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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from diffusers import StableDiffusionPipeline, DiffusionPipeline
import requests
from PIL import Image
import io
import base64
# Configuraci贸n de modelos libres
MODELS = {
"text": {
"microsoft/DialoGPT-medium": "Chat conversacional",
"gpt2": "Generaci贸n de texto",
"distilgpt2": "GPT-2 optimizado",
"EleutherAI/gpt-neo-125M": "GPT-Neo peque帽o"
},
"image": {
"runwayml/stable-diffusion-v1-5": "Stable Diffusion v1.5",
"CompVis/stable-diffusion-v1-4": "Stable Diffusion v1.4"
}
}
# Cache para los modelos
model_cache = {}
def load_text_model(model_name):
"""Cargar modelo de texto"""
if model_name not in model_cache:
print(f"Cargando modelo de texto: {model_name}")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Configurar para chat si es DialoGPT
if "dialogpt" in model_name.lower():
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
model_cache[model_name] = {
"tokenizer": tokenizer,
"model": model,
"type": "text"
}
return model_cache[model_name]
def load_image_model(model_name):
"""Cargar modelo de imagen"""
if model_name not in model_cache:
print(f"Cargando modelo de imagen: {model_name}")
pipe = StableDiffusionPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
model_cache[model_name] = {
"pipeline": pipe,
"type": "image"
}
return model_cache[model_name]
def generate_text(prompt, model_name, max_length=100):
"""Generar texto con el modelo seleccionado"""
try:
model_data = load_text_model(model_name)
tokenizer = model_data["tokenizer"]
model = model_data["model"]
# Preparar input
inputs = tokenizer.encode(prompt, return_tensors="pt")
# Generar
with torch.no_grad():
outputs = model.generate(
inputs,
max_length=max_length,
num_return_sequences=1,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
# Decodificar respuesta
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Para DialoGPT, extraer solo la respuesta del asistente
if "dialogpt" in model_name.lower():
response = response.replace(prompt, "").strip()
return response
except Exception as e:
return f"Error generando texto: {str(e)}"
def generate_image(prompt, model_name, num_inference_steps=20):
"""Generar imagen con el modelo seleccionado"""
try:
model_data = load_image_model(model_name)
pipeline = model_data["pipeline"]
# Generar imagen
image = pipeline(
prompt,
num_inference_steps=num_inference_steps,
guidance_scale=7.5
).images[0]
return image
except Exception as e:
return f"Error generando imagen: {str(e)}"
def chat_with_model(message, history, model_name):
"""Funci贸n de chat para DialoGPT"""
try:
model_data = load_text_model(model_name)
tokenizer = model_data["tokenizer"]
model = model_data["model"]
# Construir historial de conversaci贸n
conversation = ""
for user_msg, bot_msg in history:
conversation += f"User: {user_msg}\n"
if bot_msg:
conversation += f"Assistant: {bot_msg}\n"
conversation += f"User: {message}\nAssistant:"
# Generar respuesta
inputs = tokenizer.encode(conversation, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model.generate(
inputs,
max_length=inputs.shape[1] + 50,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extraer solo la respuesta del asistente
response = response.split("Assistant:")[-1].strip()
return response
except Exception as e:
return f"Error en el chat: {str(e)}"
# Interfaz de Gradio
with gr.Blocks(title="Modelos Libres de IA", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 馃 Modelos Libres de IA")
gr.Markdown("### Genera texto e im谩genes sin l铆mites de cuota")
with gr.Tabs():
# Tab de Generaci贸n de Texto
with gr.TabItem("馃摑 Generaci贸n de Texto"):
with gr.Row():
with gr.Column():
text_model = gr.Dropdown(
choices=list(MODELS["text"].keys()),
value="microsoft/DialoGPT-medium",
label="Modelo de Texto"
)
text_prompt = gr.Textbox(
label="Prompt",
placeholder="Escribe tu prompt aqu铆...",
lines=3
)
max_length = gr.Slider(
minimum=50,
maximum=200,
value=100,
step=10,
label="Longitud m谩xima"
)
text_btn = gr.Button("Generar Texto", variant="primary")
with gr.Column():
text_output = gr.Textbox(
label="Resultado",
lines=10,
interactive=False
)
text_btn.click(
generate_text,
inputs=[text_prompt, text_model, max_length],
outputs=text_output
)
# Tab de Chat
with gr.TabItem("馃挰 Chat"):
with gr.Row():
with gr.Column():
chat_model = gr.Dropdown(
choices=["microsoft/DialoGPT-medium"],
value="microsoft/DialoGPT-medium",
label="Modelo de Chat"
)
with gr.Column():
chatbot = gr.Chatbot(
label="Chat",
height=400
)
chat_input = gr.Textbox(
label="Mensaje",
placeholder="Escribe tu mensaje...",
lines=2
)
chat_btn = gr.Button("Enviar", variant="primary")
chat_btn.click(
chat_with_model,
inputs=[chat_input, chatbot, chat_model],
outputs=[chatbot],
clear_input=True
)
chat_input.submit(
chat_with_model,
inputs=[chat_input, chatbot, chat_model],
outputs=[chatbot],
clear_input=True
)
# Tab de Generaci贸n de Im谩genes
with gr.TabItem("馃帹 Generaci贸n de Im谩genes"):
with gr.Row():
with gr.Column():
image_model = gr.Dropdown(
choices=list(MODELS["image"].keys()),
value="runwayml/stable-diffusion-v1-5",
label="Modelo de Imagen"
)
image_prompt = gr.Textbox(
label="Prompt de Imagen",
placeholder="Describe la imagen que quieres generar...",
lines=3
)
steps = gr.Slider(
minimum=10,
maximum=50,
value=20,
step=5,
label="Pasos de inferencia"
)
image_btn = gr.Button("Generar Imagen", variant="primary")
with gr.Column():
image_output = gr.Image(
label="Imagen Generada",
type="pil"
)
image_btn.click(
generate_image,
inputs=[image_prompt, image_model, steps],
outputs=image_output
)
# Configuraci贸n para Hugging Face Spaces
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
) |