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import gradio as gr |
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import requests |
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import json |
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import os |
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import random |
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import base64 |
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import io |
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from PIL import Image |
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from typing import List, Optional |
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class HyperrealisticPromptGenerator: |
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def __init__(self): |
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self.ROLES = [ |
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"nurse", "nun", "maid", "flight attendant", "secretary", "teacher", "schoolgirl", "lawyer", |
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"doctor", "boudoir model", "fitness model", "elegant judge", "seductive librarian", |
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"business executive", "policewoman", "female military officer", "WWII-era secretary", |
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"1960s flight attendant", "seductive maid", "mysterious nurse", "captivating schoolgirl" |
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] |
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self.AGES = [ |
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"early 20s youthful vibrance", |
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"early 20s fresh and vibrant", |
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"mid 20s graceful confidence", |
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"mid 20s elegant and fresh", |
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"early 20s natural glow" |
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] |
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self.HAIR_COLORS = [ |
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"deep sapphire blue", "silver platinum", "vibrant ruby red", "glossy jet black", |
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"luxurious chestnut brown", "emerald green", "vivid amethyst purple", |
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"chocolate brown", "honey blonde", "burgundy red" |
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] |
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self.EYE_COLORS = [ |
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"intense brown", "bright sapphire blue", "emerald green", "golden amber", |
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"fascinating hazel", "deep violet", "piercing emerald", "mysterious gray", |
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"vibrant violet", "intense amber" |
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] |
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self.HAIR_STYLES = [ |
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"long flowing chestnut hair styled in soft waves", |
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"sleek straight long black hair", |
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"luxurious long blonde curls", |
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"elegant updo with loose cascading strands", |
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"glossy long brunette hair parted in the middle", |
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"voluminous curls", |
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"thick braid over the shoulder", |
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"loose and silky layers", |
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"messy chic bun" |
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] |
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self.POSES = [ |
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"standing with one leg slightly forward, skirt shifting gently to subtly reveal lace thong, view from knees to head", |
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"seated on a chair edge, legs crossed, skirt moving slightly, natural sensual expression, low angle from knees", |
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"leaning against a desk with hips cocked, skirt riding up, captured from knees to head", |
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"walking with natural sway, skirt flowing, viewed contrapicado from knees", |
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"adjusting stockings or shoes, skirt slightly lifted revealing thong, viewed low angle knees up" |
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] |
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self.SETTINGS = [ |
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"modern office with elegant decor and warm ambient light", |
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"luxury hotel suite with velvet furnishings and city view", |
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"classic library with wooden shelves and soft reading lamps", |
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"outdoor balcony at sunset with urban skyline", |
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"high-end photo studio with professional soft lighting" |
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] |
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self.ATMOSPHERES = [ |
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"soft professional lighting with smooth skin shadows, perfect color balance", |
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"warm golden hour sunlight creating rich highlights and depth", |
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"moody cinematic lighting with subtle shadow play", |
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"gentle romantic candlelight with warm glows", |
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"sharp studio flash lighting with balanced illumination" |
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] |
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self.TECHNICAL_DETAILS = ( |
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"Captured in ultra HD 16K (15360×8640) vertical 9:16 full body format. " |
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"Canon EOS R5 Cine RAW camera and Canon RF 85mm f/1.2L USM lens at f/1.2 aperture for creamy bokeh and realistic depth of field. " |
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"ARRI SkyPanel S360-C with soft shadowless 3:1 lighting ratio. " |
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"Advanced Path Tracing, Physically Based Rendering (PBR), Subsurface Scattering (SSS) for lifelike skin translucency, " |
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"Ray Tracing for global illumination and reflections. " |
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"Photogrammetry-based texture mapping, displacement maps for skin pores, delicate fabric weave and lace micro-details. " |
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"Natural, physics-driven hair strand flow. " |
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"Composition uses contrapicado low-angle (knee to head) shots emphasizing natural, sensual lingerie reveal." |
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) |
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self.CONDITION_FIXED = ( |
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"Wearing elegant thigh-high stockings, no bra, and high stilettos. " |
