app file

app.py

@@ -0,0 +1,118 @@
+import streamlit as st
+import torch
+import spacy
+# from spacy.lang.en import English
+# from utils import spacy_function, make_predictions, example_input
+
+import sys
+sys.path.insert(1, 'PyTorch')
+from Dataset import SkimlitDataset
+from Embeddings import get_embeddings
+from Model import SkimlitModel
+from Tokenizer import Tokenizer
+from LabelEncoder import LabelEncoder
+from MakePredictions import make_skimlit_predictions
+from RandomAbstract import Choose_Random_text
+
+MODEL_PATH = 'PyTorch/utils/skimlit-model-final-1.pt'
+TOKENIZER_PATH = 'PyTorch/utils/tokenizer.json'
+LABEL_ENOCDER_PATH = "PyTorch/utils/label_encoder.json"
+EMBEDDING_FILE_PATH = 'PyTorch/utils/glove.6B.300d.txt'
+
+@st.cache()
+def create_utils(model_path, tokenizer_path, label_encoder_path, embedding_file_path):
+    tokenizer = Tokenizer.load(fp=tokenizer_path)
+    label_encoder = LabelEncoder.load(fp=label_encoder_path)
+    embedding_matrix = get_embeddings(embedding_file_path, tokenizer, 300)
+    model = SkimlitModel(embedding_dim=300, vocab_size=len(tokenizer), hidden_dim=128, n_layers=3, linear_output=128, num_classes=len(label_encoder), pretrained_embeddings=embedding_matrix)
+    model.load_state_dict(torch.load(model_path, map_location='cpu'))
+    print(model)
+    return model, tokenizer, label_encoder
+
+def model_prediction(abstract, model, tokenizer, label_encoder):
+    objective = ''
+    background = ''
+    method = ''
+    conclusion = ''
+    result = ''
+
+    lines, pred = make_skimlit_predictions(abstract, model, tokenizer, label_encoder)
+    # pred, lines = make_predictions(abstract)
+
+    for i, line in enumerate(lines):
+        if pred[i] == 'OBJECTIVE':
+            objective = objective + line
+        
+        elif pred[i] == 'BACKGROUND':
+            background = background + line
+        
+        elif pred[i] == 'METHODS':
+            method = method + line
+        
+        elif pred[i] == 'RESULTS':
+            result = result + line
+        
+        elif pred[i] == 'CONCLUSIONS':
+            conclusion = conclusion + line
+
+    return objective, background, method, conclusion, result
+
+
+
+def main():
+    
+    st.set_page_config(
+        page_title="SkimLit",
+        page_icon="📄",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+
+    st.title('SkimLit📄🔥')
+    st.caption('An NLP model to classify abstract sentences into the role they play (e.g. objective, methods, results, etc..) to enable researchers to skim through the literature and dive deeper when necessary.')
+    
+    # creating model, tokenizer and labelEncoder
+    # if PREP_MODEL:
+    #     skimlit_model, tokenizer, label_encoder = create_utils(MODEL_PATH, TOKENIZER_PATH, LABEL_ENOCDER_PATH, EMBEDDING_FILE_PATH)
+    #     PREP_MODEL = False
+    
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.write('#### Entre Abstract Here !!')
+        abstract = st.text_area(label='', height=200)
+
+        agree = st.checkbox('Show Example Abstract')
+        predict = st.button('Extract !')
+        
+        if agree:
+            example_input = Choose_Random_text()
+            st.info(example_input)
+    
+    # make prediction button logic
+    if predict:
+        with col2:
+            with st.spinner('Wait for prediction....'):
+                skimlit_model, tokenizer, label_encoder = create_utils(MODEL_PATH, TOKENIZER_PATH, LABEL_ENOCDER_PATH, EMBEDDING_FILE_PATH)
+                objective, background, methods, conclusion, result = model_prediction(abstract, skimlit_model, tokenizer, label_encoder)
+
+                st.markdown(f'### Objective : ')
+                st.info(objective)
+                # st.write(f'{objective}')
+                st.markdown(f'### Background : ')
+                st.info(background)
+                # st.write(f'{background}')
+                st.markdown(f'### Methods : ')
+                st.info(methods)
+                # st.write(f'{methods}')
+                st.markdown(f'### Result : ')
+                st.info(result)
+                # st.write(f'{result}')
+                st.markdown(f'### Conclusion : ')
+                st.info(conclusion)
+                # st.write(f'{conclusion}')
+
+
+
+if __name__=='__main__': 
+    main()