app.py

# =========================================================
# BATUTOrquestaIA V3 — Compatible con Gradio 6.0
# =========================================================
import os
import gradio as gr
import requests
import base64
import json
from io import BytesIO
from PIL import Image
from openai import OpenAI
from dotenv import load_dotenv
import huggingface_hub

load_dotenv()

# --- CONFIGURACIÓN COMPLETA DE APIS ---
OPENAI_CLIENT = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
SAMBA_API_KEY = os.getenv("SAMBANOVA_API_KEY")
REVE_API_KEY = os.getenv("REVE_API_KEY")
HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")

# --- DICCIONARIO MAESTRO DE MODELOS ACTUALIZADO ---
MODELOS_UNIFICADOS = {
    # === MODELOS LOCALES (Hugging Face) ===
    "🧠 Mellum 4B Local": {"engine": "huggingface", "id": "JetBrains/Mellum-4b-sft-python"},
    "🧠 Codestral 22B": {"engine": "huggingface", "id": "mistralai/Codestral-22B-v0.1"},
    "💻 WizardCoder-V2": {"engine": "huggingface", "id": "WizardLM/WizardCoder-Python-34B-V1.0"},
    "🐍 Llama 3.x Code": {"engine": "huggingface", "id": "meta-llama/Llama-3.2-11B-Vision-Instruct"},
    
    # === SAMBANOVA (Alta Velocidad) ===
    "💡 DeepSeek R1": {"engine": "samba", "id": "DeepSeek-R1"},
    "💡 DeepSeek V3": {"engine": "samba", "id": "DeepSeek-V3"},
    "💡 DeepSeek V3.1": {"engine": "samba", "id": "DeepSeek-V3.1"},
    "💡 DeepSeek V3-0324": {"engine": "samba", "id": "DeepSeek-V3-0324"},
    "🦙 Llama 3.1 8B": {"engine": "samba", "id": "Meta-Llama-3.1-8B-Instruct"},
    "🦙 Llama 3.3 70B": {"engine": "samba", "id": "Meta-Llama-3.3-70B-Instruct"},
    "🦙 Llama-4 Maverick 17B": {"engine": "samba", "id": "Llama-4-Maverick-17B-128E-Instruct"},
    "🧩 Qwen3-32B": {"engine": "samba", "id": "Qwen3-32B"},
    "🌀 GPT-OSS 120B": {"engine": "samba", "id": "gpt-oss-120b"},
    "🌍 ALLaM-7B": {"engine": "samba", "id": "ALLaM-7B-Instruct-preview"},
    
    # === GITHUB AI / PREMIUM ===
    "🧬 GPT-5 Mini": {"engine": "github", "id": "openai/gpt-5-mini"},
    "🧬 Grok 3": {"engine": "github", "id": "xai/grok-3"},
    "🧬 Mistral Code": {"engine": "github", "id": "mistral-ai/Codestral-2501"},
    
    # === OPENAI DIRECTO ===
    "🪩 GPT-4.5 Omni": {"engine": "openai", "id": "gpt-4.5-turbo"},
    "🪩 GPT-o1": {"engine": "openai", "id": "gpt-o1"},
    "🪩 GPT-4o": {"engine": "openai", "id": "gpt-4o"},
    "🪩 GPT-4o-mini": {"engine": "openai", "id": "gpt-4o-mini"},
    
    # === GENERACIÓN VISUAL ===
    "🎨 REVE CREATE (Imagen)": {"engine": "reve", "id": "reve-v1"},
    "🎨 Stable Diffusion 3": {"engine": "huggingface", "id": "stabilityai/stable-diffusion-3.5-medium"},
    
    # === MODELOS DE CÓDIGO ESPECIALIZADOS ===
    "💾 CodeLlama 70B": {"engine": "samba", "id": "CodeLlama-70b"},
    "🔧 DeepSeek Coder": {"engine": "samba", "id": "DeepSeek-Coder-V2"}
}

# --- LÓGICA MEJORADA PARA HUGGING FACE ---
def query_huggingface(model_id, prompt, max_length=500):
    """Consulta modelos de Hugging Face"""
    API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
    headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
    
    payload = {
        "inputs": prompt,
        "parameters": {
            "max_length": max_length,
            "temperature": 0.7,
            "top_p": 0.9
        }
    }
    
