Update app.py

app.py

@@ -136,171 +136,42 @@ CONDITION_SYNONYMS = {
 
 FOOD_NAME_TO_FOODB_KEY = {
     # --- CEREALES Y GRANOS ---
-    "alforfón": ["buckwheat"],
-    "arroz": ["rice"],
-    "avena": ["oat", "oats"],
-    "cebada": ["barley"],
-    "centeno": ["rye"],
-    "galleta": ["cookie", "biscuit"],
-    "maíz": ["corn", "maize"],
-    "pan": ["bread"],
-    "pasta": ["pasta"],
-    "pizza": ["pizza"],
-    "quinoa": ["quinoa"],
-    "trigo": ["wheat"],
-    "trigo sarraceno": ["buckwheat"],
-
+    "alforfón": ["buckwheat"], "arroz": ["rice"], "avena": ["oat", "oats"], "cebada": ["barley"], "centeno": ["rye"], "galleta": ["cookie", "biscuit"], "maíz": ["corn", "maize"], "pan": ["bread"], "pasta": ["pasta"], "pizza": ["pizza"], "quinoa": ["quinoa"], "trigo": ["wheat"], "trigo sarraceno": ["buckwheat"],
     # --- LÁCTEOS Y DERIVADOS ---
-    "crema": ["cream"],
-    "helado": ["ice cream"],
-    "leche": ["milk"],
-    "mantequilla": ["butter"],
-    "queso": ["cheese"],
-    "yogur": ["yogurt", "yoghurt"],
-
+    "crema": ["cream"], "helado": ["ice cream"], "leche": ["milk"], "mantequilla": ["butter"], "queso": ["cheese"], "yogur": ["yogurt", "yoghurt"],
     # --- VEGETALES ---
-    "acelga": ["chard", "swiss chard"],
-    "ajo": ["garlic"],
-    "alcachofa": ["artichoke"],
-    "apio": ["celery"],
-    "berenjena": ["eggplant", "aubergine"],
-    "brócoli": ["broccoli"],
-    "calabacín": ["zucchini", "courgette"],
-    "calabaza": ["pumpkin", "squash"],
-    "cebolla": ["onion"],
-    "champiñón": ["mushroom"],
-    "col": ["cabbage"],
-    "coliflor": ["cauliflower"],
-    "edamame": ["edamame"],
-    "espárrago": ["asparagus"],
-    "espinaca": ["spinach"],
-    "garbanzo": ["chickpea"],
-    "guisante": ["pea", "peas"],
-    "frijol": ["bean", "beans"],
-    "lenteja": ["lentil"],
-    "patata": ["potato"],
-    "pepino": ["cucumber"],
-    "pimiento": ["bell pepper", "pepper"],
-    "remolacha": ["beet", "beetroot"],
-    "repollo": ["cabbage"],
-    "seta": ["mushroom"],
-    "soja": ["soy", "soybean"],
-    "tofu": ["tofu"],
-    "tomate": ["tomato"],
-    "zanahoria": ["carrot"],
-
+    "acelga": ["chard", "swiss chard"], "ajo": ["garlic"], "alcachofa": ["artichoke"], "apio": ["celery"], "berenjena": ["eggplant", "aubergine"], "brócoli": ["broccoli"], "calabacín": ["zucchini", "courgette"], "calabaza": ["pumpkin", "squash"], "cebolla": ["onion"], "champiñón": ["mushroom"], "col": ["cabbage"], "coliflor": ["cauliflower"], "edamame": ["edamame"], "espárrago": ["asparagus"], "espinaca": ["spinach"], "garbanzo": ["chickpea"], "guisante": ["pea", "peas"], "frijol": ["bean", "beans"], "lenteja": ["lentil"], "patata": ["potato"], "pepino": ["cucumber"], "pimiento": ["bell pepper", "pepper"], "remolacha": ["beet", "beetroot"], "repollo": ["cabbage"], "seta": ["mushroom"], "soja": ["soy", "soybean"], "tofu": ["tofu"], "tomate": ["tomato"], "zanahoria": ["carrot"],
     # --- FRUTAS ---
-    "aguacate": ["avocado"],
-    "albaricoque": ["apricot"],
-    "arándano": ["blueberry"],
-    "cereza": ["cherry"],
-    "ciruela": ["plum"],
-    "dátil": ["date"],
-    "frambuesa": ["raspberry"],
-    "fresa": ["strawberry"],
-    "higo": ["fig"],
-    "kiwi": ["kiwi", "kiwifruit"],
-    "limón": ["lemon"],
-    "mandarina": ["tangerine", "mandarin"],
