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import gradio as gr |
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from PIL import Image, PngImagePlugin |
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import json |
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import traceback |
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def extract_metadata(image): |
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if image is None: |
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return "Please upload an image.", {} |
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try: |
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metadata = {} |
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if 'metadata' in image.info: |
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metadata = json.loads(image.info['metadata']) |
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elif 'prompt' in image.info: |
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metadata = json.loads(image.info['prompt']) |
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elif 'Comment' in image.info: |
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metadata = json.loads(image.info['Comment']) |
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metadata['model'] = 'NovelAI' |
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elif 'parameters' in image.info: |
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if image.info['parameters'].startswith('{'): |
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parameters_data = json.loads(image.info['parameters']) |
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if 'sui_image_params' in parameters_data: |
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sui_image_params = parameters_data['sui_image_params'] |
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metadata.update(sui_image_params) |
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else: |
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metadata = parameters_data |
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else: |
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lines = image.info['parameters'].split('\n') |
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prompt = lines[0].strip() |
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negative_prompt = lines[1].strip().replace('Negative prompt:', '').strip() |
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metadata['prompt'] = prompt |
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metadata['negative_prompt'] = negative_prompt |
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for line in lines[2:]: |
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line = line.strip() |
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if line.startswith('Steps:'): |
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steps_info = line.split(':', 1)[1].strip().split(',') |
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for info in steps_info: |
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info = info.strip() |
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if ':' in info: |
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key, value = info.split(':', 1) |
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metadata[key.strip()] = value.strip() |
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else: |
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return "No supported metadata found in the image.", {} |
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return "Metadata extracted successfully.", metadata |
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except Exception as e: |
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error_message = f"Error extracting metadata: {str(e)}\n{traceback.format_exc()}" |
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return error_message, {} |
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def process_image(image): |
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status, metadata = extract_metadata(image) |
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return status, metadata |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# Image Metadata Extractor |
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Extract and display metadata from images generated by various AI tools. |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(label="Input Image", type="pil", height=480) |
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with gr.Column(): |
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status_output = gr.Textbox(label="Status") |
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output_metadata = gr.JSON(label="Metadata") |
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input_image.change( |
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fn=process_image, |
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inputs=input_image, |
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outputs=[status_output, output_metadata], |
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api_name="interrogate" |
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) |
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gr.Examples( |
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examples=[ |
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["example/asukatest.png"], |
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["example/arimakana.png"], |
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["example/stelle.png"], |
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["example/shinji.png"], |
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], |
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inputs=input_image, |
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outputs=[status_output, output_metadata], |
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fn=process_image, |
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cache_examples=True, |
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) |
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demo.launch() |