first commit

app.py

@@ -0,0 +1,17 @@
+import gradio as gr
+import model
+
+
+input_1 = gr.inputs.Textbox(lines=1, placeholder='Patient History', default="", label=None, optional=False)
+input_2 = gr.inputs.Textbox(lines=1, placeholder='Feature Text', default="", label=None, optional=False)
+
+output_1 = gr.outputs.Textbox(self, type="auto", label=None)
+
+gr.Interface(
+    model.get_predictions, 
+    inputs=[input_1, input_2], 
+    outputs=[output_1],
+    title='Identify Key Phrases in Patient Notes from Medical Licensing Exams',
+    theme='dark',
+)
+gr.launch()

app_stream.py

@@ -0,0 +1,14 @@
+import streamlit as st
+import model
+
+st.title('Identify Key Phrases in Patient Notes from Medical Licensing Exams')
+
+pn_history = st.text_area("Patient History")
+feature_text = st.text_input('Feature Text')
+
+label='submit'
+if st.button(label):
+
+    if pn_history and feature_text != '':
+        pred = model.get_predictions(pn_history, feature_text)
+        st.write(pred)

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6cd45fa27f18598291557baea3fcafe14c0132bb506c4e2e23b294779daa5a5
+size 498669869

model.py

@@ -6,7 +6,7 @@ from tqdm.notebook import tqdm, trange
 
 import torch
 from torch import nn
-import transformers
+# import transformers
 from transformers import AutoModel, AutoTokenizer, AutoConfig
 
 
@@ -237,14 +237,15 @@ def predict_location_preds(tokenizer, model, feature_text, pn_history):
 
 def get_predictions(feature_text, pn_history):
     location_preds, pred_string = predict_location_preds(tokenizer, model, [feature_text], [pn_history])
-    print(pred_string)
+    # print(pred_string)
+    return pred_string
 
 tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_path'])
-path = '../input/nbme-training-biomed-roberta-base/best_model_0.bin'
+path = 'model.pth'
 
 model = NBMEModel().to(config['device'])
 model.load_state_dict(torch.load(path, map_location=torch.device(config['device']))['model'])
-model.eval();
+model.eval()
 
 # input_text = create_sample_test()
 # feature_text = input_text.feature_text[0]

requirements.txt

@@ -0,0 +1,6 @@
+gc
+numpy
+pandas
+torch==1.9.1+cpu
+transformers==4.12.5
+tqdm