Add application file

app.py

@@ -0,0 +1,117 @@
+import streamlit as st
+import torch
+from PIL import Image
+import json
+from tqdm import tqdm
+
+import hydra
+from transformers import AutoModelForQuestionAnswering, LayoutLMv2Processor, AutoTokenizer
+from data_loader.data_loaders import DataLoader
+from utils.util import predict_start_first
+
+class Config():
+    def __init__(self):
+        self.data_dir = "/opt/ml/input/data/"
+        self.model = "layoutlmv2"
+        self.device = "cpu"
+        self.checkpoint = "microsoft/layoutlmv2-base-uncased"
+        self.use_ocr_library = False
+        self.debug = False
+        self.batch_data = 1
+        self.num_proc = 1
+        self.shuffle = True
+        
+        self.lr = 5e-6
+        self.seed = 42
+        self.batch = 1
+        self.max_len = 512
+        self.epochs = 1000
+        
+        self.fuzzy = False
+        self.model_name = ''
+        
+config = Config()
+
+# Define function to make predictions
+def predict(config, model, image, question):
+    
+    processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+    encoding = processor(image, question, return_tensors="pt")
+    
+    # print(encoding.word_ids(i))
+    
+    # word_ids = [[-1 if id is None else id for id in encoding.word_ids(i)] for i in range(len(question))]
+    
+    # model
+    with torch.no_grad():
+        output = model(
+            input_ids=encoding['input_ids'], 
+            attention_mask=encoding['attention_mask'],
+            token_type_ids=encoding['token_type_ids'],
+            bbox=encoding['bbox'], image=encoding['image']
+        )
+    
+    predicted_start_idx, predicted_end_idx = predict_start_first(output)
+    
+    answer = processor.tokenizer.decode(encoding['input_ids'][0, predicted_start_idx[0]:predicted_end_idx[0]+1])
+      
+    # for batch_idx in range(1):
+    #     answer     = ""
+    #     pred_start = predicted_start_idx[batch_idx]
+    #     pred_end   = predicted_end_idx[batch_idx]
+    #     word_id    = word_ids[batch_idx, pred_start]
+    #     for i in range(pred_start, pred_end + 1):
+    #         if word_id == word_ids[batch_idx, i]:
+    #             answer += processor.tokenizer.decode(encoding['input_ids'][batch_idx][i])
+    #         else:
+    #             answer += ' ' + processor.tokenizer.decode(encoding['input_ids'][batch_idx][i])
+    #             word_id = word_ids[batch_idx, i]
+
+    #     answer = answer.replace('##', '')
+    
+    #     print(answer)
+
+    return answer
+
+def main(config):
+    hydra.core.global_hydra.GlobalHydra.instance().clear()
+
+    # Load deep learning model
+    checkpoint = ''
+    model = AutoModelForQuestionAnswering.from_pretrained('microsoft/layoutlmv2-base-uncased').to(config.device)
+    # model.load_state_dict(torch.load("model")) 
+
+    # Create Streamlit app
+    st.title('Deep Learning Pipeline')
+    st.write('Upload an image and ask a question to get a prediction')
+
+    # Create file uploader and text input widgets
+    uploaded_file = st.file_uploader("Choose an image", type=['jpg', 'jpeg', 'png'])
+    question = st.text_input('Ask a question')
+
+    # If file is uploaded, show the image
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # If question is asked and file is uploaded, make a prediction
+    if st.button('Get Prediction') and uploaded_file is not None and question != '':
+        # Preprocess the image and question as needed
+        # ...
+
+        # Make the prediction
+        with st.spinner('Predicting...'):
+            output = predict(config, model, image, question)
+
+        # Show the output
+        st.write('Output:', output)
+
+
+if __name__ == '__main__':
+    config = Config()
+    main(config)
+
+
+    
+
+