app.py

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"}


def process_image(image):
    if image is None:
        return None
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")


def analizar_imagen_y_generar_prompt(image_base64):
    if not API_KEY:
        return "```\n⚠️ SAMBANOVA_API_KEY no configurada.\n```"
    
    # Formato de contenido para el análisis de imagen (puede variar)
    messages = [
        {"role": "system", "content": "Describe images in detailed English."},
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
                {"type": "text", "text": "Provide a detailed English description suitable for a prompt."}
            ]
        }
    ]
    
    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, "", ""
    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.');
            }
        }"""
    )

if __name__ == "__main__":
    try:
        demo.launch()
    except Exception as e:
        print(f"Error al iniciar Gradio: {str(e)}")