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| from transformers import pipeline | |
| import streamlit as st | |
| col_left, col_middle, col_right= st.columns(3) | |
| col_middle.title("MedBotDash") | |
| st.divider() | |
| def load_model(): | |
| model = pipeline("token-classification", | |
| model="Clinical-AI-Apollo/Medical-NER", | |
| aggregation_strategy='simple') | |
| return model | |
| pipe = load_model() | |
| st.subheader("Enter Detailed Description of your condition") | |
| condition = st.text_input("enter the condition") | |
| data = pipe(condition) | |
| #st.write(data) | |
| severity = [] | |
| sign_symptom = [] | |
| biological_structure = [] | |
| age = [] | |
| sex = [] | |
| lab_value = [] | |
| # Iterate through the data and append words to their respective lists based on entity_group | |
| for entity in data: | |
| if entity['entity_group'] == 'SEVERITY': | |
| severity.append(entity['word']) | |
| elif entity['entity_group'] == 'SIGN_SYMPTOM': | |
| sign_symptom.append(entity['word']) | |
| elif entity['entity_group'] == 'BIOLOGICAL_STRUCTURE': | |
| biological_structure.append(entity['word']) | |
| elif entity['entity_group'] == 'AGE': | |
| age.append(entity['word']) | |
| elif entity['entity_group'] == 'SEX': | |
| sex.append(entity['word']) | |
| elif entity['entity_group'] == 'LAB_VALUE': | |
| lab_value.append(entity['word']) | |
| col1, col2= st.columns(2) | |
| col1.metric("Age", age[0] if age else 'NA') | |
| col2.metric("Sex", sex[0] if sex else 'NA') | |
| st.divider() | |
| sign_symptom = set(sign_symptom) | |
| severity = set(severity) | |
| biological_structure = set(biological_structure) | |
| age = set(age) | |
| sex = set(age) | |
| lab_value = set(lab_value) | |
| tab1, tab2, tab3 = st.tabs(["Signs", "Biological Structure", "Severity"]) | |
| #st.subheader("Signs") | |
| with tab1: | |
| for sign in sign_symptom: | |
| st.text(sign) | |
| #st.subheader("Severity") | |
| with tab2: | |
| for bio in biological_structure: | |
| st.text(bio) | |
| with tab3: | |
| for severity in severity: | |
| st.text(severity) | |
| #st.subheader("Biological Structure") | |