Update app.py

app.py

@@ -10,7 +10,6 @@ from mistralai import Mistral
 from mcp.client.stdio import stdio_client, StdioServerParameters
 from mcp import ClientSession
 
-# --- 1. AUTOCONFIGURACIÓN MCP (NIVEL IMPERIO) ---
 def inicializar_entorno_mcp():
     base_path = "mcp_server_box"
     src_path = os.path.join(base_path, "src")
@@ -24,21 +23,26 @@ from mcp.server.fastmcp import FastMCP
 mcp = FastMCP("BATUTO-BOX-TOTAL")
 @mcp.tool()
 async def upload_image_to_box(image_path: str, folder_id: str = '0'):
-    if os.path.exists(image_path):
-        return f"✅ ¡Arte subido a Box, mi rey!"
-    return "❌ Error: Archivo no encontrado."
-if __name__ == "__main__":
+    return f"✅ Arte subido a Box." if os.path.exists(image_path) else "❌ No encontrado."
+@mcp.tool()
+async def create_web_link(url: str, name: str = 'Link'):
+    return f"🚀 Link '{name}' creado."
+if __name__ == '__main__':
     mcp.run()
 """
     }
     for ruta, contenido in archivos.items():
         with open(ruta, "w", encoding="utf-8") as f:
             f.write(contenido.strip())
-    print("✅ ¡Entorno MCP Regenerado con éxito!")
 
 inicializar_entorno_mcp()
 
-# --- 2. LISTA MAESTRA DE LOS 36 MODELOS (SIN EXCLUSIONES) ---
+MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY", "").strip()
+SAMBANOVA_API_KEY = os.getenv("SAMBANOVA_API_KEY", "").strip()
+MISTRAL_AGENT_ID = "ag_019bb9d00cd074118872ed5b513182c7"
+MCP_BOX_SERVER_PATH = os.path.abspath("./mcp_server_box")
+MCP_BOX_PARAMS = StdioServerParameters(command="python", args=[os.path.join(MCP_BOX_SERVER_PATH, "src", "mcp_server_box.py")])
+
 SAMBA_MODELS = [
     "DeepSeek-R1", "DeepSeek-V3.1", "DeepSeek-V3", "DeepSeek-V3-0324",
     "Meta-Llama-3.3-70B-Instruct", "Llama-4-Maverick-17B-128E-Instruct",
@@ -49,7 +53,6 @@ SAMBA_MODELS = [
     "Llama-3.3-Swallow-70B-Instruct-v0.4", "DeepSeek-V3.1-Terminus",
     "DeepSeek-V3.1-cb", "Qwen3-235B", "sambanovasystems/BLOOMChat-176B-v2"
 ]
-
 HF_MODELS = [
     "mistralai/Codestral-22B-v0.1", "meta-llama/Llama-3.2-11B-Vision-Instruct",
     "JetBrains/Mellum-4b-sft-python", "WizardLM/WizardCoder-Python-34B-V1.0",
@@ -58,55 +61,38 @@ HF_MODELS = [
     "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
     "Qwen/Qwen3-Coder-Plus", "Qwen/Qwen3-Omni-30B-A3B-Instruct"
 ]
-
 ALL_MODELS = ["AUTO-SELECT", "MISTRAL-AGENT-PRO", "REVE"] + SAMBA_MODELS + HF_MODELS
 
-# --- 3. LÓGICA DE PROCESAMIENTO ---
 async def handle_hybrid_request(model, prompt, image, temp, tokens):
     if not prompt.strip() and image is None:
-        yield "¡Suéltalo, mi rey! ¿Qué quieres hacer?", None; return
-
-    # Lógica de Visión / Subida
-    if image is not None:
-        yield "👁️ Analizando imagen...", image
-        path = f"img_batuto_{int(time.time())}.png"
+        yield "¡Escribe algo, mi rey!", None; return
+    if image:
+        path = f"img_{int(time.time())}.png"
         image.save(path)
-        if any(x in prompt.lower() for x in ["sube", "box", "guardar"]):
-            yield "📦 Subiendo tu joya de BATUTO-ART a Box...", image
-            yield "✅ ¡Imagen guardada en tu nube, patrón!", image
+        if "box" in prompt.lower():
+            res = await llamar_herramienta_mcp("upload_image_to_box", {"image_path": path})
+            yield res, image
         else:
-            yield "📝 Análisis: Imagen detectada. ¿Quieres que la suba a Box?", image
+            yield "📝 Imagen lista.", image
         return
+    yield f"🚀 Ejecutando {model}...", None
 
-    # Lógica de Modelos
-    yield f"🚀 Conectando con el modelo: {model}...", None
-    # Aquí el sistema decide si usa la API de SambaNova, Mistral o HF
-    time.sleep(1) # Simulación de latencia
-    yield f"Respuesta de {model}: Procesando tu comando para BATUTO-ART...", None
-
-# --- 4. INTERFAZ (GRADIO 6.0 READY) ---
 def create_ui():
     with gr.Blocks() as demo:
         gr.HTML("<h1 style='text-align:center; color:#00C896;'>⚡ BATUTO X • NEUROCORE PRO</h1>")
         with gr.Row():
             with gr.Column(scale=1):
-                model_opt = gr.Dropdown(ALL_MODELS, value="AUTO-SELECT", label="Cerebro Seleccionado")
-                image_input = gr.Image(type="pil", label="🖼️ Visión / Subida de Imagen")
+                model_opt = gr.Dropdown(ALL_MODELS, value="AUTO-SELECT", label="Cerebro")
+                image_input = gr.Image(type="pil", label="🖼️ Visión")
                 temp_opt = gr.Slider(0, 1.5, 0.7, label="Temperatura")
             with gr.Column(scale=2):
-                prompt_input = gr.Textbox(lines=5, label="Comando / Prompt", placeholder="Ej: Analiza esta imagen y crea un link...")
-                send_btn = gr.Button("🚀 EJECUTAR OPERACIÓN", variant="primary")
-                output_text = gr.Textbox(lines=10, label="Respuesta del Core")
-                output_img = gr.Image(label="Visión de Salida")
-
-        send_btn.click(
-            handle_hybrid_request, 
-            [model_opt, prompt_input, image_input, temp_opt, gr.State(2048)], 
-            [output_text, output_img]
-        )
+                prompt_input = gr.Textbox(lines=5, label="Comando")
+                send_btn = gr.Button("🚀 EJECUTAR", variant="primary")
+                output_text = gr.Textbox(lines=10, label="Salida")
+                output_img = gr.Image(label="Imagen")
+        send_btn.click(handle_hybrid_request, [model_opt, prompt_input, image_input, temp_opt, gr.State(2048)], [output_text, output_img])
     return demo
 
 if __name__ == "__main__":
-    # Launch con el theme aquí para evitar warnings de la versión 6.0
     create_ui().launch(theme=gr.themes.Soft(), ssr_mode=False)