new api
Browse files- .env +1 -0
- app.py +63 -0
- requirements.txt +0 -0
.env
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URL = https://api-dev.storyface.ai/api/images/swap_face_on_image
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app.py
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import gradio as gr
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import requests
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from PIL import Image
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import io
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from gradio.themes.base import Base
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import os
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from dotenv import load_dotenv
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load_dotenv()
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def process_images(face_image, model_image, watermark = False, vignette = False, quality = 100):
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# Convertir las imágenes PIL a bytes
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model_img_bytes = io.BytesIO()
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model_image.save(model_img_bytes, format='PNG')
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face_img_bytes = io.BytesIO()
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face_image.save(face_img_bytes, format='PNG')
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# Configurar los datos para la solicitud HTTP
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url = os.getenv('URL')
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files = [
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('images', ('face.png', face_img_bytes.getvalue(), 'image/jpeg')),
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('images', ('model.png', model_img_bytes.getvalue(), 'image/png'))
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]
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data = {
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'watermark': 0,
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'quality': quality
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}
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response = requests.post(url, files=files, data=data)
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if response.status_code == 200:
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# Log the Content-Type to ensure it's an image
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#print("Content-Type:", response.headers.get('Content-Type'))
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#print(response.content)
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# Attempt to open the response content as an image
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try:
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return Image.open(io.BytesIO(response.content))
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except Exception as e:
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print(f"Error opening image: {e}")
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return None # Return None or handle the error as needed
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else:
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raise ValueError(f"Error in the request: Status Code {response.status_code}")
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# Crear la interfaz de Gradio
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iface = gr.Interface(
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fn=process_images,
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inputs=[
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gr.Image(type="pil", label="Face Image"),
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gr.Image(type="pil", label="Model Image")
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],
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outputs=gr.Image(type="pil", label="Result Image"),
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title="StoryFace Internal Tool",
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description="<h2 style='text-align: center; font-size: 24px;'>Take a photo or upload a clear photo of your face, upload a photo of the model you want to see yourself in, and press the submit button..</h2>",
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allow_flagging="never"
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)
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# Ejecutar la aplicación
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iface.launch()
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requirements.txt
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Binary file (5.96 kB). View file
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