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| import gradio as gr | |
| from dotenv import load_dotenv | |
| from transformers import pipeline | |
| import urllib.request | |
| from PIL import Image | |
| # Load environment variables from the .env file de forma local | |
| load_dotenv() | |
| import base64 | |
| with open("Iso_Logotipo_Ceibal.png", "rb") as image_file: | |
| encoded_image = base64.b64encode(image_file.read()).decode() | |
| import os | |
| from openai import OpenAI | |
| es_en_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-es-en") | |
| def translate_text(text): | |
| text = es_en_translator(text)[0].get("translation_text") # type: ignore | |
| return text | |
| client = OpenAI() | |
| client.api_key = os.environ["OPENAI_API_KEY"] | |
| def get_image(text: str, translate: bool): | |
| print(text) | |
| if translate: | |
| text = translate_text(text) | |
| response = client.images.generate( | |
| model="dall-e-3", | |
| prompt=text, | |
| size="1024x1024", | |
| quality="standard", | |
| n=1, | |
| ) # ,response_format="b64_json" | |
| print(response) | |
| urllib.request.urlretrieve(response.data[0].url, "img.png") # type: ignore | |
| img = Image.open("img.png") | |
| return img, response.data[0].revised_prompt | |
| # return response["data"][0]["url"] # type: ignore | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| <center> | |
| <h1> | |
| Uso de AI para la generación de imagenes a partir de texto. | |
| </h1> | |
| <img src='data:image/jpg;base64,{}' width=200px> | |
| <h2> | |
| Con este espacio podrás generar imagenes a partir de texto. Utiliza el modelo DALL-E 3 de OpenAI. | |
| </h2> | |
| <h3> | |
| Tenés que tener en cuenta que el modelo ahora toma el mensaje predeterminado proporcionado y lo reescribe automáticamente por razones de seguridad y para agregar más detalles. | |
| </h3> | |
| </center> | |
| """.format( | |
| encoded_image | |
| ) | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown( | |
| "El modelo funciona mejor con entradas en inglés, podés elegir traducir al inglés con este check:" | |
| ) | |
| check = gr.Checkbox(label="Traducir al inglés") | |
| with gr.Row(): | |
| gr.Markdown("Primero debes ingresar el texto para generar la imagen:") | |
| with gr.Row(): | |
| with gr.Column(scale=4): | |
| prompt = gr.Textbox( | |
| label="Texo base para generar la imagen" | |
| ) # Give prompt some real estate | |
| with gr.Column(scale=1, min_width=50): | |
| btn = gr.Button("Generar") # Submit button side by side! | |
| with gr.Column(): | |
| text_output = gr.TextArea(label="Prompt revisado por el modelo:") | |
| output = gr.Image( | |
| label="Resultado", height=1024, width=1024 | |
| ) # Move the output up too | |
| # examples = gr.Examples( | |
| # inputs=[prompt] | |
| # examples=[["Un perro en el parque", "low quality"]], | |
| # ) | |
| btn.click( | |
| fn=get_image, | |
| inputs=[prompt, check], | |
| outputs=[output, text_output], | |
| ) # steps,guidance,width,height] | |
| demo.queue() | |
| demo.launch(debug=True) | |