-    "mango": ["mango"],
-    "manzana": ["apple"],
-    "melocotón": ["peach"],
-    "melón": ["melon", "cantaloupe"],
-    "mora": ["blackberry"],
-    "naranja": ["orange"],
-    "nectarina": ["nectarine"],
-    "papaya": ["papaya"],
-    "pera": ["pear"],
-    "piña": ["pineapple"],
-    "plátano": ["banana"],
-    "pomelo": ["grapefruit"],
-    "sandía": ["watermelon"],
-    "uva": ["grape"],
-
+    "aguacate": ["avocado"], "albaricoque": ["apricot"], "arándano": ["blueberry"], "cereza": ["cherry"], "ciruela": ["plum"], "dátil": ["date"], "frambuesa": ["raspberry"], "fresa": ["strawberry"], "higo": ["fig"], "kiwi": ["kiwi", "kiwifruit"], "limón": ["lemon"], "mandarina": ["tangerine", "mandarin"], "mango": ["mango"], "manzana": ["apple"], "melocotón": ["peach"], "melón": ["melon", "cantaloupe"], "mora": ["blackberry"], "naranja": ["orange"], "nectarina": ["nectarine"], "papaya": ["papaya"], "pera": ["pear"], "piña": ["pineapple"], "plátano": ["banana"], "pomelo": ["grapefruit"], "sandía": ["watermelon"], "uva": ["grape"],
     # --- PROTEÍNAS (CARNES, PESCADOS, HUEVOS) ---
-    "anchoa": ["anchovy", "anchovies"],
-    "atún": ["tuna"],
-    "camarón": ["shrimp", "prawn"],
-    "carne": ["meat", "beef", "pork", "lamb"],
-    "cerdo": ["pork"],
-    "cordero": ["lamb"],
-    "gamba": ["shrimp", "prawn"],
-    "huevo": ["egg"],
-    "marisco": ["shellfish", "seafood"],
-    "pavo": ["turkey"],
-    "pescado": ["fish"],
-    "pollo": ["chicken"],
-    "salchicha": ["sausage"],
-    "salmón": ["salmon"],
-    "sardina": ["sardine"],
-    "ternera": ["beef", "veal"],
-
+    "anchoa": ["anchovy", "anchovies"], "atún": ["tuna"], "camarón": ["shrimp", "prawn"], "carne": ["meat", "beef", "pork", "lamb"], "cerdo": ["pork"], "cordero": ["lamb"], "gamba": ["shrimp", "prawn"], "huevo": ["egg"], "marisco": ["shellfish", "seafood"], "pavo": ["turkey"], "pescado": ["fish"], "pollo": ["chicken"], "salchicha": ["sausage"], "salmón": ["salmon"], "sardina": ["sardine"], "ternera": ["beef", "veal"],
     # --- FRUTOS SECOS Y SEMILLAS ---
-    "almendra": ["almond"],
-    "anacardo": ["cashew"],
-    "avellana": ["hazelnut"],
-    "cacahuete": ["peanut"],
-    "chía": ["chia", "chia seed"],
-    "lino": ["flax", "flaxseed", "linseed"],
-    "nuez": ["walnut"],
-    "pistacho": ["pistachio"],
-    "sésamo": ["sesame", "sesame seed"],
-
+    "almendra": ["almond"], "anacardo": ["cashew"], "avellana": ["hazelnut"], "cacahuete": ["peanut"], "chía": ["chia", "chia seed"], "lino": ["flax", "flaxseed", "linseed"], "nuez": ["walnut"], "pistacho": ["pistachio"], "sésamo": ["sesame", "sesame seed"],
     # --- BEBIDAS, DULCES Y CONDIMENTOS ---
-    "aceituna": ["olive"],
-    "café": ["coffee"],
-    "caldo": ["broth", "stock"],
-    "cerveza": ["beer"],
-    "chocolate": ["chocolate"],
-    "jengibre": ["ginger"],
-    "cúrcuma": ["turmeric"],
-    "miel": ["honey"],
-    "mostaza": ["mustard"],
-    "té": ["tea"],
-    "vinagre": ["vinegar"],
-    "vino": ["wine", "red wine", "white wine"]
+    "aceituna": ["olive"], "café": ["coffee"], "caldo": ["broth", "stock"], "cerveza": ["beer"], "chocolate": ["chocolate"], "jengibre": ["ginger"], "cúrcuma": ["turmeric"], "miel": ["honey"], "mostaza": ["mustard"], "té": ["tea"], "vinagre": ["vinegar"], "vino": ["wine", "red wine", "white wine"]
 }
 
