Update app.py

app.py

@@ -1,373 +1,373 @@
-import gradio as gr
-import json
-from PIL import Image
-import os
-from collections import defaultdict
-
-css = """
-#custom-gallery{--row-height:180px;display:grid;grid-auto-rows:min-content;gap:10px}#custom-gallery .thumbnail-item{height:var(--row-height);width:100%;position:relative;overflow:hidden;border-radius:8px;box-shadow:0 2px 5px rgb(0 0 0 / .1);transition:transform 0.2s ease,box-shadow 0.2s ease}#custom-gallery .thumbnail-item:hover{transform:translateY(-3px);box-shadow:0 4px 12px rgb(0 0 0 / .15)}#custom-gallery .thumbnail-item img{width:auto;height:100%;max-width:100%;max-height:var(--row-height);object-fit:contain;margin:0 auto;display:block}#custom-gallery .thumbnail-item img.portrait{max-width:100%}#custom-gallery .thumbnail-item img.landscape{max-height:100%}.gallery-container{max-height:500px;overflow-y:auto;padding-right:0;--size-80:500px}.thumbnails{display:flex;position:absolute;bottom:0;width:120px;overflow-x:scroll;padding-top:320px;padding-bottom:280px;padding-left:4px;flex-wrap:wrap}
-"""
-
-EMPTY_RESULT = ("Not Available2",) * 15
-
-# ---------- EXTRACTION FUNCTIONS ----------
-def read_metadata(file_path):
-    try:
-        with Image.open(file_path) as img:
-            return img.info
-    except Exception as e:
-        return {"error": f"Error reading file: {str(e)}"}
-
-def extract_workflow_data(file_path):
-    metadata = read_metadata(file_path)
-    if "error" in metadata:
-        return {"error": metadata["error"]}
-
-    if 'prompt' in metadata:
-        try:
-            return json.loads(metadata['prompt'])
-        except json.JSONDecodeError:
-            pass
-
-    for key, value in metadata.items():
-        if isinstance(value, str) and value.strip().startswith('{'):
-            try:
-                return json.loads(value)
-            except json.JSONDecodeError:
-                continue
-    return {"error": "No workflow data found"}
-
-def extract_ksampler_params(workflow_data):
-    seed = steps = cfg = sampler = scheduler = denoise = "Not found"
-    if not isinstance(workflow_data, dict):
-        return seed, steps, cfg, sampler, scheduler, denoise
-    for node in workflow_data.values():
-        if isinstance(node, dict) and node.get("class_type", "") in ["KSampler", "KSampler (Efficient)"]:
-            inputs = node.get("inputs", {})
-            seed = inputs.get("seed", "Not found")
-            steps = inputs.get("steps", "Not found")
-            cfg = inputs.get("cfg", "Not found")
-            sampler = inputs.get("sampler_name", "Not found")
-            scheduler = inputs.get("scheduler", "Not found")
-            denoise = inputs.get("denoise", "Not found")
-            break
-    return str(seed), str(steps), str(cfg), str(sampler), str(scheduler), str(denoise)
-
-def extract_prompts(workflow_data):
-    positive = negative = "Not found"
-    if not isinstance(workflow_data, dict):
-        return positive, negative
-    for node in workflow_data.values():
-        if isinstance(node, dict):
-            class_type = node.get("class_type", "")
-            inputs = node.get("inputs", {})
-            title = node.get("_meta", {}).get("title", "") if node.get("_meta") else ""
-
-            if "Text to Conditioning" in class_type:
-                if "POSITIVE" in title:
-                    positive = inputs.get("text", "Not found")
-                elif "NEGATIVE" in title:
-                    negative = inputs.get("text", "Not found")
-            if "ShowText|pysssss" in class_type:
-                if "text_1" in inputs:
-                    positive = inputs["text_1"]
-                if "text_2" in inputs:
-                    negative = inputs["text_2"]
-            if "DPRandomGenerator" in class_type:
-                if "POSITIVE" in title:
-                    positive = inputs.get("text", "Not found")
-                elif "NEGATIVE" in title:
-                    negative = inputs.get("text", "Not found")
-    return str(positive), str(negative)
-
-def extract_loras(workflow_data):
-    loras = []
-    if not isinstance(workflow_data, dict):
-        return "None found"
-    for node in workflow_data.values():
-        if isinstance(node, dict):
-            inputs = node.get("inputs", {})
-            if "LoraLoader" in node.get("class_type", ""):
-                name = inputs.get("lora_name", "Unknown")
-                strength = inputs.get("strength_model", "Unknown")
-                loras.append(f"{name} (Strength: {strength})")
-            for val in inputs.values():
-                if isinstance(val, str) and "lora:" in val.lower():
