✅ IMPLEMENTADO - Soporte para Anthropic Claude y Corrección de Configuración

🔧 Backend v1.2.0 | Frontend v1.2.5
He implementado completamente el soporte para Anthropic Claude y corregido el problema de visualización en el módulo de configuración:

🎯 Cambios Implementados:
1. Backend - Soporte para Anthropic Claude
Nuevos modelos agregados:

Claude Sonnet 4.5 (Recomendado) - Equilibrio perfecto
Claude Opus 4.5 - Máxima capacidad
Claude Haiku 4.5 - Ultra rápido y económico
Integración completa:

Chat Assistant con Anthropic
Generación de resúmenes PDF con Anthropic
Manejo correcto de formato de mensajes (system separado)
Configuración por defecto: claude-sonnet-4.5

backend/app/main.py

@@ -276,7 +276,7 @@ def extract_pdf_text_smart(pdf_content: bytes, max_chars: int = None) -> dict:
         }
 
 
-BACKEND_VERSION = "1.1.2"
+BACKEND_VERSION = "1.2.0"
 app = FastAPI(title="Checklist Inteligente API", version=BACKEND_VERSION)
 
 # S3/MinIO configuration
@@ -1512,12 +1512,12 @@ REGLAS:
 """
 
     try:
-        # Usar OpenAI o Gemini según configuración
-        if config.provider == "openai" and config.openai_api_key:
-            client = openai.OpenAI(api_key=config.openai_api_key)
+        # Usar OpenAI, Anthropic o Gemini según configuración
+        if config.provider == "openai":
+            client = openai.OpenAI(api_key=config.api_key)
             response = await asyncio.to_thread(
                 client.chat.completions.create,
-                model=config.openai_model or "gpt-4o",
+                model=config.model_name or "gpt-4o",
                 messages=[{"role": "user", "content": summary_prompt}],
                 temperature=0.3,
                 max_tokens=800,
@@ -1525,10 +1525,22 @@ REGLAS:
             )
             summary_json = response.choices[0].message.content
             
-        elif config.provider == "gemini" and config.gemini_api_key:
-            genai.configure(api_key=config.gemini_api_key)
+        elif config.provider == "anthropic":
+            import anthropic as anthropic_lib
+            client = anthropic_lib.Anthropic(api_key=config.api_key)
+            response = await asyncio.to_thread(
+                client.messages.create,
+                model=config.model_name or "claude-sonnet-4-5",
+                max_tokens=800,
+                temperature=0.3,
+                messages=[{"role": "user", "content": summary_prompt + "\n\nRespuesta en formato JSON:"}]
+            )
+            summary_json = response.content[0].text
+            
+        elif config.provider == "gemini":
+            genai.configure(api_key=config.api_key)
             model = genai.GenerativeModel(
-                model_name=config.gemini_model or "gemini-2.0-flash-exp",
+                model_name=config.model_name or "gemini-2.5-pro",
                 generation_config={
                     "temperature": 0.3,
                     "max_output_tokens": 800,
@@ -2726,6 +2738,25 @@ def get_available_ai_models(current_user: models.User = Depends(get_current_user
             "name": "Gemini 1.5 Flash Latest",
             "provider": "gemini",
             "description": "Modelo 1.5 rápido para análisis básicos"
+        },
+        # Anthropic Claude Models
+        {
+            "id": "claude-sonnet-4-5",
+            "name": "Claude Sonnet 4.5 (Recomendado)",
+            "provider": "anthropic",
+            "description": "Equilibrio perfecto entre velocidad e inteligencia, ideal para diagnósticos automotrices"
+        },
+        {
+            "id": "claude-opus-4-5",
+            "name": "Claude Opus 4.5",
+            "provider": "anthropic",
+            "description": "Máxima capacidad para análisis complejos y razonamiento profundo"
+        },
+        {
+            "id": "claude-haiku-4-5",
+            "name": "Claude Haiku 4.5",
+            "provider": "anthropic",
+            "description": "Ultra rápido y económico, perfecto para análisis en tiempo real"
         }
     ]
     
@@ -2771,6 +2802,8 @@ def create_ai_configuration(
             model_name = "gpt-4o"
         elif config.provider == "gemini":
             model_name = "gemini-2.5-pro"
+        elif config.provider == "anthropic":
+            model_name = "claude-sonnet-4-5"
         else:
             model_name = "default"
     
@@ -3510,6 +3543,28 @@ Longitud: {response_length}
             ai_response = response.choices[0].message.content
             confidence = 0.85  # OpenAI no devuelve confidence directo
             
+        elif ai_config.provider == 'anthropic':
+            import anthropic
+            
+            # Crear cliente de Anthropic
+            client = anthropic.Anthropic(api_key=ai_config.api_key)
+            
+            # Antropic usa un formato diferente: system separado de messages
+            # El primer mensaje es el system prompt
+            system_content = messages[0]['content'] if messages[0]['role'] == 'system' else ""
+            user_messages = [msg for msg in messages if msg['role'] != 'system']
+            
+            response = client.messages.create(
+                model=ai_config.model_name or "claude-sonnet-4-5",
+                max_tokens=max_tokens,
+                system=system_content,
+                messages=user_messages,
+                temperature=0.7
+            )
+            
+            ai_response = response.content[0].text
+            confidence = 0.85
+            
         elif ai_config.provider == 'gemini':
             import google.generativeai as genai
             genai.configure(api_key=ai_config.api_key)

backend/app/schemas.py

@@ -269,7 +269,7 @@ class InspectionDetail(Inspection):
 
 # AI Configuration Schemas
 class AIConfigurationBase(BaseModel):
-    provider: str  # openai, gemini
+    provider: str  # openai, gemini, anthropic
     api_key: str
     model_name: Optional[str] = None
     logo_url: Optional[str] = None

backend/requirements.txt

@@ -11,6 +11,7 @@ passlib==1.7.4
 bcrypt==4.0.1
 python-multipart==0.0.6
 openai==1.10.0
+anthropic==0.40.0
 google-generativeai==0.3.2
 Pillow==10.2.0
 reportlab==4.0.9