""
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# pred_start = predicted_start_idx[batch_idx]
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# pred_end = predicted_end_idx[batch_idx]
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# word_id = word_ids[batch_idx, pred_start]
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# for i in range(pred_start, pred_end + 1):
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# if word_id == word_ids[batch_idx, i]:
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# answer += processor.tokenizer.decode(encoding['input_ids'][batch_idx][i])
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# else:
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# answer += ' ' + processor.tokenizer.decode(encoding['input_ids'][batch_idx][i])
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# word_id = word_ids[batch_idx, i]
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# answer = answer.replace('##', '')
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# print(answer)
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return answer
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def main(config):
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hydra.core.global_hydra.GlobalHydra.instance().clear()
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# Load deep learning model
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checkpoint = ''
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model = AutoModelForQuestionAnswering.from_pretrained('microsoft/layoutlmv2-base-uncased').to(config.device)
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# model.load_state_dict(torch.load("model"))
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# Create Streamlit app
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st.title('Deep Learning Pipeline')
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st.write('Upload an image and ask a question to get a prediction')
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# Create file uploader and text input widgets
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uploaded_file = st.file_uploader("Choose an image", type=['jpg', 'jpeg', 'png'])
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question = st.text_input('Ask a question')
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# If file is uploaded, show the image
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if uploaded_file is not None:
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image = Image.open(uploaded_file).convert("RGB")
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st.image(image, caption='Uploaded Image', use_column_width=True)
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# If question is asked and file is uploaded, make a prediction
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if st.button('Get Prediction') and uploaded_file is not None and question != '':
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# Preprocess the image and question as needed
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# ...
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# Make the prediction
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with st.spinner('Predicting...'):
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output = predict(config, model, image, question)
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# Show the output
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st.write('Output:', output)
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if __name__ == '__main__':
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config = Config()
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main(config)
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