Create app.py

app.py

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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()
+
+@st.cache_resource()
+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")
+
+
+