-# --- AÑADE ESTA FUNCIÓN NUEVA EN TU CÓDIGO ---
-
 def reinforce_entities_with_keywords(entities, query, food_map):
-    """
-    Revisa la salida de la IA y añade alimentos clave si fueron omitidos.
-    Esta es una red de seguridad contra las fallas de extracción de la IA.
-    """
     if not entities:
         entities = {"alimentos": [], "sintomas": []}
-    
     query_sanitized = sanitize_text(query)
     current_foods = entities.get("alimentos", [])
-    
-    # Si la IA ya encontró alimentos, los sanitizamos para la comparación
     current_foods_sanitized = {sanitize_text(f) for f in current_foods}
-    
-    # Iteramos por nuestra lista de alimentos conocidos
     for food_keyword in food_map.keys():
-        # Si una palabra clave de alimento está en la consulta...
         if food_keyword in query_sanitized:
-            # ...y la IA NO la incluyó en su lista...
             if food_keyword not in current_foods_sanitized:
-                # ...la añadimos nosotros.
                 logger.info(f"Red de seguridad: La IA omitió '{food_keyword}', añadiéndolo manualmente.")
                 current_foods.append(food_keyword)
-                
-    entities["alimentos"] = list(set(current_foods)) # Eliminar duplicados
+    entities["alimentos"] = list(set(current_foods))
     return entities
 
-    
-# --- LÓGICA DE BÚSQUEDA Y ANÁLISIS ---
-# (Todas las funciones auxiliares se mantienen igual que en la versión anterior)
 def sanitize_text(text):
     if not text: return ""
     return re.sub(r'[.,;()]', '', text).lower().strip()
+
 def extract_and_infer_with_gemini(query, condiciones):
     if not model: return None
     condiciones_str = "\n".join([f"- {c}" for c in condiciones])
-    
-    # Prompt mejorado para ser más estricto
     system_prompt = f"""
     Eres un asistente de triaje clínico experto. Tu tarea es analizar la consulta de un usuario y extraer tres tipos de información:
     1.  `alimentos`: Lista de alimentos consumidos.
@@ -331,31 +202,23 @@ def extract_and_infer_with_gemini(query, condiciones):
         
 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", []))
     inferred_condition_raw = sanitize_text(entities.get("condicion_probable", ""))
-
     candidate_terms = set(user_foods)
     for food in user_foods:
         food_sanitized = sanitize_text(food)
         if food_sanitized in FOOD_TO_COMPOUND_MAP:
             candidate_terms.update(c.lower() for c in FOOD_TO_COMPOUND_MAP[food_sanitized])
-        # Añadir también los componentes del vino directamente para ser más robusto
         if food_sanitized in ["vino", "vino tinto", "vino rojo"]:
             candidate_terms.update(["histamina", "tiramina", "sulfitos"])
-
     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
-
-        # Inicializamos el diccionario SIN el total
         score_details = {'condition': 0, 'food': 15, 'symptoms': 0}
-
         if inferred_condition_raw:
             entry_condition_sanitized = sanitize_text(entry.get("condicion_asociada", ""))
             is_match = (entry_condition_sanitized == inferred_condition_raw)
@@ -364,7 +227,6 @@ def find_best_matches_hybrid(entities, data):
                     is_match = True
             if is_match:
                 score_details['condition'] = 100
-
         entry_symptoms_keys = set(sanitize_text(s) for s in entry.get("sintomas_clave", []))
         symptom_score = 0
         matched_symptoms = []
@@ -375,24 +237,13 @@ def find_best_matches_hybrid(entities, data):
                     matched_symptoms.append(key)
                     break 
         score_details['symptoms'] = symptom_score
-        
-        # --- LÍNEA CORREGIDA Y CRÍTICA ---
-        # Calculamos la puntuación total y la añadimos al diccionario
         score_details['total'] = score_details['condition'] + score_details['food'] + score_details['symptoms']
-        
         results.append({
             'entry': entry, 
             'score': score_details, 
             'matched_symptoms': list(set(matched_symptoms))
         })
-
     if not results: return []
-    
-    # Ahora el ordenamiento funcionará correctamente porque 'total' tiene el valor correcto
-    sorted_results = sorted(results, key=lambda x: x['score']['total'], reverse=True)
-    return sorted_results
-    
-    # 4. Ordenar y devolver (sin cambios)
     sorted_results = sorted(results, key=lambda x: x['score']['total'], reverse=True)
     return sorted_results
 
