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",
            "early 20s fresh and vibrant",
            "mid 20s graceful confidence",
            "mid 20s elegant and fresh",
            "early 20s natural glow"
        ]
        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",
            "glossy long brunette hair parted in the middle",
            "voluminous curls",
            "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"""
        """
        return prompt

    def generate_prompt_automatic(self):
        return self.generate_single_prompt()


# ==============================================================
#  CONFIGURACIÓN SAMBANOVA API
# ==============================================================

gen = HyperrealisticPromptGenerator()

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 "``````"
    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 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"``````"
    except Exception:
        return f"``````"


# ==============================================================
#  FUNCIÓN PRINCIPAL DE CHAT Y PROMPT
# ==============================================================

def chat_sambanova(user_message, image_input, auto_mode, chat_history, loading_state):
    updated_history = chat_history[:] if chat_history else []
    image_base64 = process_image(image_input) if image_input else None

    loading_state = "Procesando..."
    yield "", updated_history, "", loading_state

    if not API_KEY:
        error_msg = "Error: SAMBANOVA_API_KEY no configurada."
        updated_history.append((user_message, error_msg))
        yield "", 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", f"IA - Prompt generado:\n{prompt}"))
        yield "", updated_history, "", ""
        return

    messages = [{"role": "system", "content": "Eres un asistente útil"}]
    for user_msg, ai_msg in updated_history:
        messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]})
        messages.append({"role": "assistant", "content": ai_msg})

    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)
                    yield "", updated_history, "", "Procesando..."
                except json.JSONDecodeError:
                    continue
        yield "", updated_history, "", ""
    except Exception as e:
        error_msg = f"Error inesperado: {str(e)}"
        updated_history[-1] = (user_message, error_msg)
        yield "", updated_history, error_msg, ""


def generar_prompt_interno():
    return gen.generate_prompt_automatic(), ""


# ==============================================================
#  INTERFAZ GRADIO CON MODO OSCURO Y ESTILO BATUTO
# ==============================================================

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="gradio/soft") as demo:
    gr.Markdown("# ⚡ BATUTO / Prompt Studio — Hyperrealistic Generator")

    chat_history = gr.State([])
    error_display = gr.Textbox(label="System messages", interactive=False, visible=True)
    loading_state = gr.State("")

    chatbot = gr.Chatbot(label="💬 BATUTO Assistant (SambaNova - Llama-4 Maverick)", type='messages')
    prompt_output = gr.Markdown(label="🎨 Prompt generado", elem_classes=["prompt-output"])

    with gr.Row():
        msg = gr.Textbox(label="Tu mensaje", scale=4)
        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")

    with gr.Row():
        loading = gr.Markdown(value=lambda x: f"**{x}**" if x else "", label="Estado")

    btn_send.click(
        fn=chat_sambanova,
        inputs=[msg, img_input, auto_mode, chat_history, loading_state],
        outputs=[msg, chatbot, chat_history, error_display, loading]
    )
    msg.submit(
        fn=chat_sambanova,
        inputs=[msg, img_input, auto_mode, chat_history, loading_state],
        outputs=[msg, chatbot, chat_history, error_display, loading]
    )
    btn_gen_prompt.click(
        fn=generar_prompt_interno,
        inputs=[],
        outputs=[msg, prompt_output]
    )

    # ✅ Botón copiar funcional con alerta
    copy_button.click(
        None,
        [],
        [],
        _js="""() => { 
            const el = document.querySelector('.prompt-output'); 
            if (el) { 
                navigator.clipboard.writeText(el.innerText); 
                alert('✅ Prompt copiado al portapapeles');
            } else { 
                alert('❌ No se encontró el prompt para copiar');
            } 
        }"""
    )

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