Actualizar app.py

app.py

@@ -2,118 +2,11 @@ import gradio as gr
 import requests
 import json
 import os
-import random
 import base64
 import io
 from PIL import Image
-from typing import List, Optional
-
-# ==============================================================
-#  CLASE PRINCIPAL: GENERADOR DE PROMPTS HIPERREALISTAS
-# ==============================================================
-
-class HyperrealisticPromptGenerator:
-    def __init__(self):
-        self.ROLES = [
-            "nurse", "nun", "maid", "flight attendant", "secretary", "teacher", "schoolgirl", "lawyer",
-            "doctor", "boudoir model", "fitness model", "elegant judge", "seductive librarian",
-            "business executive", "policewoman", "female military officer", "WWII-era secretary",
-            "1960s flight attendant", "seductive maid", "mysterious nurse", "captivating schoolgirl"
-        ]
-        self.AGES = [
-            "early 20s youthful vibrance",
-            "mid 20s graceful confidence",
-            "late 20s elegant maturity"
-        ]
-        self.HAIR_COLORS = [
-            "deep sapphire blue", "silver platinum", "vibrant ruby red", "glossy jet black",
-            "luxurious chestnut brown", "emerald green", "vivid amethyst purple",
-            "chocolate brown", "honey blonde", "burgundy red"
-        ]
-        self.EYE_COLORS = [
-            "intense brown", "bright sapphire blue", "emerald green", "golden amber",
-            "fascinating hazel", "deep violet", "piercing emerald", "mysterious gray",
-            "vibrant violet", "intense amber"
-        ]
-        self.HAIR_STYLES = [
-            "long flowing chestnut hair styled in soft waves",
-            "sleek straight long black hair",
-            "luxurious long blonde curls",
-            "elegant updo with loose cascading strands",
-            "voluminous curls with side part",
-            "thick braid over the shoulder",
-            "loose and silky layers",
-            "messy chic bun"
-        ]
-        self.POSES = [
-            "standing with one leg slightly forward, natural elegance",
-            "seated on a chair edge, legs crossed, professional expression",
-            "leaning against a desk, confident look",
-            "walking with subtle grace, light movement",
-            "adjusting hair gently, natural body language"
-        ]
-        self.SETTINGS = [
-            "modern office with elegant decor and warm ambient light",
-            "luxury hotel suite with velvet furnishings and city view",
-            "classic library with wooden shelves and soft reading lamps",
-            "outdoor balcony at sunset with urban skyline",
-            "high-end photo studio with professional soft lighting"
-        ]
-        self.ATMOSPHERES = [
-            "soft professional lighting with smooth skin shadows, perfect color balance",
-            "warm golden hour sunlight creating rich highlights and depth",
-            "moody cinematic lighting with subtle shadow play",
-            "gentle romantic candlelight with warm glows",
-            "sharp studio flash lighting with balanced illumination"
-        ]
-        self.TECHNICAL_DETAILS = (
-            "Captured in ultra HD 16K (15360×8640) vertical 9:16 full body format. "
-            "Canon EOS R5 Cine RAW camera and Canon RF 85mm f/1.2L USM lens at f/1.2 aperture for creamy bokeh. "
-            "ARRI SkyPanel S360-C soft lighting, Path Tracing, PBR, SSS for lifelike skin, and Ray Tracing. "
-            "Photogrammetry-based textures, displacement maps for skin pores, delicate fabric weave. "
-            "Natural hair strand flow, low-angle (knee to head) composition."
-        )
-        self.CONDITION_FIXED = (
-            "Wearing elegant professional attire matching the role, natural posture, confident expression. "
-            "Full body portrait, cinematic tone, vertical 9:16 framing."
-        )
-
-    def _choose_random(self, options: List[str]) -> str:
-        return random.choice(options)
-
-    def generate_single_prompt(self, role: Optional[str] = None) -> str:
-        selected_role = role if role else self._choose_random(self.ROLES)
-        age = self._choose_random(self.AGES)
-        hair_color = self._choose_random(self.HAIR_COLORS)
-        eye_color = self._choose_random(self.EYE_COLORS)
-        hair_style = self._choose_random(self.HAIR_STYLES)
-        pose = self._choose_random(self.POSES)
-        setting = self._choose_random(self.SETTINGS)
-        atmosphere = self._choose_random(self.ATMOSPHERES)
-
-        prompt = f"""
-Highly detailed hyperrealistic full-body portrait of a {selected_role}, {age}, with {hair_style}, {hair_color} hair, and {eye_color} eyes.
-She is {pose}, in a {setting}. {atmosphere}.