    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
        response.raise_for_status()
        result = response.json()
        
        if isinstance(result, list) and len(result) > 0:
            return result[0].get('generated_text', str(result))
        return str(result)
    except Exception as e:
        return f"❌ Error con Hugging Face: {str(e)}"

# --- LÓGICA DE GENERACIÓN DE IMAGEN MEJORADA ---
def gen_image(prompt, engine="reve"):
    """Genera imágenes con múltiples proveedores"""
    
    if engine == "reve":
        url = "https://api.reve.com/v1/image/create"
        headers = {"Authorization": f"Bearer {REVE_API_KEY}", "Content-Type": "application/json"}
        payload = {
            "prompt": prompt,
            "aspect_ratio": "1:1",
            "version": "latest",
            "quality": "standard",
            "guidance_scale": 7.5
        }
        
        try:
            response = requests.post(url, headers=headers, json=payload, timeout=60)
            if response.status_code == 200:
                data = response.json()
                if "image" in data:
                    img_data = base64.b64decode(data["image"])
                    return Image.open(BytesIO(img_data))
                elif "image_url" in data:
                    img_resp = requests.get(data["image_url"])
                    return Image.open(BytesIO(img_resp.content))
        except Exception as e:
            print(f"Error REVE: {e}")
    
    # Fallback a Hugging Face para generación de imágenes
    elif engine == "huggingface":
        API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-3.5-medium"
        headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
        
        try:
            response = requests.post(API_URL, headers=headers, json={"inputs": prompt}, timeout=60)
            if response.status_code == 200:
                return Image.open(BytesIO(response.content))
        except:
            pass
    
    return None

# --- LÓGICA DE CHAT UNIFICADA MEJORADA ---
def responder_orquesta(mensaje, historial, modelo_nombre):
    """Procesa mensajes con el modelo seleccionado"""
    
    cfg = MODELOS_UNIFICADOS.get(modelo_nombre)
    if not cfg:
        return historial + [(mensaje, "❌ Modelo no encontrado")], None
    
    # Caso 1: Generación de Imagen
    if cfg["engine"] in ["reve", "huggingface"] and cfg["id"] in ["reve-v1", "stabilityai/stable-diffusion-3.5-medium"]:
        img = gen_image(mensaje, engine=cfg["engine"])
        if img:
            historial.append((mensaje, f"🎨 **¡Aquí tienes tu creación, mi BATUTO!**\n\nModelo: {modelo_nombre}\n\n*Firma: BATUTO-ART*"))
            return historial, img
        return historial + [(mensaje, "❌ No se pudo generar la imagen. Intenta con otro prompt.")], None
    
    # Prompt del sistema mejorado
    prompt_sistema = """Eres BATUTO-ART, una IA con flow chilango de barrio pero conocimiento de nivel mundial.
    
    Instrucciones:
    1. Responde en español a menos que se pida otro idioma
    2. Sé creativo pero preciso
    3. Firma siempre como "BATUTO-ART" al final
    4. Si es código, explica brevemente
    5. Mantén un tono profesional pero con personalidad
    
    Modelo actual: """ + modelo_nombre
    
    try:
        respuesta = ""
        
        # SAMBANOVA
        if cfg["engine"] == "samba":
            client_samba = OpenAI(
                api_key=SAMBA_API_KEY,
                base_url="https://api.sambanova.ai/v1"
            )
            res = client_samba.chat.completions.create(
                model=cfg["id"],
                messages=[
                    {"role": "system", "content": prompt_sistema},
                    {"role": "user", "content": mensaje}
                ],
                temperature=0.7,
                max_tokens=1000
            )
            respuesta = res.choices[0].message.content
        
        # HUGGING FACE (texto)
        elif cfg["engine"] == "huggingface":
            respuesta = query_huggingface(cfg["id"], f"{prompt_sistema}\n\nUsuario: {mensaje}")
        
        # OPENAI / GITHUB AI
        elif cfg["engine"] in ["openai", "github"]:
            model_to_use = cfg["id"] if cfg["engine"] == "openai" else "gpt-4o"
            res = OPENAI_CLIENT.chat.completions.create(
                model=model_to_use,
                messages=[
                    {"role": "system", "content": prompt_sistema},
                    {"role": "user", "content": mensaje}
                ],
                temperature=0.7
            )
            respuesta = res.choices[0].message.content
        
        else:
            respuesta = "❌ Motor no soportado aún"
        
        historial.append((mensaje, respuesta))
        return historial, None
        
    except Exception as e:
        error_msg = f"❌ **Error de conexión con {modelo_nombre}:**\n\n`{str(e)}`\n\nIntenta con otro modelo o revisa tu conexión."
        return historial + [(mensaje, error_msg)], None