-                    loras.append(val)
-    return "\n".join(loras) if loras else "None found"
-
-def extract_model_info(workflow_data):
-    models = []
-    if not isinstance(workflow_data, dict):
-        return "Not found"
-    for node in workflow_data.values():
-        if isinstance(node, dict):
-            inputs = node.get("inputs", {})
-            class_type = node.get("class_type", "")
-            if "CheckpointLoader" in class_type:
-                models.append(inputs.get("ckpt_name", "Unknown"))
-            if "Model Mecha Recipe" in class_type:
-                models.append(inputs.get("model_path", "Unknown"))
-    return "\n".join(models) if models else "Not found"
-
-def extract_image_info(workflow_data):
-    width = height = batch_size = "Not found"
-    if not isinstance(workflow_data, dict):
-        return width, height, batch_size
-    for node in workflow_data.values():
-        if isinstance(node, dict) and node.get("class_type", "") == "EmptyLatentImage":
-            inputs = node.get("inputs", {})
-            width = inputs.get("width", "Not found")
-            height = inputs.get("height", "Not found")
-            batch_size = inputs.get("batch_size", "Not found")
-            break
-    return str(width), str(height), str(batch_size)
-
-def extract_nodes_info(workflow_data):
-    if not isinstance(workflow_data, dict):
-        return "Not found"
-    total_nodes = len(workflow_data)
-    node_types = defaultdict(int)
-    for node in workflow_data.values():
-        if isinstance(node, dict):
-            node_types[node.get("class_type", "Unknown")] += 1
-    summary = f"Total Nodes: {total_nodes}\n"
-    for t, c in sorted(node_types.items()):
-        summary += f"{t}: {c}\n"
-    return summary.strip()
-
-def extract_workflow_as_json(workflow_data):
-    if isinstance(workflow_data, dict):
-        return json.dumps(workflow_data, ensure_ascii=False, indent=2)
-    return "{}"
-# ---------- EXTRACTION FUNCTIONS ----------
-#
-# ---------- IMAGE PROCESSING ----------
-def process_single_image(image_path):
-    """Extract all workflow info from a single image path."""
-    if not image_path:
-        return EMPTY_RESULT
-
-    workflow_data = extract_workflow_data(image_path)
-
-    if isinstance(workflow_data, dict) and "error" not in workflow_data:
-        seed, steps, cfg, sampler, scheduler, denoise = extract_ksampler_params(workflow_data)
-        positive, negative = extract_prompts(workflow_data)
-        loras = extract_loras(workflow_data)
-        models = extract_model_info(workflow_data)
-        width, height, batch = extract_image_info(workflow_data)
-        nodes = extract_nodes_info(workflow_data)
-        full_json = extract_workflow_as_json(workflow_data)
-    else:
-        error = str(workflow_data.get("error", "Unknown error"))
-        seed = steps = cfg = sampler = scheduler = denoise = positive = negative = loras = models = width = height = batch = nodes = full_json = error
-
-    return seed, steps, cfg, sampler, scheduler, denoise, \
-           positive, negative, loras, models, width, height, batch, nodes, full_json
-
-def append_gallery(gallery: list, image: str):
-    """Add a single image to the gallery"""
-    if gallery is None:
-        gallery = []
-    if not image:
-        return gallery, None
-    gallery.append(image)
-    return gallery, None
-
-def extend_gallery(gallery, images):
-    """Extend gallery preserving uniqueness"""
-
-    if gallery is None:
-        gallery = []
-
-    if not images:
-        return gallery
-
-    # Normalize input - Gradio might pass various formats
-    incoming_paths = []
-    if isinstance(images, str):  # Single image path
-        incoming_paths.append(images)
-    elif isinstance(images, list):
-        for img in images:
-            # Handle cases where elements could be tuples from Gallery
-            if isinstance(img, (tuple, list)):
-                incoming_paths.append(str(img[0]))
-            else:
-                incoming_paths.append(str(img))
-
-    unique_incoming = list(set(incoming_paths))  # Avoid duplicates
-
-    seen_paths = {item[0] if isinstance(item, (list, tuple)) else item for item in gallery}
-    new_entries = [path for path in unique_incoming if path not in seen_paths]
-
-    # Create entries matching expected gallery style
-    formatted_new = [(path, '') for path in new_entries]
-
-    updated_gallery = gallery + formatted_new
-
-    return updated_gallery
-
-def process_gallery(gallery, results_state):
-    """Process all images and populate metadata in session."""
