Update app.py

app.py

@@ -51,6 +51,36 @@ def setup_spell_checker():
                              term_index=0, count_index=1)
     return sym_spell
 
+# ----------------------
+# Severity Analysis
+# ----------------------
+SEVERITY_RANK = {
+    'vomiting': 3, 'nausea': 3, 'diarrhea': 3,
+    'dizziness': 2, 'headache': 2, 'palpitations': 2,
+    'rash': 1, 'itching': 1, 'fatigue': 1
+}
+
+def severity_score(side_effects):
+    return sum(SEVERITY_RANK.get(effect.strip().lower(), 0) 
+              for effect in side_effects.split(',') if effect.strip())
+
+# ----------------------
+# Drug Comparison
+# ----------------------
+def compare_drugs(df, drug1, drug2):
+    try:
+        d1 = df[df['name'].str.lower() == drug1.lower()].iloc[0]
+        d2 = df[df['name'].str.lower() == drug2.lower()].iloc[0]
+        
+        comparison = pd.DataFrame({
+            'Attribute': ['Uses', 'Substitutes', 'Side Effects', 'Therapeutic Class'],
+            drug1: [d1['uses'], d1['substitutes'], d1['side_effects'], d1['Therapeutic Class']],
+            drug2: [d2['uses'], d2['substitutes'], d2['side_effects'], d2['Therapeutic Class']]
+        })
+        return comparison
+    except IndexError:
+        return pd.DataFrame()
+
 # ----------------------
 # Streamlit App
 # ----------------------
@@ -62,13 +92,14 @@ def main():
     model, faiss_index = setup_faiss(df)
     sym_spell = setup_spell_checker()
     
-    # User input
+    # User input section
     query = st.text_input("Describe your symptoms or medical need:")
     therapeutic_class = st.selectbox(
         "Filter by Therapeutic Class (optional):",
         ['All'] + sorted(df['Therapeutic Class'].dropna().unique().tolist())
     )
     
+    # Process query and show results
     if query:
         # Spelling correction
         suggestions = sym_spell.lookup(query, Verbosity.CLOSEST, max_edit_distance=2)
@@ -80,15 +111,19 @@ def main():
         query_embedding = model.encode([query])
         D, I = faiss_index.search(query_embedding, k=5)
         
-        # Filter results
+        # Process results
         results = df.iloc[I[0]].copy()
         if therapeutic_class != 'All':
             results = results[results['Therapeutic Class'] == therapeutic_class]
         
+        # Add severity analysis
+        results['severity'] = results['side_effects'].apply(severity_score)
+        results = results.sort_values('severity', ascending=True)
+        
         # Display results
         st.subheader("Recommended Medicines")
         for _, row in results.iterrows():
-            with st.expander(f"💊 {row['name']}"):
+            with st.expander(f"💊 {row['name']} (Severity: {row['severity']})"):
                 cols = st.columns(3)
                 cols[0].write(f"**Uses:** {row['uses']}")
                 cols[1].write(f"**Substitutes:** {row['substitutes']}")
@@ -98,5 +133,24 @@ def main():
                 cols2[0].write(f"Therapeutic Class: {row['Therapeutic Class']}")
                 cols2[1].write(f"Habit Forming: {row['Habit Forming']}")
 
+    # Drug comparison section
+    st.subheader("🔍 Drug Comparison Tool")
+    col1, col2 = st.columns(2)
+    drug_list = df['name'].unique().tolist()
+    
+    with col1:
+        drug1 = st.selectbox("Select first drug:", drug_list, index=0)
+    with col2:
+        drug2 = st.selectbox("Select second drug:", drug_list, index=1 if len(drug_list) > 1 else 0)
+    
+    comparison_df = compare_drugs(df, drug1, drug2)
+    if not comparison_df.empty:
+        st.table(comparison_df.style.set_properties(**{
+            'white-space': 'pre-wrap',
+            'text-align': 'left'
+        }))
+    else:
+        st.warning("One or both selected drugs not found in database")
+
 if __name__ == "__main__":
     main()