-{self.CONDITION_FIXED}
-{self.TECHNICAL_DETAILS}
-"""
-        return prompt.strip()
-
-    def generate_prompt_automatic(self):
-        return self.generate_single_prompt(), ""
-
-# ==============================================================
-#  SAMBANOVA API CONFIG
-# ==============================================================
-
-gen = HyperrealisticPromptGenerator()
-
-# Asegúrate de configurar SAMBANOVA_API_KEY en tu entorno
-API_KEY = os.getenv("SAMBANOVA_API_KEY") 
-API_URL = "https://api.sambanova.ai/v1/chat/completions"
-headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
-
 
+# Procesar imagen a base64
 def process_image(image):
     if image is None:
         return None
@@ -121,12 +14,13 @@ def process_image(image):
     image.save(buffered, format="PNG")
     return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
-
+# Función para llamar a la API Sambanova para análisis de imagen + texto
 def analizar_imagen_y_generar_prompt(image_base64):
+    API_KEY = os.getenv("SAMBANOVA_API_KEY") 
+    API_URL = "https://api.sambanova.ai/v1/chat/completions"
+    headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
     if not API_KEY:
-        return "```\n⚠️ SAMBANOVA_API_KEY no configurada.\n```"
-    
-    # Formato de contenido para el análisis de imagen (puede variar)
+        return "⚠️ SAMBANOVA_API_KEY no configurada."
     messages = [
         {"role": "system", "content": "Describe images in detailed English."},
         {
@@ -137,178 +31,43 @@ def analizar_imagen_y_generar_prompt(image_base64):
             ]
         }
     ]
-    
     json_data = {"model": "Llama-4-Maverick-17B-128E-Instruct", "messages": messages, "stream": False}
     try:
         response = requests.post(API_URL, headers=headers, json=json_data)
         response.raise_for_status()
         text_resp = response.json()["choices"][0]["message"]["content"]
-        return f"```\n{text_resp}\n```"
-    except Exception as e:
-        return f"```\nError analizando imagen: {str(e)}\n```"
-
-
-# ==============================================================
-#  FUNCIÓN PRINCIPAL DE CHAT Y PROMPT (CORREGIDA)
-# ==============================================================
-
-def chat_sambanova(user_message, image_input, auto_mode, chat_history):
-    updated_history = chat_history[:] if chat_history else []
-    image_base64 = process_image(image_input) if image_input else None
-    
-    # 1. Yield inicial para mostrar "Procesando..." y borrar la entrada del usuario
-    yield "", updated_history, "", "Procesando..."
-
-    if not API_KEY:
-        error_msg = "Error: SAMBANOVA_API_KEY no configurada."
-        updated_history.append((user_message, error_msg))
-        # 2. Yield de error
-        yield user_message, updated_history, error_msg, ""
-        return
-
-    if auto_mode and image_base64:
-        prompt = analizar_imagen_y_generar_prompt(image_base64)
-        updated_history.append((user_message or "Análisis automático (Imagen)", f"IA - Prompt generado:\n{prompt}"))
-        # 3. Yield de finalización de análisis
-        yield "", updated_history, "", ""
-        return
-
-    # Preparar el historial de chat para la API
-    messages = [{"role": "system", "content": "Eres un asistente útil"}]
-    for user_msg, ai_msg in updated_history:
-        # Los mensajes del historial deben ser solo de texto para el rol 'user'
-        # Esto previene errores de formato en mensajes pasados.
-        messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]}) 
-        messages.append({"role": "assistant", "content": ai_msg})
-
-    # Preparar el mensaje actual (puede ser texto o multimodal)
-    user_content = [{"type": "text", "text": user_message}]
-    if image_base64:
-        user_content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}})
-    messages.append({"role": "user", "content": user_content})
-
-    json_data = {"model": "Llama-4-Maverick-17B-128E-Instruct", "messages": messages, "stream": True}
-
-    try:
-        response = requests.post(API_URL, headers=headers, json=json_data, stream=True)
-        response.raise_for_status()
-        collected_text = ""
-        updated_history.append((user_message, "")) 
-
-        for line in response.iter_lines(decode_unicode=True):
-            if line.startswith("data: "):
-                json_str = line[len("data: "):]
-                if json_str == "[DONE]":
-                    break
-                try:
-                    data = json.loads(json_str)
-                    delta = data.get("choices", [{}])[0].get("delta", {})
-                    text_fragment = delta.get("content", "")
-                    collected_text += text_fragment
-                    updated_history[-1] = (user_message, collected_text)
-                    # 4. Yield de streaming
-                    yield "", updated_history, "", "Procesando..."