-    if not gallery or len(gallery) == 0:
-        # Clear results if nothing left
-        results_state.clear()
-        return EMPTY_RESULT + (results_state,)
-
-    updated_state = {}
-    first_image_result = EMPTY_RESULT
-    try:
-        for item in gallery:
-            path = item if isinstance(item, str) else item[0]
-
-            if path not in results_state:
-                res = process_single_image(path)
-                results_state[path] = res
-                updated_state[path] = res
-
-                if first_image_result == EMPTY_RESULT:
-                    first_image_result = res
-            else:
-                # Already cached
-                res = results_state[path]
-                updated_state[path] = res
-
-                if first_image_result == EMPTY_RESULT:
-                    first_image_result = res
-
-        results_state.update(updated_state)
-        return first_image_result + (results_state,)
-    except Exception as e:
-        print("[ERROR]", str(e))
-        return EMPTY_RESULT + (results_state,)
-
-def get_selection_from_gallery(gallery, results_state, evt: gr.SelectData):
-    """Fetch result for selected image in gallery."""
-    if evt is None or evt.value is None:
-        # No selection: use first image
-        if gallery and len(gallery) > 0:
-            img_path = str(gallery[0][0] if isinstance(gallery[0], (list, tuple)) else gallery[0])
-            if img_path in results_state:
-                return list(results_state[img_path])
-    else:
-        # Handle selection event
-        try:
-            selected_value = evt.value
-            img_path = None
-
-            if isinstance(selected_value, dict) and 'image' in selected_value:
-                img_path = selected_value['image']['path']
-            elif isinstance(selected_value, (list, tuple)):
-                img_path = selected_value[0]
-            else:
-                img_path = str(selected_value)
-
-            if img_path in results_state:
-                return list(results_state[img_path])
-        except Exception as e:
-            print(f"Selection error: {e}")
-
-    # Return empty if no image found
-    return list(EMPTY_RESULT)
-# ---------- IMAGE PROCESSING ----------
-#
-with gr.Blocks(title="ComfyUI Workflow Extractor", css=css, theme="Werli/Purple-Crimson-Gradio-Theme", fill_width=True) as demo:
-    gr.Markdown("# 🛠️ ComfyUI Workflow Information Extractor")
-    gr.Markdown("Upload Multiple ComfyUI-generated images. Extract prompts, parameters, models, and full workflows.")
-    with gr.Row():
-        with gr.Column(scale=2):
-            upload_button = gr.UploadButton(
-                "📁 Upload Multiple Images",
-                file_types=["image"],
-                file_count="multiple",
-                size='lg'
-            )
-            gallery = gr.Gallery(
-                columns=3,
-                show_share_button=False,
-                interactive=True,
-                height='auto',
-                label='Grid of images',
-                preview=False,
-                elem_id='custom-gallery'
-            )
-
-        with gr.Column(scale=3):
-            with gr.Tabs():
-                with gr.Tab("Sampling Parameters"):
-                    with gr.Row():
-                        with gr.Column():
-                            seed_out = gr.Textbox(label="Seed", interactive=False)
-                            steps_out = gr.Textbox(label="Steps", interactive=False)
-                            cfg_out = gr.Textbox(label="CFG Scale", interactive=False)
-                        with gr.Column():
-                            sampler_out = gr.Textbox(label="Sampler", interactive=False)
-                            scheduler_out = gr.Textbox(label="Scheduler", interactive=False)
-                            denoise_out = gr.Textbox(label="Denoise", interactive=False)
-
-                with gr.Tab("Prompts"):
-                    pos_prompt = gr.Textbox(label="Positive Prompt", lines=4, interactive=False, show_copy_button=True)
-                    neg_prompt = gr.Textbox(label="Negative Prompt", lines=4, interactive=False, show_copy_button=True)
-
-                with gr.Tab("Models & LoRAs"):
-                    with gr.Row():
-                        lora_out = gr.Textbox(label="LoRAs", lines=5, interactive=False, show_copy_button=True)
-                        model_out = gr.Textbox(label="Base Models", lines=5, interactive=False, show_copy_button=True)