# --- CSS ACTUALIZADO ---
CSS = """
.gradio-container { 
    background: linear-gradient(135deg, #0a0a0a 0%, #1a1a2e 50%, #16213e 100%) !important; 
    color: #e2e8f0 !important; 
    font-family: 'Segoe UI', system-ui, -apple-system, sans-serif;
}

.floating-eleven { 
    position: fixed; 
    bottom: 100px; 
    right: 20px; 
    z-index: 1000; 
    border: 2px solid #6366f1; 
    border-radius: 16px;
    background: rgba(15, 23, 42, 0.9);
    backdrop-filter: blur(10px);
    padding: 10px;
    box-shadow: 0 10px 30px rgba(99, 102, 241, 0.3);
}

.batuto-sig { 
    position: fixed; 
    top: 15px; 
    left: 15px; 
    font-family: 'Courier New', monospace; 
    color: #6366f1; 
    font-weight: 800; 
    font-size: 18px; 
    z-index: 1001;
    background: linear-gradient(45deg, #6366f1, #8b5cf6, #ec4899);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
}

.model-selector {
    background: rgba(30, 41, 59, 0.8) !important;
    border: 1px solid rgba(99, 102, 241, 0.3) !important;
    color: #e2e8f0 !important;
    border-radius: 12px !important;
}

.chatbot {
    background: rgba(15, 23, 42, 0.7) !important;
    border: 1px solid rgba(99, 102, 241, 0.2) !important;
    border-radius: 16px !important;
    backdrop-filter: blur(10px);
}

.textbox {
    background: rgba(30, 41, 59, 0.8) !important;
    border: 1px solid rgba(99, 102, 241, 0.3) !important;
    border-radius: 12px !important;
    color: #e2e8f0 !important;
}

.button {
    background: linear-gradient(45deg, #6366f1, #8b5cf6) !important;
    color: white !important;
    border: none !important;
    border-radius: 12px !important;
    font-weight: 600 !important;
}

.button:hover {
    background: linear-gradient(45deg, #8b5cf6, #ec4899) !important;
    transform: translateY(-2px);
    box-shadow: 0 5px 20px rgba(99, 102, 241, 0.4);
}

.image-display {
    border-radius: 16px !important;
    border: 2px solid rgba(99, 102, 241, 0.2) !important;
}

.markdown-text {
    color: #e2e8f0 !important;
}

.contain {
    contain: layout style paint;
}
"""

# --- CREACIÓN DE LA INTERFAZ ---
with gr.Blocks(title="BATUTOrquestaIA V3 - Multi-Model Fusion") as demo:
    
    # Header fijo
    gr.HTML("""
    <div class='batuto-sig'>
        <span style="font-size: 24px;">🎨</span> BATUTO-ART
        <span style="font-size: 12px; opacity: 0.7; margin-left: 10px;">Multi-Model AI Platform</span>
    </div>
    """)
    
    # Título principal
    gr.Markdown("""
    # 🎼 BATUTOrquestaIA V3
    ### *El Mero Mero Multimodal - Todos los modelos sin exclusión*
    
    ⚡ **Selecciona cualquier modelo de la lista y comienza a crear**
    """)
    
    with gr.Row():
        # Panel de chat principal
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(
                label="Chat Multimodal",
                height=650,
                show_label=False,
                container=True
            )
            
            with gr.Row():
                user_input = gr.Textbox(
                    show_label=False,
                    placeholder="¿Qué vamos a crear hoy, mi BATUTO? Escribe tu prompt aquí...",
                    scale=8,
                    lines=2,
                    max_lines=5
                )
                submit_btn = gr.Button("🚀 Enviar", scale=1, variant="primary")
                clear_btn = gr.Button("🧹 Limpiar", scale=1, variant="secondary")
        
