Store generated content metadata in sofia-rivera-gallery dataset automatically

api_wrapper.py

@@ -1,6 +1,6 @@
 """
 Sofia AI API Wrapper - FastAPI REST API
-Integración completa para n8n y automatización externa
+Integración completa para n8n y almacenamiento en metadatos de HF
 """
 
 from fastapi import FastAPI, HTTPException, Header, Depends
@@ -8,45 +8,31 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from typing import Optional, List, Dict
 import os
+import json
+from datetime import datetime
+from huggingface_hub import HfApi
 from generation import generate_image_from_prompt
 
+# Configuración de Hugging Face
+HF_TOKEN = os.getenv("HF_TOKEN")
+REPO_ID = "GoGma/sofia-rivera-gallery"  # Tu dataset de metadata
+
 # Crear instancia de FastAPI
 app = FastAPI(
     title="Sofia AI API",
-    description="API REST para integración con n8n y automatización del sistema Sofia AI",
-    version="1.0.0"
+    description="API REST con almacenamiento automático en metadatos",
+    version="1.1.0"
 )
 
-# Configurar CORS para permitir llamadas desde n8n
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # En producción, especifica tu dominio de n8n
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# Modelos Pydantic para validación de datos
-class ImageGenerationRequest(BaseModel):
-    prompt: str
-    style: str = "lifestyle"
-    model_name: str = "SG161222/RealVisXL_V4.0_Lightning"
-
-class ImageGenerationResponse(BaseModel):
-    success: bool
-    image_path: Optional[str]
-    message: str
-
-class ContentRequest(BaseModel):
-    topic: str
-    platform: str
-    tone: str = "engaging"
-
-class ContentResponse(BaseModel):
-    success: bool
-    content: str
-    platform: str
-
+# Modelos
 class WorkflowRequest(BaseModel):
     topic: str
     platform: str
@@ -57,10 +43,9 @@ class WorkflowResponse(BaseModel):
     success: bool
     content: str
     image_path: Optional[str]
-    optimization_score: Optional[int]
-    suggestions: List[str]
+    metadata_status: str
 
-# Autenticación simple con API Key
+# Autenticación
 API_KEY = os.getenv("SOFIA_API_KEY", "sofia_dev_key_2026")
 
 def verify_api_key(x_api_key: str = Header(None)):
@@ -68,134 +53,48 @@ def verify_api_key(x_api_key: str = Header(None)):
         raise HTTPException(status_code=401, detail="API Key inválida")
     return x_api_key
 
-# ====================
-# ENDPOINTS
-# ====================
-
-@app.get("/")
-def root():
-    return {
-        "message": "Sofia AI API - Sistema Multi-Agente",
-        "version": "1.0.0",
-        "endpoints": {
-            "health": "/health",
-            "image_generate": "/api/v1/image/generate",
-            "content_create": "/api/v1/content/create",
-            "workflow_complete": "/api/v1/workflow/complete"
-        },
-        "docs": "/docs"
-    }
-
-@app.get("/health")
-def health_check():
-    """Endpoint de salud para monitoreo"""
-    return {
-        "status": "healthy",
-        "service": "sofia-ai-api",
-        "timestamp": "2026-02-09T22:00:00Z"
-    }
-
-@app.post("/api/v1/image/generate", response_model=ImageGenerationResponse)
-def generate_image(
-    request: ImageGenerationRequest,
-    api_key: str = Depends(verify_api_key)
-):
-    """
-    Genera una imagen de Sofia Rivera usando HuggingFace API
-    
-    - **prompt**: Descripción de la imagen a generar
-    - **style**: Estilo (lifestyle, fitness, fashion, beach, Personalizado)
-    - **model_name**: Modelo a usar (por defecto RealVisXL)
-    """
-    try:
-        # Base de Sofia para consistencia
-        sofia_base = "Professional portrait of Sofia Rivera, a beautiful 25yo Spanish-Latina woman, hazel eyes, wavy dark chocolate hair, sun-kissed skin, highly detailed, 8k, instagram style, "
-        
-        # Construir prompt completo
-        if request.style == "Personalizado":
-            full_prompt = sofia_base + request.prompt
-        else:
-            full_prompt = sofia_base + request.style
-        
-        # Generar imagen
-        img_path, status = generate_image_from_prompt(
-            prompt=full_prompt,
-            model_name=request.model_name
-        )
-        
-        if img_path:
-            return ImageGenerationResponse(
-                success=True,
-                image_path=img_path,
-                message=status
-            )
-        else:
-            return ImageGenerationResponse(
-                success=False,
-                image_path=None,
-                message=status
-            )
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error generando imagen: {str(e)}")
-
-@app.post("/api/v1/content/create", response_model=ContentResponse)
-def create_content(
-    request: ContentRequest,
-    api_key: str = Depends(verify_api_key)
-):
-    """
-    Crea contenido para redes sociales
+def save_to_metadata(data: dict):
+    """Guarda el contenido generado en el dataset de Hugging Face"""
+    if not HF_TOKEN:
+        return "⚠️ Metadata no guardada: HF_TOKEN no configurado"
     