-
-                with gr.Tab("Image Info"):
-                    with gr.Row():
-                        with gr.Column():
-                            width_out = gr.Textbox(label="Width", interactive=False)
-                            height_out = gr.Textbox(label="Height", interactive=False)
-                            batch_out = gr.Textbox(label="Batch Size", interactive=False)
-                        with gr.Column():
-                            nodes_out = gr.Textbox(label="Node Counts", lines=6, interactive=False)
-
-                with gr.Tab("Full Workflow"):
-                    json_out = gr.Textbox(label="Workflow JSON", lines=20, interactive=True, show_copy_button=True)
-
-    # State to store results per image
-    results_state = gr.State({})
-
-    # Event Connections
-    upload_event = upload_button.upload(
-        fn=extend_gallery,
-        inputs=[gallery, upload_button],
-        outputs=gallery,
-        queue=False
-    )
-
-    upload_event.then(
-        fn=process_gallery,
-        inputs=[gallery, results_state],
-        outputs=[
-            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
-            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
-            batch_out, nodes_out, json_out, results_state
-        ]
-    )
-    gallery.change(
-        fn=process_gallery,
-        inputs=[gallery, results_state],
-        outputs=[
-            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
-            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
-            batch_out, nodes_out, json_out, results_state
-        ],
-        queue=True
-    )
-
-    gallery.select(
-        get_selection_from_gallery,
-        inputs=[gallery, results_state],
-        outputs=[
-            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
-            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
-            batch_out, nodes_out, json_out
-        ]
-    )
-
-    gr.Markdown("---\n💡 **Note:** It's under development.")
-
-if __name__ == "__main__":
-    demo.queue(max_size=10).launch(show_api=False, show_error=True)
+import gradio as gr
+import json
+from PIL import Image
+import os
+from collections import defaultdict
+
+css = """
+#custom-gallery{--row-height:180px;display:grid;grid-auto-rows:min-content;gap:10px}#custom-gallery .thumbnail-item{height:var(--row-height);width:100%;position:relative;overflow:hidden;border-radius:8px;box-shadow:0 2px 5px rgb(0 0 0 / .1);transition:transform 0.2s ease,box-shadow 0.2s ease}#custom-gallery .thumbnail-item:hover{transform:translateY(-3px);box-shadow:0 4px 12px rgb(0 0 0 / .15)}#custom-gallery .thumbnail-item img{width:auto;height:100%;max-width:100%;max-height:var(--row-height);object-fit:contain;margin:0 auto;display:block}#custom-gallery .thumbnail-item img.portrait{max-width:100%}#custom-gallery .thumbnail-item img.landscape{max-height:100%}.gallery-container{max-height:500px;overflow-y:auto;padding-right:0;--size-80:500px}.thumbnails{display:flex;position:absolute;bottom:0;width:120px;overflow-x:scroll;padding-top:320px;padding-bottom:280px;padding-left:4px;flex-wrap:wrap}
+"""
+
+EMPTY_RESULT = ("Not Available",) * 15
+
+# ---------- EXTRACTION FUNCTIONS ----------
+def read_metadata(file_path):
+    try:
+        with Image.open(file_path) as img:
+            return img.info
+    except Exception as e:
+        return {"error": f"Error reading file: {str(e)}"}
+
+def extract_workflow_data(file_path):
+    metadata = read_metadata(file_path)
+    if "error" in metadata:
+        return {"error": metadata["error"]}
+
+    if 'prompt' in metadata:
+        try:
+            return json.loads(metadata['prompt'])
+        except json.JSONDecodeError:
+            pass
+
+    for key, value in metadata.items():
+        if isinstance(value, str) and value.strip().startswith('{'):
+            try:
+                return json.loads(value)
+            except json.JSONDecodeError:
+                continue
+    return {"error": "No workflow data found"}
+
+def extract_ksampler_params(workflow_data):
+    seed = steps = cfg = sampler = scheduler = denoise = "Not found"
+    if not isinstance(workflow_data, dict):
+        return seed, steps, cfg, sampler, scheduler, denoise
+    for node in workflow_data.values():
+        if isinstance(node, dict) and node.get("class_type", "") in ["KSampler", "KSampler (Efficient)"]:
+            inputs = node.get("inputs", {})
+            seed = inputs.get("seed", "Not found")
+            steps = inputs.get("steps", "Not found")
+            cfg = inputs.get("cfg", "Not found")
+            sampler = inputs.get("sampler_name", "Not found")
+            scheduler = inputs.get("scheduler", "Not found")
+            denoise = inputs.get("denoise", "Not found")
+            break
+    return str(seed), str(steps), str(cfg), str(sampler), str(scheduler), str(denoise)
+
+def extract_prompts(workflow_data):
+    positive = negative = "Not found"
+    if not isinstance(workflow_data, dict):
+        return positive, negative
+    for node in workflow_data.values():
+        if isinstance(node, dict):
+            class_type = node.get("class_type", "")
+            inputs = node.get("inputs", {})
+            title = node.get("_meta", {}).get("title", "") if node.get("_meta") else ""
+
+            if "Text to Conditioning" in class_type:
+                if "POSITIVE" in title:
+                    positive = inputs.get("text", "Not found")
+                elif "NEGATIVE" in title:
+                    negative = inputs.get("text", "Not found")
+            if "ShowText|pysssss" in class_type:
+                if "text_1" in inputs:
+                    positive = inputs["text_1"]
+                if "text_2" in inputs:
+                    negative = inputs["text_2"]
+            if "DPRandomGenerator" in class_type:
+                if "POSITIVE" in title:
+                    positive = inputs.get("text", "Not found")
+                elif "NEGATIVE" in title:
+                    negative = inputs.get("text", "Not found")
+    return str(positive), str(negative)
+
+def extract_loras(workflow_data):
+    loras = []
+    if not isinstance(workflow_data, dict):
+        return "None found"
+    for node in workflow_data.values():
+        if isinstance(node, dict):
+            inputs = node.get("inputs", {})
+            if "LoraLoader" in node.get("class_type", ""):
+                name = inputs.get("lora_name", "Unknown")
+                strength = inputs.get("strength_model", "Unknown")
+                loras.append(f"{name} (Strength: {strength})")
+            for val in inputs.values():
+                if isinstance(val, str) and "lora:" in val.lower():
+                    loras.append(val)
+    return "\n".join(loras) if loras else "None found"
+
+def extract_model_info(workflow_data):
+    models = []
+    if not isinstance(workflow_data, dict):
+        return "Not found"
+    for node in workflow_data.values():
+        if isinstance(node, dict):
+            inputs = node.get("inputs", {})
+            class_type = node.get("class_type", "")
+            if "CheckpointLoader" in class_type:
+                models.append(inputs.get("ckpt_name", "Unknown"))
+            if "Model Mecha Recipe" in class_type:
+                models.append(inputs.get("model_path", "Unknown"))
+    return "\n".join(models) if models else "Not found"
+
+def extract_image_info(workflow_data):
+    width = height = batch_size = "Not found"
+    if not isinstance(workflow_data, dict):
+        return width, height, batch_size
+    for node in workflow_data.values():
+        if isinstance(node, dict) and node.get("class_type", "") == "EmptyLatentImage":
+            inputs = node.get("inputs", {})
+            width = inputs.get("width", "Not found")
+            height = inputs.get("height", "Not found")
+            batch_size = inputs.get("batch_size", "Not found")
+            break
+    return str(width), str(height), str(batch_size)
+
+def extract_nodes_info(workflow_data):
+    if not isinstance(workflow_data, dict):
+        return "Not found"
+    total_nodes = len(workflow_data)
+    node_types = defaultdict(int)
+    for node in workflow_data.values():
+        if isinstance(node, dict):
+            node_types[node.get("class_type", "Unknown")] += 1
+    summary = f"Total Nodes: {total_nodes}\n"
+    for t, c in sorted(node_types.items()):
+        summary += f"{t}: {c}\n"
+    return summary.strip()
+
+def extract_workflow_as_json(workflow_data):
+    if isinstance(workflow_data, dict):
+        return json.dumps(workflow_data, ensure_ascii=False, indent=2)
+    return "{}"
+# ---------- EXTRACTION FUNCTIONS ----------
+#
+# ---------- IMAGE PROCESSING ----------
+def process_single_image(image_path):
+    """Extract all workflow info from a single image path."""