@@ -435,7 +286,6 @@ def generate_detailed_analysis(query, match):
         logger.error(f"Error generando análisis detallado con Gemini: {e}")
         return "No se pudo generar el análisis detallado."
 def create_relevance_chart(results):
-    # ... (Sin cambios)
     top_results = results[:5]
     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]}
@@ -447,7 +297,6 @@ def create_relevance_chart(results):
     ).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):
-    # ... (Sin cambios)
     report_lines = []
     report_lines.append("="*50)
     report_lines.append("INFORME DEL DETECTIVE DE ALIMENTOS")
@@ -472,7 +321,6 @@ def generate_report_text(query, results):
     return "\n".join(report_lines)
 
 # --- INTERFAZ DE USUARIO Y LÓGICA PRINCIPAL ---
-# --- BLOQUE DE ENCABEZADO MODIFICADO ---
 col_img1, col_text, col_img2 = st.columns([1, 4, 1], gap="medium")
 with col_img1:
     if os.path.exists("imagen.png"):
@@ -491,6 +339,8 @@ if 'user_query' not in st.session_state: st.session_state.user_query = ""
 if 'entities' not in st.session_state: st.session_state.entities = None
 if 'analysis_cache' not in st.session_state: st.session_state.analysis_cache = {}
 if 'query' not in st.session_state: st.session_state.query = ""
+# --- CAMBIO 1: AÑADIMOS LA BANDERA DE CONTROL ---
+if 'start_analysis' not in st.session_state: st.session_state.start_analysis = False
 
 def clear_search_state():
     st.session_state.search_results = None
@@ -498,8 +348,10 @@ def clear_search_state():
     st.session_state.entities = None
     st.session_state.analysis_cache = {}
 
-def set_query_from_example(example_text):
+# --- CAMBIO 2: NUEVA FUNCIÓN PARA LOS BOTONES DE EJEMPLO ---
+def set_query_and_trigger_analysis(example_text):
     st.session_state.query = example_text
+    st.session_state.start_analysis = True
 
 # SECCIÓN: EJEMPLOS DE CONSULTA
 st.write("**¿No sabes por dónde empezar? Prueba con un ejemplo:**")
@@ -509,30 +361,42 @@ example_queries = [
     "Después de tomar leche, tengo muchos gases e hinchazón.",
     "El vino tinto siempre me da dolor de cabeza."
 ]
+# --- CAMBIO 3: LOS BOTONES AHORA LLAMAN A LA NUEVA FUNCIÓN ---
 if example_cols[0].button(example_queries[0]):
-    set_query_from_example(example_queries[0])
+    set_query_and_trigger_analysis(example_queries[0])
 if example_cols[1].button(example_queries[1]):
-    set_query_from_example(example_queries[1])
+    set_query_and_trigger_analysis(example_queries[1])
 if example_cols[2].button(example_queries[2]):
-    set_query_from_example(example_queries[2])
+    set_query_and_trigger_analysis(example_queries[2])
 
+# --- CAMBIO 4: ESTRUCTURA PRINCIPAL MODIFICADA ---
+# El formulario solo define la UI y activa la bandera al ser enviado.
 with st.form(key="search_form"):
-    query = st.text_area("Describe tu caso aquí:", height=150, key="query")
+    st.text_area("Describe tu caso aquí:", height=150, key="query")
     submitted = st.form_submit_button("Analizar mi caso", type="primary")
+    if submitted:
+        st.session_state.start_analysis = True
 
-if submitted:
+# La lógica de análisis se ejecuta si CUALQUIER botón activó la bandera.
+if st.session_state.start_analysis:
+    # Inmediatamente bajamos la bandera para evitar ejecuciones repetidas.
+    st.session_state.start_analysis = False
+    
+    query_to_analyze = st.session_state.query
+    
     clear_search_state()
-    st.session_state.user_query = query
-    if not query:
+    st.session_state.user_query = query_to_analyze
+    
+    if not query_to_analyze:
         st.warning("Por favor, describe lo que sientes y lo que comiste.")
     elif alimentos_data is None:
         st.error("La base de datos de alimentos no está disponible.")
     else:
         with st.spinner("🧠 Interpretando tu caso y buscando pistas con IA..."):
-            entities = extract_and_infer_with_gemini(query, lista_condiciones)
-            # --- AÑADE ESTA LÍNEA PARA ACTIVAR LA RED DE SEGURIDAD ---
-            entities = reinforce_entities_with_keywords(entities, query, FOOD_TO_COMPOUND_MAP)
+            entities = extract_and_infer_with_gemini(query_to_analyze, lista_condiciones)
+            entities = reinforce_entities_with_keywords(entities, query_to_analyze, FOOD_TO_COMPOUND_MAP)
             st.session_state.entities = entities
+        
         if entities and (entities.get("alimentos") or entities.get("sintomas")):
             info_str = f"IA identificó - Alimentos: {', '.join(entities.get('alimentos',[])) or 'Ninguno'}, Síntomas: {', '.join(entities.get('sintomas',[])) or 'Ninguno'}"
             if entities.get("condicion_probable"): info_str += f", Condición Probable: {entities.get('condicion_probable')}"
@@ -544,13 +408,13 @@ if submitted:
             st.error("No se pudieron identificar alimentos o síntomas claros en tu descripción.")
             st.session_state.search_results = []
 