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"Delicately revealing a lace thong in a natural, seductive manner, as if caught candidly. " |
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"Age between 20 and 25, radiating youthfulness and fresh allure. " |
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"Pose and framing strictly low-angle, knees to head vertical 9:16 aspect ratio, full body filling the frame." |
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) |
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def _choose_random(self, options: List[str]) -> str: |
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return random.choice(options) |
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def generate_single_prompt(self, role: Optional[str] = None) -> str: |
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selected_role = role if role else self._choose_random(self.ROLES) |
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age = self._choose_random(self.AGES) |
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hair_color = self._choose_random(self.HAIR_COLORS) |
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eye_color = self._choose_random(self.EYE_COLORS) |
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hair_style = self._choose_random(self.HAIR_STYLES) |
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pose = self._choose_random(self.POSES) |
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setting = self._choose_random(self.SETTINGS) |
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atmosphere = self._choose_random(self.ATMOSPHERES) |
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return ( |
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f"``` |
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f"Eyes: {eye_color}\nPose: {pose}\nEnvironment: {setting}\nAtmosphere: {atmosphere}\n" |
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f"Outfit: {self.CONDITION_FIXED}\nTechnical specs: {self.TECHNICAL_DETAILS}\n```" |
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) |
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def generate_prompt_automatic(self): |
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return self.generate_single_prompt() |
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gen = HyperrealisticPromptGenerator() |
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API_KEY = os.getenv("SAMBANOVA_API_KEY") |
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API_URL = "https://api.sambanova.ai/v1/chat/completions" |
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headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} |
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def process_image(image): |
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if image is None: |
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return None |
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buffered = io.BytesIO() |
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image.save(buffered, format="PNG") |
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return base64.b64encode(buffered.getvalue()).decode("utf-8") |
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def analizar_imagen_y_generar_prompt(image_base64): |
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if not API_KEY: |
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return "``````" |
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messages = [ |
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{"role": "system", "content": "Describe images in detailed English."}, |
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{ |
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"role": "user", "content": [ |
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}, |
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{"type": "text", "text": "Provide a detailed English description for a prompt."} |
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] |
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} |
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] |
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json_data = {"model": "Llama-4-Maverick-17B-128E-Instruct", "messages": messages, "stream": False} |
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try: |
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response = requests.post(API_URL, headers=headers, json=json_data) |
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response.raise_for_status() |
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text_resp = response.json()["choices"][0]["message"]["content"] |
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return f"``````" |
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except Exception as e: |
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return f"``````" |
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def chat_sambanova(user_message, image_input, auto_mode, chat_history, loading_state): |
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updated_history = chat_history[:] if chat_history else [] |
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image_base64 = process_image(image_input) if image_input else None |
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# Indicador de carga |
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loading_state = "Procesando..." |
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yield "", updated_history, "", loading_state # Limpiar input, actualizar chat, error vacío, carga activo |
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if not API_KEY: |
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error_msg = "Error: SAMBANOVA_API_KEY no configurada." |
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updated_history.append((user_message, error_msg)) |
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yield "", updated_history, error_msg, "" |
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return |
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if auto_mode and image_base64: |
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prompt = analizar_imagen_y_generar_prompt(image_base64) |
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updated_history.append((user_message or "Análisis automático", f"IA - Prompt generado:\n{prompt}")) |
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yield "", updated_history, "", "" |
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return |
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messages = [{"role": "system", "content": "Eres un asistente útil"}] |
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for user_msg, ai_msg in updated_history: |
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messages.append({"role": "user", "content": [{"type": "text", "text": user_msg}]}) |
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messages.append({"role": "assistant", "content": ai_msg}) |
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user_content = [{"type": "text", "text": user_message}] |
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if image_base64: |
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user_content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}) |
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messages.append({"role": "user", "content": user_content}) |
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json_data = {"model": "Llama-4-Maverick-17B-128E-Instruct", "messages": messages, "stream": True} |
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try: |
