Update app.py

app.py

@@ -279,34 +279,69 @@ def find_best_matches_hybrid(entities, data):
     if not entities or not data: return []
     user_symptoms = set(sanitize_text(s) for s in entities.get("sintomas", []))
     user_foods = set(sanitize_text(f) for f in entities.get("alimentos", []))
+
+    # --- LISTA DE CONDICIONES RARAS (Centralizada para fácil mantenimiento) ---
+    # Cualquier condición en esta lista recibirá una penalización en su puntuación.
+    # El resto se considerará de probabilidad normal.
+    RARE_CONDITIONS = [
+        "Porfiria Aguda Intermitente (PAI).",
+        "Enfermedad de Refsum del Adulto.",
+        "Ataxia por Gluten.",
+        "Encefalopatía por Gluten.",
+        "Enfermedad de Wilson.",
+        "Aciduria Argininosuccínica.",
+        "Síndrome de Alagille."
+        # Añade aquí otras enfermedades raras a medida que las incorpores a tu JSON.
+    ]
+    
     candidate_terms = set(user_foods)
     for food in user_foods:
         if food in FOOD_TO_COMPOUND_MAP:
             candidate_terms.update(c.lower() for c in FOOD_TO_COMPOUND_MAP[food])
+
     results = []
     for entry in data:
         entry_compounds_text = sanitize_text(entry.get("compuesto_alimento", ""))
         food_match = any(term in entry_compounds_text for term in candidate_terms)
+        
         if not food_match:
             continue
+            
         score_details = {'food': 20, 'symptoms': 0}
+        
         entry_symptoms_keys = set(sanitize_text(s) for s in entry.get("sintomas_clave", []))
         symptom_score = 0
         matched_symptoms = []
         for user_symptom in user_symptoms:
             for key in entry_symptoms_keys:
                 if key in user_symptom or user_symptom in key:
-                    symptom_score += 30
+                    symptom_score += 30 
                     matched_symptoms.append(key)
                     break
+                    
         score_details['symptoms'] = symptom_score
-        score_details['total'] = score_details['food'] + score_details['symptoms']
+        
         if score_details['symptoms'] > 0:
+            base_score = score_details['food'] + score_details['symptoms']
+            
+            # --- LÓGICA DE PONDERACIÓN SIMPLIFICADA ---
+            condition_name = entry.get("condicion_asociada", "")
+            
+            if condition_name in RARE_CONDITIONS:
+                # Penalización fuerte para condiciones raras
+                final_score = base_score * 0.4 
+            else:
+                # Puntuación completa para el resto (comunes y poco comunes)
+                final_score = base_score * 1.0
+
+            score_details['total'] = int(final_score)
+            
             results.append({
                 'entry': entry, 
                 'score': score_details, 
                 'matched_symptoms': list(set(matched_symptoms))
             })
+
     if not results: return []
     return sorted(results, key=lambda x: x['score']['total'], reverse=True)
 
@@ -352,15 +387,27 @@ def generate_detailed_analysis(query, match):
         raise e
 
 def create_relevance_chart(results):
-    top_results = results[:5]
+    # Modificado para mostrar hasta 10 resultados en el gráfico
+    top_results = results[:10] 
+    
     condition_names = [re.sub(r'\(.*\)', '', res['entry']['condicion_asociada']).strip() for res in top_results]
     chart_data = {"Condición": condition_names, "Relevancia": [res['score']['total'] for res in top_results]}
     source = pd.DataFrame(chart_data)
+    
     chart = alt.Chart(source).mark_bar(color='#1f77b4').encode(
         x=alt.X('Relevancia:Q', title='Puntuación de Relevancia'),
         y=alt.Y('Condición:N', sort='-x', title='', axis=alt.Axis(labelLimit=300)),
         tooltip=[alt.Tooltip('Condición:N', title='Condición'), alt.Tooltip('Relevancia:Q', title='Puntuación')]
-    ).properties(title='Principales Coincidencias según tu Caso').configure_axis(labelFontSize=12, titleFontSize=14).configure_title(fontSize=16, anchor='start')
+    ).properties(
+        title='Principales Coincidencias según tu Caso'
+    ).configure_axis(
+        labelFontSize=12, 
+        titleFontSize=14
+    ).configure_title(
+        fontSize=16, 
+        anchor='start'
+    )
+    
     return chart
     
 def generate_report_text(query, results):