Upload 7 files

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+example/asukatest.png filter=lfs diff=lfs merge=lfs -text
+example/cagliostro.png filter=lfs diff=lfs merge=lfs -text
+example/stelle.png filter=lfs diff=lfs merge=lfs -text
+arimakana.png filter=lfs diff=lfs merge=lfs -text
+example/shinji.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,13 @@
 ---
-title: Pnginfo
-emoji: 📉
-colorFrom: purple
-colorTo: red
+title: PNGInfo
+emoji: 📚
+colorFrom: gray
+colorTo: gray
 sdk: gradio
 sdk_version: 4.22.0
 app_file: app.py
 pinned: false
+short_description: Extract and display metadata from AI generated images
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,89 @@
+import gradio as gr
+from PIL import Image, PngImagePlugin
+import json
+import traceback
+
+def extract_metadata(image):
+    if image is None:
+        return "Please upload an image.", {}
+
+    try:
+        metadata = {}
+        if 'metadata' in image.info:
+            metadata = json.loads(image.info['metadata'])
+        elif 'prompt' in image.info:
+            metadata = json.loads(image.info['prompt'])
+        elif 'Comment' in image.info:
+            metadata = json.loads(image.info['Comment'])
+            metadata['model'] = 'NovelAI'
+        elif 'parameters' in image.info:
+            if image.info['parameters'].startswith('{'):
+                parameters_data = json.loads(image.info['parameters'])
+                if 'sui_image_params' in parameters_data:
+                    sui_image_params = parameters_data['sui_image_params']
+                    metadata.update(sui_image_params)
+            else:
+                lines = image.info['parameters'].split('\n')
+                prompt = lines[0].strip()
+                negative_prompt = lines[1].strip().replace('Negative prompt:', '').strip()
+                metadata['prompt'] = prompt
+                metadata['negative_prompt'] = negative_prompt
+                
+                for line in lines[2:]:
+                    line = line.strip()
+                    if line.startswith('Steps:'):
+                        steps_info = line.split(':', 1)[1].strip().split(',')
+                        for info in steps_info:
+                            info = info.strip()
+                            if ':' in info:
+                                key, value = info.split(':', 1)
+                                metadata[key.strip()] = value.strip()
+        else:
+            return "No supported metadata found in the image.", {}
+
+        return "Metadata extracted successfully.", metadata
+    except Exception as e:
+        error_message = f"Error extracting metadata: {str(e)}\n{traceback.format_exc()}"
+        return error_message, {}
+
+def process_image(image):
+    status, metadata = extract_metadata(image)
+    return status, metadata
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Image Metadata Extractor
+        Extract and display metadata from images generated by various AI tools.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="pil", height=480)
+
+        with gr.Column():
+            status_output = gr.Textbox(label="Status")
+            output_metadata = gr.JSON(label="Metadata")
+
+    input_image.change(
+        fn=process_image,
+        inputs=input_image,
+        outputs=[status_output, output_metadata],
+        api_name="interrogate"
+    )
+
+    gr.Examples(
+        examples=[
+            ["example/asukatest.png"],
+            ["example/arimakana.png"],
+            ["example/stelle.png"],
+            ["example/shinji.png"],
+        ],
+        inputs=input_image,
+        outputs=[status_output, output_metadata],
+        fn=process_image,
+        cache_examples=True,
+    )
+
+demo.launch()