LPX55 commited on
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4378fd8
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1 Parent(s): 45adf28

Update app.py

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Files changed (1) hide show
  1. app.py +41 -37
app.py CHANGED
@@ -7,6 +7,10 @@ from PIL import Image
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  import numpy as np
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  from utils.goat import call_inference
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  import io
 
 
 
 
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  # Ensure using GPU if available
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  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -168,6 +172,43 @@ def predict_image(img, confidence_threshold):
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  }
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  return img_pil, combined_results
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  # Define the Gradio interface
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  with gr.Blocks() as iface:
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  gr.Markdown("# AI Generated Image Classification")
@@ -185,42 +226,5 @@ with gr.Blocks() as iface:
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  gr.Button("Predict").click(fn=predict_image_with_html, inputs=inputs, outputs=outputs)
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- # Define a function to generate the HTML content
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- def generate_results_html(results):
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- html_content = f"""
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- <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet">
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- <div class="container">
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- <div class="row mt-4">
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- <div class="col">
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- <h5>SwinV2/detect</h5>
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- <p>{results.get("SwinV2/detect", "N/A")}</p>
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- </div>
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- <div class="col">
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- <h5>ViT/AI-vs-Real</h5>
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- <p>{results.get("ViT/AI-vs-Real", "N/A")}</p>
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- </div>
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- <div class="col">
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- <h5>Swin/SDXL</h5>
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- <p>{results.get("Swin/SDXL", "N/A")}</p>
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- </div>
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- <div class="col">
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- <h5>Swin/SDXL-FLUX</h5>
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- <p>{results.get("Swin/SDXL-FLUX", "N/A")}</p>
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- </div>
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- <div class="col">
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- <h5>GOAT</h5>
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- <p>{results.get("GOAT", "N/A")}</p>
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- </div>
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- </div>
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- </div>
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- """
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- return html_content
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-
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- # Modify the predict_image function to return the HTML content
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- def predict_image_with_html(img, confidence_threshold):
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- img_pil, results = predict_image(img, confidence_threshold)
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- html_content = generate_results_html(results)
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- return img_pil, html_content
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-
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  # Launch the interface
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  iface.launch()
 
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  import numpy as np
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  from utils.goat import call_inference
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  import io
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+ import warnings
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+
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+ # Suppress warnings
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+ warnings.filterwarnings("ignore", category=UserWarning, message="Using a slow image processor as `use_fast` is unset")
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  # Ensure using GPU if available
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  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
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  }
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  return img_pil, combined_results
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+ # Define a function to generate the HTML content
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+ def generate_results_html(results):
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+ html_content = f"""
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+ <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" rel="stylesheet">
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+ <div class="container">
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+ <div class="row mt-4">
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+ <div class="col">
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+ <h5>SwinV2/detect</h5>
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+ <p>{results.get("SwinV2/detect", "N/A")}</p>
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+ </div>
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+ <div class="col">
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+ <h5>ViT/AI-vs-Real</h5>
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+ <p>{results.get("ViT/AI-vs-Real", "N/A")}</p>
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+ </div>
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+ <div class="col">
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+ <h5>Swin/SDXL</h5>
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+ <p>{results.get("Swin/SDXL", "N/A")}</p>
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+ </div>
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+ <div class="col">
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+ <h5>Swin/SDXL-FLUX</h5>
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+ <p>{results.get("Swin/SDXL-FLUX", "N/A")}</p>
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+ </div>
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+ <div class="col">
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+ <h5>GOAT</h5>
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+ <p>{results.get("GOAT", "N/A")}</p>
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+ </div>
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+ </div>
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+ </div>
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+ """
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+ return html_content
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+
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+ # Modify the predict_image function to return the HTML content
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+ def predict_image_with_html(img, confidence_threshold):
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+ img_pil, results = predict_image(img, confidence_threshold)
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+ html_content = generate_results_html(results)
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+ return img_pil, html_content
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+
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  # Define the Gradio interface
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  with gr.Blocks() as iface:
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  gr.Markdown("# AI Generated Image Classification")
 
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  gr.Button("Predict").click(fn=predict_image_with_html, inputs=inputs, outputs=outputs)
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  # Launch the interface
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  iface.launch()