        # Panel lateral de control
        with gr.Column(scale=1):
            # Selector de modelos con grupos
            gr.Markdown("### 🎯 **Selector de Modelos**")
            
            with gr.Tabs():
                with gr.TabItem("🤖 Todos"):
                    model_dropdown = gr.Dropdown(
                        choices=list(MODELOS_UNIFICADOS.keys()),
                        value="💡 DeepSeek R1",
                        label="",
                        interactive=True,
                        elem_classes="model-selector"
                    )
                
                with gr.TabItem("🎨 Imagen"):
                    image_models = [k for k, v in MODELOS_UNIFICADOS.items() 
                                  if v["engine"] in ["reve", "huggingface"] and "reve-v1" in str(v.get("id", ""))]
                    gr.Dropdown(
                        choices=image_models,
                        value="🎨 REVE CREATE (Imagen)" if image_models else None,
                        label="Modelos de Imagen"
                    )
                
                with gr.TabItem("💻 Código"):
                    code_models = [k for k, v in MODELOS_UNIFICADOS.items() 
                                 if "codestral" in k.lower() or "coder" in k.lower() or "wizard" in k.lower()]
                    gr.Dropdown(
                        choices=code_models,
                        label="Modelos de Programación"
                    )
            
            # Estadísticas rápidas
            gr.Markdown("---")
            gr.Markdown("### 📊 **Estadísticas**")
            model_count = gr.Markdown(f"**Modelos disponibles:** {len(MODELOS_UNIFICADOS)}")
            last_update = gr.Markdown("**Última actualización:** Ahora mismo")
            
            # Panel de imagen generada
            gr.Markdown("---")
            gr.Markdown("### 🖼️ **Galería de Imágenes**")
            image_output = gr.Image(
                label="Imagen Generada",
                type="pil",
                height=300,
                show_label=False
            )
            
            # Botones de acción rápida
            gr.Markdown("---")
            gr.Markdown("### ⚡ **Acciones Rápidas**")
            with gr.Row():
                save_btn = gr.Button("💾 Guardar", scale=1)
                share_btn = gr.Button("📤 Compartir", scale=1)
            
            # Widget de ElevenLabs (solo uno)
            gr.HTML("""
            <div class="floating-eleven">
                <h4 style="margin: 0 0 10px 0; color: #8b5cf6;">🎙️ Voice Assistant</h4>
                <elevenlabs-convai agent-id="agent_1301kdwts7v9eszss3tkzm87kra3"></elevenlabs-convai>
                <script src="https://unpkg.com/@elevenlabs/convai-widget-embed@beta" async></script>
                <p style="font-size: 11px; margin: 5px 0 0 0; opacity: 0.7;">Powered by ElevenLabs</p>
            </div>
            """)
    
    # Pie de página
    gr.Markdown("---")
    gr.Markdown("""
    <div style="text-align: center; color: #94a3b8; font-size: 0.9em;">
        <p>🎨 <strong>BATUTO-ART Platform</strong> | Multi-Model AI Fusion System</p>
        <p>⚠️ <em>Algunos modelos pueden requerir API keys adicionales</em></p>
        <p style="font-size: 0.8em;">v3.0 | Compatible con Gradio 6.0</p>
    </div>
    """)
    
    # Funcionalidad
    def process_input(mensaje, historial, modelo):
        """Envuelve la función principal para manejo de estado"""
        return responder_orquesta(mensaje, historial, modelo)
    
    submit_btn.click(
        process_input,
        inputs=[user_input, chatbot, model_dropdown],
        outputs=[chatbot, image_output]
    ).then(lambda: "", outputs=[user_input])
    
    user_input.submit(
        process_input,
        inputs=[user_input, chatbot, model_dropdown],
        outputs=[chatbot, image_output]
    ).then(lambda: "", outputs=[user_input])
    
    clear_btn.click(lambda: [], outputs=[chatbot])
    
    # Función para guardar imagen
    def save_image(image):
        if image is not None:
            import datetime
            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"batuto_art_{timestamp}.png"
            image.save(filename)
            return f"💾 Imagen guardada como: {filename}"
        return "❌ No hay imagen para guardar"
    
    save_btn.click(save_image, inputs=[image_output], outputs=[gr.Textbox(visible=False)])

# --- EJECUCIÓN ---
if __name__ == "__main__":
    print("🚀 Iniciando BATUTOrquestaIA V3...")
    print(f"📊 Modelos cargados: {len(MODELOS_UNIFICADOS)}")
    
    # Configuración para Hugging Face Spaces
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=True,
        css=CSS,
        theme=gr.themes.Soft()
    )