Update app.py

app.py

@@ -6,6 +6,7 @@ import chromadb
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 import requests
+from itertools import combinations
 # Define FastAPI app
 app = FastAPI()
 
@@ -120,34 +121,50 @@ class SelectedSymptomsQuery(BaseModel):
 
 @app.post("/find_disease")
 def find_disease(query: SelectedSymptomsQuery):
-    selected_symptoms = query.selected_symptoms
-    # Filter diseases that match at least one of the selected symptoms
-    matching_diseases = df[df['Symptoms'].apply(lambda x: any(s in x for s in selected_symptoms))]
-
-    # Sort diseases by the number of matching symptoms in descending order
-    matching_diseases['match_count'] = matching_diseases['Symptoms'].apply(lambda x: sum(s in selected_symptoms for s in x))
-    matching_diseases = matching_diseases.sort_values(by='match_count', ascending=False)
-
-    # Create a list of disease information
+    SelectedSymptoms = query.selected_symptoms
+    all_selected_symptoms.extend(SelectedSymptoms)
+    
     disease_list = []
-    max_match_count_disease = None
-    max_match_count = -1
-
-    for _, row in matching_diseases.iterrows():
-        disease_info = {
-            'Disease': row['Name'],
-            'Symptoms': row['Symptoms'],
-            'Treatments': row['Treatments'],
-            'MatchCount': row['match_count']
-        }
-        disease_list.append(disease_info)
-
-        # Check if this disease has the maximum match count
-        if row['match_count'] > max_match_count:
-            max_match_count = row['match_count']
-            max_match_count_disease = disease_info
-
-    return {"disease_list": disease_list, "max_match_count_disease": max_match_count_disease}
+    symptoms_list = []
+    unique_symptoms_list = []
+    
+    # Combine all the symptoms we already know (all_symptoms + selected symptoms)
+    known_symptoms = {symptom.lower() for symptom_set in all_symptoms for symptom in symptom_set}
+    known_symptoms.update([symptom.lower() for symptom in SelectedSymptoms])
+    
+    # Generate combinations of symptoms from all_symptoms and selected symptoms
+    for symptoms_set in all_symptoms:
+        for i in range(1, len(symptoms_set) + 1):  # Generate combinations with all lengths
+            for symptom_combination in combinations(symptoms_set, i):
+                temp = list(symptom_combination) + SelectedSymptoms  # Combine with selected symptoms
+                
+                # Search for diseases that match the combination
+                matching_diseases = df[df['Symptoms'].apply(lambda x: all(s in x for s in temp))]
+                
+                for _, row in matching_diseases.iterrows():
+                    disease_info = {
+                        'Disease': row['Name'],
+                        'Symptoms': row['Symptoms'],
+                        'Treatments': row['Treatments']
+                    }
+                    disease_list.append(disease_info)
+                    
+                    symptoms_info = row['Symptoms']
+                    symptoms_list.append(symptoms_info)
+    
+    # Flatten the list of symptoms and remove duplicates, excluding known symptoms
+    for symptoms in symptoms_list:
+        for symptom in symptoms:
+            symptom_lower = symptom.lower()
+            if symptom_lower not in known_symptoms and symptom_lower not in unique_symptoms_list:
+                unique_symptoms_list.append(symptom_lower)
+    
+    return {
+        "unique_symptoms_list": unique_symptoms_list,
+        "all_selected_symptoms": all_selected_symptoms,
+        "all_symptoms": all_symptoms,
+        "disease_list": disease_list
+    }
 class DiseaseListQuery(BaseModel):
     disease_list: list