add requirement

app.py

@@ -1,117 +1,122 @@
 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
+
+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
         
-        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)