button

.gitignore

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+/venv

README.md

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+---
+title: Metadata
+emoji: 🐨
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from PIL import Image
+from PIL.ExifTags import TAGS
+import numpy as np
+import re
+
+def extract_metadata(image_file):
+    img = Image.open(image_file)
+    metadata = {}
+
+    # Extract metadata
+    if img.format == 'PNG':
+        metadata = img.info
+    elif img.format in ['JPEG', 'TIFF']:
+        exif_data = img._getexif()
+        if exif_data:
+            for tag, value in exif_data.items():
+                tag_name = TAGS.get(tag, tag)
+                metadata[tag_name] = value
+
+    # Convert image to NumPy array for display
+    img_display = np.array(img)
+
+    # Initialize outputs
+    prompt = 'N/A'
+    negative_prompt = 'N/A'
+    adetailer_prompt_1 = 'N/A'
+    adetailer_prompt_2 = 'N/A'
+
+    # Convert the entire metadata dictionary to a readable string
+    metadata_str = "\n".join([f"{key}: {value}" for key, value in metadata.items()])
+
+    # Parse metadata to extract prompts
+    if 'parameters' in metadata:
+        parameters = metadata['parameters']
+
+        # Use regular expressions to extract prompts
+        # Extract the prompt (before 'Negative prompt:')
+        prompt_match = re.search(r'(.*?)Negative prompt:', parameters, re.DOTALL)
+        if prompt_match:
+            prompt = prompt_match.group(1).strip()
+        else:
+            # If 'Negative prompt:' not found, try to extract prompt up to 'Steps:' or end of string
+            prompt_match = re.search(r'(.*?)(Steps:|$)', parameters, re.DOTALL)
+            if prompt_match:
+                prompt = prompt_match.group(1).strip()
+
+        # Extract the negative prompt
+        negative_prompt_match = re.search(r'Negative prompt:(.*?)(Steps:|$)', parameters, re.DOTALL)
+        if negative_prompt_match:
+            negative_prompt = negative_prompt_match.group(1).strip()
+
+        # Extract ADetailer prompt 1
+        adetailer_prompt_1_match = re.search(r'ADetailer prompt:\s*"(.*?)"', parameters)
+        if adetailer_prompt_1_match:
+            adetailer_prompt_1 = adetailer_prompt_1_match.group(1).strip()
+
+        # Extract ADetailer prompt 2
+        adetailer_prompt_2_match = re.search(r'ADetailer negative prompt 2nd:\s*"(.*?)"', parameters)
+        if adetailer_prompt_2_match:
+            adetailer_prompt_2 = adetailer_prompt_2_match.group(1).strip()
+        
+        seed_match = re.search(r'Seed:\s*(\d+)', parameters)
+        if seed_match:
+            seed_number = seed_match.group(1).strip()
+
+    else:
+        # No 'parameters' field in metadata
+        prompt = 'N/A'
+
+    # Return the outputs, replacing 'original_metadata_output' with 'metadata_str'
+    return img_display, prompt, negative_prompt, seed_number, adetailer_prompt_1, adetailer_prompt_2, metadata_str
+
+# Create the Gradio interface
+with gr.Blocks() as iface:
+    gr.Markdown("# Image Metadata Extractor")
+    gr.Markdown("Upload an image (PNG, JPEG, TIFF) to extract its metadata and parse it for prompts.")
+
+    with gr.Row():
+        with gr.Column():  # First column for image upload and preview
+            image_input = gr.File(label="Upload Image", type="filepath")
+            image_output = gr.Image(label="Image Preview")
+
+        with gr.Column():  # Second column for metadata outputs
+            prompt_output = gr.Textbox(label="Prompt", lines=4, show_copy_button=True)
+            negative_prompt_output = gr.Textbox(label="Negative Prompt", lines=4, show_copy_button=True)
+            seed_output = gr.Textbox(label="Seed Number", lines=1, show_copy_button=True)
+            adetailer_prompt_1_output = gr.Textbox(label="ADetailer Prompt 1", lines=3, show_copy_button=True)
+            adetailer_prompt_2_output = gr.Textbox(label="ADetailer Prompt 2", lines=3, show_copy_button=True)
+            with gr.Row():  # Second column for metadata outputs
+                original_metadata_output = gr.Textbox(label="Original Metadata", lines=10)
+
+    # Set up the input-output relationships
+    image_input.change(
+        fn=extract_metadata,
+        inputs=image_input,
+        outputs=[
+            image_output,
+            prompt_output,
+            negative_prompt_output,
+            seed_output,
+            adetailer_prompt_1_output,
+            adetailer_prompt_2_output,
+            original_metadata_output,
+        ]
+    )
+
+# Launch the app
+iface.launch()

gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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requirements.txt

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+gradio
+Pillow