+# La sección de mostrar resultados permanece igual, se ejecuta si hay resultados en el estado.
 if st.session_state.search_results is not None:
     results = st.session_state.search_results
     
     if not results:
         st.warning(f"No se encontraron coincidencias claras para tu caso: '{st.session_state.user_query}'.")
     else:
-        # --- Cabecera de resultados y botón de descarga (sin cambios) ---
         col1, col2 = st.columns([3,1])
         with col1:
             st.success(f"Hemos encontrado {len(results)} posible(s) causa(s) relacionada(s) con tu caso.")
@@ -563,12 +427,10 @@ if st.session_state.search_results is not None:
                 mime="text/plain"
             )
 
-        # --- Gráfico de relevancia (sin cambios) ---
-        st.subheader("Análisis de Relevancia de las Coinciden cias")
+        st.subheader("Análisis de Relevancia de las Coincidencias")
         chart = create_relevance_chart(results)
         st.altair_chart(chart, use_container_width=True)
 
-        # --- Análisis detallado del MEJOR resultado (sin cambios) ---
         best_match_data = results[0]
         best_match = best_match_data['entry']
         with st.expander(f"**Análisis Detallado de la Principal Coincidencia: {best_match.get('condicion_asociada')}**", expanded=True):
@@ -605,7 +467,7 @@ if st.session_state.search_results is not None:
                                                 for item in foodb_data[:3]:
                                                     st.write(f"**Compuesto:** {item['compound']}")
                                                     st.write(f"**Efectos reportados:** {', '.join(item['effects'])}")
-                                            st.markdown("---")
+                                                st.markdown("---")
                             if not found_data:
                                 st.warning("Sin datos moleculares para este alimento.")
             st.markdown("---")
@@ -614,36 +476,25 @@ if st.session_state.search_results is not None:
                     st.session_state.analysis_cache['best_match_analysis'] = generate_detailed_analysis(st.session_state.user_query, best_match)
                 st.markdown(st.session_state.analysis_cache['best_match_analysis'])
             
-        # --- NUEVA SECCIÓN MEJORADA: HASTA 4 OTRAS POSIBILIDADES ---
         if len(results) > 1:
             with st.expander("🔍 **Explora otras posibilidades relevantes (Diagnóstico Diferencial)**"):
-                # Iteramos sobre los resultados del 2 al 5 (hasta 4 alternativas)
                 for i, result in enumerate(results[1:5]): 
                     with st.container(border=True):
                         entry = result['entry']
                         score = result['score']
-                        
                         st.subheader(f"{i+2}. {entry.get('condicion_asociada')}")
-                        
                         col_info, col_action = st.columns([3, 1])
-                        
                         with col_info:
                             if result.get('matched_symptoms'):
                                 st.markdown(f"**Pistas Clave (Síntomas Coincidentes):** {', '.join(result['matched_symptoms']).capitalize()}")
                             st.markdown(f"**Alimentos Típicos Asociados:** {entry.get('compuesto_alimento')}")
-
                         with col_action:
                             st.metric("Relevancia", score['total'])
                             analysis_key = f"analysis_{i+2}"
                             if st.button("Generar análisis", key=analysis_key, help=f"Generar análisis de IA para {entry.get('condicion_asociada')}"):
                                 with st.spinner(f"Generando análisis para {entry.get('condicion_asociada')}..."):
-                                    # Guardamos el análisis en el cache para no regenerarlo
                                     st.session_state.analysis_cache[analysis_key] = generate_detailed_analysis(st.session_state.user_query, entry)
-                        
-                        # Muestra el análisis si ya fue generado y guardado en el cache
                         if analysis_key in st.session_state.analysis_cache:
                             st.info(st.session_state.analysis_cache[analysis_key])
-                        
-                        # Añade un separador visual, excepto para el último elemento
                         if i < len(results[1:5]) - 1:
-                            st.markdown("---")
+                            st.markdown("---")