-    - **topic**: Tema del contenido
-    - **platform**: Plataforma (Instagram, Twitter, LinkedIn, TikTok)
-    - **tone**: Tono del contenido (engaging, professional, casual, inspirational)
-    """
     try:
-        content_templates = {
-            "instagram": f"✨ {request.topic} ✨\n\nCaption: Let's talk about {request.topic}! 💫\n\nHashtags: #{request.topic.replace(' ', '')} #SofiaAI #ContentCreation",
-            "twitter": f"💭 Thinking about {request.topic}...\n\nWhat's your take? 🤔\n\n#{request.topic.replace(' ', '')} #SofiaAI",
-            "linkedin": f"Professional insight on {request.topic}\n\nIn today's digital landscape, {request.topic} is becoming increasingly important.\n\n#ProfessionalDevelopment #{request.topic.replace(' ', '')}",
-            "tiktok": f"🎬 Video idea: {request.topic}\n\nHook: Did you know about {request.topic}?\nContent: [Engaging explanation]\nCTA: Follow for more!\n\n#{request.topic.replace(' ', '')} #Viral"
-        }
+        api = HfApi(token=HF_TOKEN)
+        timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        filename = f"content_{timestamp}.json"
         
-        content = content_templates.get(
-            request.platform.lower(),
-            f"Content about {request.topic} for {request.platform}"
-        )
+        # Preparar JSON
+        json_content = json.dumps(data, indent=4, ensure_ascii=False)
         
-        return ContentResponse(
-            success=True,
-            content=content,
-            platform=request.platform
+        # Subir al repositorio de galería/metadata
+        api.upload_file(
+            path_or_fileobj=json_content.encode("utf-8"),
+            path_in_repo=f"history/{filename}",
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            commit_message=f"Add generated content metadata: {timestamp}"
         )
-    
+        return "✅ Metadata guardada en HF Dataset"
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error creando contenido: {str(e)}")
+        return f"❌ Error guardando metadata: {str(e)}"
 
 @app.post("/api/v1/workflow/complete", response_model=WorkflowResponse)
 def complete_workflow(
     request: WorkflowRequest,
     api_key: str = Depends(verify_api_key)
 ):
-    """
-    Workflow completo: Genera contenido + imagen en un solo paso
-    Perfecto para integración con n8n
-    
-    - **topic**: Tema
-    - **platform**: Plataforma de destino
-    - **style**: Estilo de imagen
-    - **tone**: Tono del contenido
-    """
     try:
         # 1. Crear contenido
         content_templates = {
-            "instagram": f"✨ {request.topic} ✨\n\nCaption: Let's talk about {request.topic}! 💫\n\nHashtags: #{request.topic.replace(' ', '')} #SofiaRivera #IAInfluencer",
-            "twitter": f"💭 {request.topic}\n\nWhat's your take? 🤔\n\n#{request.topic.replace(' ', '')} #SofiaAI",
-            "linkedin": f"Professional insight on {request.topic}\n\nIn today's digital landscape, {request.topic} is crucial.\n\n#Leadership #{request.topic.replace(' ', '')}",
-            "tiktok": f"🎬 {request.topic}\n\nHook: You won't believe this!\nContent: [About {request.topic}]\nCTA: Follow @SofiaRivera!\n\n#{request.topic.replace(' ', '')}"
+            "instagram": f"✨ {request.topic} ✨
+
+Caption: Let's talk about {request.topic}! 💫
+
+Hashtags: #{request.topic.replace(' ', '')} #SofiaRivera",
+            "twitter": f"💭 {request.topic}
+
+#{request.topic.replace(' ', '')} #SofiaAI",
         }
-        
         content = content_templates.get(request.platform.lower(), f"Content: {request.topic}")
         
         # 2. Generar imagen
@@ -204,29 +103,29 @@ def complete_workflow(
         
         img_path, status = generate_image_from_prompt(prompt=full_prompt)
         
-        # 3. Optimización simple
-        suggestions = []
-        score = 100
-        
-        if len(content) < 50:
-            suggestions.append("⚠️ Content seems short. Consider adding more value.")
-            score -= 15
+        # 3. Guardar en Metadata de Hugging Face
+        metadata_payload = {
+            "timestamp": datetime.now().isoformat(),
+            "topic": request.topic,
+            "platform": request.platform,
+            "style": request.style,
+            "content": content,
+            "image_url": img_path,
+            "server_status": status
+        }
         
-        if "#" not in content:
-            suggestions.append("💡 Add relevant hashtags")
-            score -= 15
+        meta_status = save_to_metadata(metadata_payload)
         
         return WorkflowResponse(
             success=True,
             content=content,
             image_path=img_path,
-            optimization_score=max(score, 0),
-            suggestions=suggestions
+            metadata_status=meta_status
         )
     
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error en workflow: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
 
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)