add requirement

app.py

@@ -1,122 +1,122 @@
-import streamlit as st
+# import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# x = st.slider('Select a value')
+# st.write(x, 'squared is', x * x)
 
-# import streamlit as st
-# import torch
-# from PIL import Image
-# import json
-# from tqdm import tqdm
-
-# from transformers import AutoModelForQuestionAnswering, LayoutLMv2Processor, AutoTokenizer
-
-# 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
+import streamlit as st
+import torch
+from PIL import Image
+import json
+from tqdm import tqdm
+
+from transformers import AutoModelForQuestionAnswering, LayoutLMv2Processor, AutoTokenizer
+
+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.lr = 5e-6
+        self.seed = 42
+        self.batch = 1
+        self.max_len = 512
+        self.epochs = 1000
         
-#         self.fuzzy = False
-#         self.model_name = ''
+        self.fuzzy = False
+        self.model_name = ''
         
-# config = Config()
+config = Config()
 
-# def predict_start_first(outputs):
-#     start_logits = outputs.start_logits
-#     end_logits = outputs.end_logits
+def predict_start_first(outputs):
+    start_logits = outputs.start_logits
+    end_logits = outputs.end_logits
     
-#     predicted_start_idx_list = []
-#     predicted_end_idx_list = []
+    predicted_start_idx_list = []
+    predicted_end_idx_list = []
     
-#     start_position = start_logits.argmax(1)
+    start_position = start_logits.argmax(1)
 
-#     for i in range(len(start_logits)):
+    for i in range(len(start_logits)):
         
-#         start = start_position[i]
-#         predicted_start_idx_list.append(start)
-#         max_score = -float('inf')
-#         predicted_end_idx = 0
+        start = start_position[i]
+        predicted_start_idx_list.append(start)
+        max_score = -float('inf')
+        predicted_end_idx = 0
         
-#         for end in range(start, len(end_logits[i])):
-#             score = end_logits[i][end]
-#             if score > max_score:
-#                 max_score = score
-#                 predicted_end_idx = end
+        for end in range(start, len(end_logits[i])):
+            score = end_logits[i][end]
+            if score > max_score:
+                max_score = score
+                predicted_end_idx = end
                 
-#         predicted_end_idx_list.append(predicted_end_idx)
+        predicted_end_idx_list.append(predicted_end_idx)
     
-#     return predicted_start_idx_list, predicted_end_idx_list
+    return predicted_start_idx_list, predicted_end_idx_list
 
-# # Define function to make predictions
-# def predict(config, model, image, question):
+# 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")
+    processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+    encoding = processor(image, question, return_tensors="pt")
     
-#     # 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']
-#         )
+    # 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)
+    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])
+    answer = processor.tokenizer.decode(encoding['input_ids'][0, predicted_start_idx[0]:predicted_end_idx[0]+1])
       
-#     return answer
+    return answer
 
-# def main(config):
+def main(config):
 
-#     # Load deep learning model
-#     checkpoint = ''
-#     model = AutoModelForQuestionAnswering.from_pretrained('microsoft/layoutlmv2-base-uncased').to(config.device)
-#     # model.load_state_dict(torch.load("model")) 
+    # 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 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')
+    # 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 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
-#         # ...
+    # 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)
+        # Make the prediction
+        with st.spinner('Predicting...'):
+            output = predict(config, model, image, question)
 
-#         # Show the output
-#         st.write('Output:', output)
+        # Show the output
+        st.write('Output:', output)
 
 
-# if __name__ == '__main__':
-#     config = Config()
-#     main(config)
+if __name__ == '__main__':
+    config = Config()
+    main(config)
 
 
     

requirements.txt

@@ -1,9 +1,9 @@
-# datasets
-# editdistance
-# numpy
-# pandas
-# Pillow
-# torch
-# tqdm
-# transformers
-# thefuzz
+datasets
+editdistance
+numpy
+pandas
+Pillow
+torch
+tqdm
+transformers
+thefuzz