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response = requests.post(API_URL, headers=headers, json=json_data, stream=True) |
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response.raise_for_status() |
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collected_text = "" |
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updated_history.append((user_message, "")) |
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for line in response.iter_lines(decode_unicode=True): |
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if line.startswith("data: "): |
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json_str = line[len("data: "):] |
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if json_str == "[DONE]": |
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break |
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try: |
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data = json.loads(json_str) |
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delta = data.get("choices", [{}])[0].get("delta", {}) |
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text_fragment = delta.get("content", "") |
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collected_text += text_fragment |
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updated_history[-1] = (user_message, collected_text) |
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yield "", updated_history, "", "Procesando..." |
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except json.JSONDecodeError: |
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continue |
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yield "", updated_history, "", "" |
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except requests.exceptions.HTTPError as http_err: |
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error_msg = f"Error HTTP {http_err.response.status_code}: {http_err.response.text}" |
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if updated_history: |
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updated_history[-1] = (user_message, error_msg) |
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yield "", updated_history, error_msg, "" |
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except requests.exceptions.ConnectionError: |
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error_msg = "Error: No se pudo conectar con la API de SambaNova. Verifica tu conexión." |
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if updated_history: |
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updated_history[-1] = (user_message, error_msg) |
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yield "", updated_history, error_msg, "" |
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except requests.exceptions.Timeout: |
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error_msg = "Error: La solicitud a la API timed out." |
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if updated_history: |
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updated_history[-1] = (user_message, error_msg) |
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yield "", updated_history, error_msg, "" |
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except Exception as e: |
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error_msg = f"Error inesperado: {str(e)}" |
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if updated_history: |
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updated_history[-1] = (user_message, error_msg) |
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yield "", updated_history, error_msg, "" |
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def generar_prompt_interno(): |
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return gen.generate_prompt_automatic(), "" |
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with gr.Blocks() as demo: |
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gr.Markdown(" |
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chat_history = gr.State([]) |
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error_display = gr.Textbox(label="Mensajes de error", interactive=False, visible=True) |
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loading_state = gr.State("") |
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chatbot = gr.Chatbot(label="Chatbot IA (SambaNova - Llama-4 Maverick)", type='messages') |
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prompt_output = gr.Markdown(label="Prompt Generado", elem_classes=["prompt-output"]) |
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with gr.Row(): |
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msg = gr.Textbox(label="Escribe tu mensaje", scale=4) |
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img_input = gr.Image(label="Subir imagen (opcional)", type="pil", scale=2) |
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with gr.Row(): |
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auto_mode = gr.Checkbox(label="Modo automático (generar prompt desde imagen)", value=False) |
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btn_send = gr.Button("Enviar mensaje", variant="primary") |
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btn_gen_prompt = gr.Button("Generar prompt automático", variant="secondary") |
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copy_button = gr.Button("Copiar Prompt") |
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with gr.Row(): |
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loading = gr.Markdown(value=lambda x: f"**{x}**" if x else "", label="Estado") |
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btn_send.click( |
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fn=chat_sambanova, |
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inputs=[msg, img_input, auto_mode, chat_history, loading_state], |
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outputs=[msg, chatbot, chat_history, error_display, loading] |
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) |
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msg.submit( |
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fn=chat_sambanova, |
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inputs=[msg, img_input, auto_mode, chat_history, loading_state], |
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outputs=[msg, chatbot, chat_history, error_display, loading] |
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) |
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btn_gen_prompt.click( |
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fn=generar_prompt_interno, |
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inputs=[], |
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outputs=[msg, prompt_output] |
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) |
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copy_button.click( |
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fn=None, |
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_js="() => { navigator.clipboard.writeText(document.querySelector('.prompt-output').innerText); }", |
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outputs=None |
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) |
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if __name__ == "__main__": |
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try: |
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demo.launch() |
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except Exception as e: |
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print(f"Error al iniciar Gradio: {str(e)}") |
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