-                except json.JSONDecodeError:
-                    continue
-        # 5. Yield final
-        yield "", updated_history, "", ""
+        return text_resp
     except Exception as e:
-        error_msg = f"Error inesperado de la API: {str(e)}"
-        if updated_history and updated_history[-1][1] == "":
-            updated_history[-1] = (user_message, error_msg)
-        else:
-            updated_history.append((user_message, error_msg))
-        # 6. Yield de error final
-        yield user_message, updated_history, error_msg, ""
-
-def generar_prompt_interno():
-    prompt, _ = gen.generate_single_prompt(), ""
-    # Retorna el prompt como texto de salida y una cadena vacía para el display de error
-    return prompt, ""
-
-# ==============================================================
-#  INTERFAZ GRADIO (CORREGIDA)
-# ==============================================================
-
-css_batuto = """
-body {background-color: #05070A; color: #B0C8FF; font-family: 'Poppins', sans-serif;}
-h1, h2, h3, h4 {color: #5CA8FF; text-align: center;}
-.gradio-container {background-color: #05070A !important;}
-button {background-color: #0B1A33 !important; color: #B0C8FF !important; border-radius: 12px;}
-button:hover {background-color: #1B335F !important;}
-.prompt-output {background-color: #0A0F1A; color: #A8CFFF; border-radius: 10px; padding: 10px;}
-input, textarea {background-color: #0B101A !important; color: #DDE8FF !important;}
-"""
-
-with gr.Blocks(css=css_batuto, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# ⚡ BATUTO / Prompt Studio — Hyperrealistic Generator")
-
-    chat_history = gr.State([])
-    error_display = gr.Textbox(label="System messages", value="", visible=True, interactive=False)
-    
-    chatbot = gr.Chatbot(label="💬 BATUTO Assistant (SambaNova - Llama-4 Maverick)", type='messages')
-    # Cambiado a Textbox para mejor funcionalidad de copia.
-    prompt_output = gr.Textbox(label="🎨 Prompt generado", elem_classes=["prompt-output"], lines=5, max_lines=10) 
-
-    with gr.Row():
-        # La salida msg se usa para limpiar la caja de texto después del envío
-        msg = gr.Textbox(label="Tu mensaje", scale=4, placeholder="Escribe tu mensaje o usa el modo automático...")
-        img_input = gr.Image(label="Sube una imagen (opcional)", type="pil", scale=2)
-
-    with gr.Row():
-        auto_mode = gr.Checkbox(label="Modo automático (Generar prompt desde imagen)", value=False)
-        btn_send = gr.Button("Enviar mensaje", variant="primary")
-        btn_gen_prompt = gr.Button("🎲 Generar prompt automático", variant="secondary")
-        copy_button = gr.Button("📋 Copiar Prompt")
-
-    # Componente para mostrar el estado de carga (Procesando...)
-    loading_state = gr.Textbox(value="", label="Estado", interactive=False) 
-
-    # Asignación de outputs corregida para coincidir con la función chat_sambanova
-    btn_send.click(
-        fn=chat_sambanova,
-        inputs=[msg, img_input, auto_mode, chat_history],
-        outputs=[msg, chatbot, error_display, loading_state]
-    )
-    
-    msg.submit(
-        fn=chat_sambanova,
-        inputs=[msg, img_input, auto_mode, chat_history],
-        outputs=[msg, chatbot, error_display, loading_state]
-    )
-    
-    # La salida del prompt va al Textbox y la segunda salida es para limpiar errores
-    btn_gen_prompt.click(
-        fn=generar_prompt_interno,
-        inputs=[],
-        outputs=[prompt_output, error_display]
-    )
-
-    # CORRECCIÓN DE ERROR: Cambiado _js a js
-    copy_button.click(
-        None,
-        [],
-        [],
-        js="""() => { 
-            // Usa el ID o selector de clase del Textbox
-            const promptBox = document.querySelector('.prompt-output textarea'); 
-            const promptText = promptBox ? promptBox.value : '';
-            
-            if (promptText) {
-                navigator.clipboard.writeText(promptText).then(() => {
-                    alert('✅ Prompt copiado al portapapeles');
-                }).catch(err => {
-                    alert('❌ Error al copiar: ' + err);
-                });
-            } else {
-                alert('❌ No se encontró el prompt para copiar. Genera uno primero.');
-            }
-        }"""
+        return f"Error al analizar imagen: {str(e)}"
+
+# Función principal del chat dinámico
+def chat_mode(user_message, image_input, chat_history, auto_mode):
+    chat_history = chat_history or []
+    image_b64 = process_image(image_input) if image_input else None
+    chat_history.append(("Usuario", user_message))
+    if auto_mode and image_b64:
+        respuesta = analizar_imagen_y_generar_prompt(image_b64)
+    else:
+        respuesta = "Mensaje recibido: " + user_message
+    chat_history.append(("IA", respuesta))
+    return chat_history
+
+# Interfaz Gradio chat dinámica
+with gr.Blocks() as demo:
+    gr.Markdown("# ⚡ BATUTO / Prompt Studio — Chat IA + Imagen")
+    chat_box = gr.Chatbot()
+    msg_input = gr.Textbox(placeholder="Escribe tu mensaje...")
+    upload_img = gr.Image(label="Sube una imagen (opcional)", type="pil")
+    auto_mode = gr.Checkbox(label="Modo automático: analizar imagen para prompt", value=False)
+    send_button = gr.Button("Enviar")
+    chat_state = gr.State([])
+
+    send_button.click(
+        chat_mode,
+        inputs=[msg_input, upload_img, chat_state, auto_mode],
+        outputs=[chat_box, chat_state]
     )
 
 if __name__ == "__main__":
-    try:
-        demo.launch()
-    except Exception as e:
-        print(f"Error al iniciar Gradio: {str(e)}")
-        
+    demo.launch()
+