+    if not image_path:
+        return EMPTY_RESULT
+
+    workflow_data = extract_workflow_data(image_path)
+
+    if isinstance(workflow_data, dict) and "error" not in workflow_data:
+        seed, steps, cfg, sampler, scheduler, denoise = extract_ksampler_params(workflow_data)
+        positive, negative = extract_prompts(workflow_data)
+        loras = extract_loras(workflow_data)
+        models = extract_model_info(workflow_data)
+        width, height, batch = extract_image_info(workflow_data)
+        nodes = extract_nodes_info(workflow_data)
+        full_json = extract_workflow_as_json(workflow_data)
+    else:
+        error = str(workflow_data.get("error", "Unknown error"))
+        seed = steps = cfg = sampler = scheduler = denoise = positive = negative = loras = models = width = height = batch = nodes = full_json = error
+
+    return seed, steps, cfg, sampler, scheduler, denoise, \
+           positive, negative, loras, models, width, height, batch, nodes, full_json
+
+def append_gallery(gallery: list, image: str):
+    """Add a single image to the gallery"""
+    if gallery is None:
+        gallery = []
+    if not image:
+        return gallery, None
+    gallery.append(image)
+    return gallery, None
+
+def extend_gallery(gallery, images):
+    """Extend gallery preserving uniqueness"""
+
+    if gallery is None:
+        gallery = []
+
+    if not images:
+        return gallery
+
+    # Normalize input - Gradio might pass various formats
+    incoming_paths = []
+    if isinstance(images, str):  # Single image path
+        incoming_paths.append(images)
+    elif isinstance(images, list):
+        for img in images:
+            # Handle cases where elements could be tuples from Gallery
+            if isinstance(img, (tuple, list)):
+                incoming_paths.append(str(img[0]))
+            else:
+                incoming_paths.append(str(img))
+
+    unique_incoming = list(set(incoming_paths))  # Avoid duplicates
+
+    seen_paths = {item[0] if isinstance(item, (list, tuple)) else item for item in gallery}
+    new_entries = [path for path in unique_incoming if path not in seen_paths]
+
+    # Create entries matching expected gallery style
+    formatted_new = [(path, '') for path in new_entries]
+
+    updated_gallery = gallery + formatted_new
+
+    return updated_gallery
+
+def process_gallery(gallery, results_state):
+    """Process all images and populate metadata in session."""
+    if not gallery or len(gallery) == 0:
+        # Clear results if nothing left
+        results_state.clear()
+        return EMPTY_RESULT + (results_state,)
+
+    updated_state = {}
+    first_image_result = EMPTY_RESULT
+    try:
+        for item in gallery:
+            path = item if isinstance(item, str) else item[0]
+
+            if path not in results_state:
+                res = process_single_image(path)
+                results_state[path] = res
+                updated_state[path] = res
+
+                if first_image_result == EMPTY_RESULT:
+                    first_image_result = res
+            else:
+                # Already cached
+                res = results_state[path]
+                updated_state[path] = res
+
+                if first_image_result == EMPTY_RESULT:
+                    first_image_result = res
+
+        results_state.update(updated_state)
+        return first_image_result + (results_state,)
+    except Exception as e:
+        print("[ERROR]", str(e))
+        return EMPTY_RESULT + (results_state,)
+
+def get_selection_from_gallery(gallery, results_state, evt: gr.SelectData):
+    """Fetch result for selected image in gallery."""
