Update app.py

app.py

@@ -41,7 +41,7 @@ model = get_gemini_model()
 def load_data():
     try:
         path_alimentos = os.path.join('DATOS', 'alimentos_enriquecido.json') 
-        path_foodb_index = os.path.join('DATOS', 'foodb_index.json')
+        path_foodb_index = os.path.join('DATOS', 'foodb_index_smart.json')
         with open(path_alimentos, 'r', encoding='utf-8') as f:
             data_alimentos = json.load(f)
         foodb_index = {}
@@ -593,13 +593,12 @@ if st.session_state.search_results is not None:
             st.success(f"Hemos encontrado {len(results)} posible(s) causa(s) relacionada(s) con tu caso.")
         with col2:
             report_data = generate_report_text(st.session_state.user_query, results)
-            # --- CORRECCIÓN APLICADA AQUÍ ---
             st.download_button(
                 label="📄 Descargar Informe", 
                 data=report_data, 
                 file_name=f"informe_detective_{datetime.now().strftime('%Y%m%d')}.txt", 
                 mime="text/plain",
-                key="download_main_report" # Se añade una clave única
+                key="download_main_report"
             )
         
         st.subheader("Análisis de Relevancia de las Coincidencias")
@@ -617,45 +616,53 @@ if st.session_state.search_results is not None:
                 score_col2.metric("Puntos por Síntomas", f"{best_match_data['score']['symptoms']}")
                 score_col3.metric("PUNTUACIÓN TOTAL", f"{best_match_data['score']['total']}", delta="Máxima coincidencia")
             
+            # --- LÓGICA DEL POPOVER INTELIGENTE ---
             with col2:
                 st.write("") 
                 if foodb_index:
-                    with st.popover("🔬 Componentes moleculares"):
-                        st.info("Información de la base de datos FoodB (en inglés).")
+                    with st.popover("🔬 Componentes Moleculares Relevantes"):
+                        st.info("Compuestos en los alimentos mencionados que podrían estar relacionados con tus síntomas (datos de FoodB en inglés).")
+                        
                         user_foods_mentioned = st.session_state.entities.get("alimentos", [])
+                        user_symptoms = st.session_state.entities.get("sintomas", [])
                         
                         if not user_foods_mentioned:
                             st.warning("No se identificó un alimento específico para buscar.")
                         else:
-                            found_data = False
-                            displayed_foodb_keys = set()
+                            found_relevant_data = False
                             
+                            search_terms_en = []
                             for alimento_es in user_foods_mentioned:
-                                search_terms_en = []
                                 for key_es, value_en_list in FOOD_NAME_TO_FOODB_KEY.items():
                                     if key_es in alimento_es.lower():
                                         search_terms_en.extend(value_en_list)
-                                
-                                for term in set(search_terms_en):
-                                    for foodb_key, foodb_data in foodb_index.items():
-                                        if term in foodb_key and foodb_key not in displayed_foodb_keys:
-                                            found_data = True
-                                            displayed_foodb_keys.add(foodb_key)
-                                            with st.container(border=True):
-                                                st.subheader(f"Análisis de: {foodb_key.capitalize()}")
-                                                for item in foodb_data[:3]:
-                                                    st.write(f"**Compuesto:** {item['compound']}")
-                                                    st.write(f"**Efectos reportados:** {', '.join(item['effects'])}")
-                                                st.markdown("---")
                             
-                            if not found_data:
-                                st.warning("No se encontraron datos moleculares para los alimentos mencionados.")
-
+                            for foodb_key, compounds_data in foodb_index.items():
+                                if any(term in foodb_key.lower() for term in set(search_terms_en)):
+                                    
+                                    relevant_compounds = []
+                                    for item in compounds_data:
+                                        effects = item.get("effects", [])
+                                        if any(symptom.lower() in effect.lower() for symptom in user_symptoms for effect in effects):
+                                            relevant_compounds.append(item)
+                                    
+                                    if relevant_compounds:
+                                        found_relevant_data = True
+                                        with st.container(border=True):
+                                            st.subheader(f"Análisis de: {foodb_key.capitalize()}")
+                                            for item in relevant_compounds[:5]:
+                                                st.write(f"**Compuesto:** {item['compound']}")
+                                                relevant_effects = [eff for eff in item['effects'] if any(symptom.lower() in eff.lower() for symptom in user_symptoms)]
+                                                st.write(f"**Efectos Relevantes:** {', '.join(relevant_effects)}")
+                                            st.markdown("---")
+
+                            if not found_relevant_data:
+                                st.warning("No se encontraron compuestos moleculares directamente relacionados con tus síntomas para los alimentos mencionados.")
+            
             st.markdown("---")
 
             with st.container(border=True):
                 st.markdown("##### 🧠 Posibles Efectos Neuropsicológicos de los Componentes")
-                
                 user_foods = st.session_state.entities.get("alimentos", [])
                 relevant_compounds = set()
                 if user_foods:
@@ -665,7 +672,7 @@ if st.session_state.search_results is not None:
                 
                 found_neuro_effect = False
                 if relevant_compounds:
-                    for compound in sorted(list(relevant_compounds)): 
+                    for compound in sorted(list(relevant_compounds)):
                         if compound in INTEGRATED_NEURO_FOOD_MAP:
                             found_neuro_effect = True
                             effect_info = INTEGRATED_NEURO_FOOD_MAP[compound]