+    if evt is None or evt.value is None:
+        # No selection: use first image
+        if gallery and len(gallery) > 0:
+            img_path = str(gallery[0][0] if isinstance(gallery[0], (list, tuple)) else gallery[0])
+            if img_path in results_state:
+                return list(results_state[img_path])
+    else:
+        # Handle selection event
+        try:
+            selected_value = evt.value
+            img_path = None
+
+            if isinstance(selected_value, dict) and 'image' in selected_value:
+                img_path = selected_value['image']['path']
+            elif isinstance(selected_value, (list, tuple)):
+                img_path = selected_value[0]
+            else:
+                img_path = str(selected_value)
+
+            if img_path in results_state:
+                return list(results_state[img_path])
+        except Exception as e:
+            print(f"Selection error: {e}")
+
+    # Return empty if no image found
+    return list(EMPTY_RESULT)
+# ---------- IMAGE PROCESSING ----------
+#
+with gr.Blocks(title="ComfyUI Workflow Extractor", css=css, theme="Werli/Purple-Crimson-Gradio-Theme", fill_width=True) as demo:
+    gr.Markdown("# 🛠️ ComfyUI Workflow Information Extractor")
+    gr.Markdown("Upload Multiple ComfyUI-generated images. Extract prompts, parameters, models, and full workflows.")
+    with gr.Row():
+        with gr.Column(scale=2):
+            upload_button = gr.UploadButton(
+                "📁 Upload Multiple Images",
+                file_types=["image"],
+                file_count="multiple",
+                size='lg'
+            )
+            gallery = gr.Gallery(
+                columns=3,
+                show_share_button=False,
+                interactive=True,
+                height='auto',
+                label='Grid of images',
+                preview=False,
+                elem_id='custom-gallery'
+            )
+
+        with gr.Column(scale=3):
+            with gr.Tabs():
+                with gr.Tab("Sampling Parameters"):
+                    with gr.Row():
+                        with gr.Column():
+                            seed_out = gr.Textbox(label="Seed", interactive=False)
+                            steps_out = gr.Textbox(label="Steps", interactive=False)
+                            cfg_out = gr.Textbox(label="CFG Scale", interactive=False)
+                        with gr.Column():
+                            sampler_out = gr.Textbox(label="Sampler", interactive=False)
+                            scheduler_out = gr.Textbox(label="Scheduler", interactive=False)
+                            denoise_out = gr.Textbox(label="Denoise", interactive=False)
+
+                with gr.Tab("Prompts"):
+                    pos_prompt = gr.Textbox(label="Positive Prompt", lines=4, interactive=False, show_copy_button=True)
+                    neg_prompt = gr.Textbox(label="Negative Prompt", lines=4, interactive=False, show_copy_button=True)
+
+                with gr.Tab("Models & LoRAs"):
+                    with gr.Row():
+                        lora_out = gr.Textbox(label="LoRAs", lines=5, interactive=False, show_copy_button=True)
+                        model_out = gr.Textbox(label="Base Models", lines=5, interactive=False, show_copy_button=True)
+
+                with gr.Tab("Image Info"):
+                    with gr.Row():
+                        with gr.Column():
+                            width_out = gr.Textbox(label="Width", interactive=False)
+                            height_out = gr.Textbox(label="Height", interactive=False)
+                            batch_out = gr.Textbox(label="Batch Size", interactive=False)
+                        with gr.Column():
+                            nodes_out = gr.Textbox(label="Node Counts", lines=6, interactive=False)
+
+                with gr.Tab("Full Workflow"):
+                    json_out = gr.Textbox(label="Workflow JSON", lines=20, interactive=True, show_copy_button=True)
+
+    # State to store results per image
+    results_state = gr.State({})
+
+    # Event Connections
+    upload_event = upload_button.upload(
+        fn=extend_gallery,
+        inputs=[gallery, upload_button],
+        outputs=gallery,
+        queue=False
+    )
+
+    upload_event.then(
+        fn=process_gallery,
+        inputs=[gallery, results_state],
+        outputs=[
+            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
+            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
+            batch_out, nodes_out, json_out, results_state
+        ]
+    )
+    gallery.change(
+        fn=process_gallery,
+        inputs=[gallery, results_state],
+        outputs=[
+            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
+            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
+            batch_out, nodes_out, json_out, results_state
+        ],
+        queue=True
+    )
+
+    gallery.select(
+        get_selection_from_gallery,
+        inputs=[gallery, results_state],
+        outputs=[
+            seed_out, steps_out, cfg_out, sampler_out, scheduler_out, denoise_out,
+            pos_prompt, neg_prompt, lora_out, model_out, width_out, height_out,
+            batch_out, nodes_out, json_out
+        ]
+    )
+
+    gr.Markdown("---\n💡 **Note:** It's under development.")
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch(show_api=False, show_error=True)