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README.md

@@ -10,7 +10,7 @@ app_file: space.py
 ---
 
 # `gradio_imagemeta`
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.4%20-%20blue"> <a href="https://huggingface.co/spaces/elismasilva/gradio_imagemeta"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue"></a><p><span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_imagemeta'>Component GitHub Code</a></span></p>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.6%20-%20blue"> <a href="https://huggingface.co/spaces/elismasilva/gradio_imagemeta"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue"></a><p><span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_imagemeta'>Component GitHub Code</a></span></p>
 
 Image Preview with Metadata for Gradio Interface
 
@@ -41,10 +41,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -62,101 +61,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     """Root configuration for image properties, including nested image settings."""
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    """
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    """
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     """
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     """
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     """
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     """
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -263,7 +235,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

app.py

@@ -4,10 +4,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -25,101 +24,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     """Root configuration for image properties, including nested image settings."""
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    """
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    """
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     """
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     """
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     """
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     """
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -226,7 +198,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

space.py

@@ -44,10 +44,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -65,101 +64,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     \"\"\"Root configuration for image properties, including nested image settings.\"\"\"
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    \"\"\"
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    \"\"\"
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     \"\"\"
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     \"\"\"
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     \"\"\"
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     \"\"\"
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -266,7 +238,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

src/README.md

@@ -10,7 +10,7 @@ app_file: space.py
 ---
 
 # `gradio_imagemeta`
-<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.4%20-%20blue"> <a href="https://huggingface.co/spaces/elismasilva/gradio_imagemeta"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue"></a><p><span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_imagemeta'>Component GitHub Code</a></span></p>
+<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%200.0.6%20-%20blue"> <a href="https://huggingface.co/spaces/elismasilva/gradio_imagemeta"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue"></a><p><span>💻 <a href='https://github.com/DEVAIEXP/gradio_component_imagemeta'>Component GitHub Code</a></span></p>
 
 Image Preview with Metadata for Gradio Interface
 
@@ -41,10 +41,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -62,101 +61,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     """Root configuration for image properties, including nested image settings."""
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    """
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    """
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     """
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     """
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     """
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     """
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -263,7 +235,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

src/backend/gradio_imagemeta/helpers.py

@@ -1,10 +1,57 @@
-from dataclasses import fields
+from dataclasses import fields, is_dataclass
 import os
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 from PIL import Image, PngImagePlugin, ExifTags
 import numpy as np
 from gradio import image_utils
+from gradio_propertysheet.helpers import build_path_to_metadata_key_map
+
+def infer_type(s: str):
+    """
+    Infers and converts a string to the most likely data type.
+
+    It attempts conversions in the following order:
+    1. Integer
+    2. Float
+    3. Boolean (case-insensitive 'true' or 'false')
+    If all conversions fail, it returns the original string.
+
+    Args:
+        s: The input string to be converted.
+
+    Returns:
+        The converted value (int, float, bool) or the original string.
+    """
+    if not isinstance(s, str):
+        # If the input is not a string, return it as is.
+        return s
+
+    # 1. Try to convert to an integer
+    try:
+        return int(s)
+    except ValueError:
+        # Not an integer, continue...
+        pass
+
+    # 2. Try to convert to a float
+    try:
+        return float(s)
+    except ValueError:
+        # Not a float, continue...
+        pass
+    
+    # 3. Check for a boolean value
+    # This explicit check is important because bool('False') evaluates to True.
+    s_lower = s.lower()
+    if s_lower == 'true':
+        return True
+    if s_lower == 'false':
+        return False
+        
+    # 4. If nothing else worked, return the original string
+    return s
+
 
 def extract_metadata(image_data: str | Path | Image.Image | np.ndarray | None, only_custom_metadata: bool = True) -> Dict[str, Any]:
     """
@@ -143,46 +190,99 @@ def add_metadata(image_data: str | Path | Image.Image | np.ndarray, save_path: s
         return True
     except Exception:
         return False
-    
-def transfer_metadata(output_fields: List[Any], metadata: Dict[str, Any], dataclass_fields: Dict[str, str]) -> List[Any]:
+
+def transfer_metadata(
+    output_fields: List[Any], 
+    metadata: Dict[str, Any],
+    propertysheet_map: Optional[Dict[int, Dict[str, Any]]] = None
+) -> List[Any]:
     """
-    Maps metadata to a list of output components based on their labels.
+    Maps a flat metadata dictionary to a list of Gradio UI components, including
+    complex, nested PropertySheets.
+
+    This function is UI-agnostic. It populates standard components based on their
+    labels. For PropertySheet components, it uses a provided map to understand
+    which dataclass type to construct and which metadata keys to use for building
+    the necessary prefixes to find the correct values.
 
     Args:
-        output_fields: List of components (e.g., Textbox, PropertySheet).
-        metadata: Dictionary of extracted image metadata.
-        dataclass_fields: Dictionary mapping component labels (e.g., 'Model') to field paths (e.g., 'image_settings.model' or 'description').
+        output_fields (List[Any]): The list of Gradio components to be updated.
+        metadata (Dict[str, Any]): The flat dictionary of metadata extracted from an image.
+        propertysheet_map (Optional[Dict[int, Dict[str, Any]]]): 
+            A dictionary mapping the `id()` of each PropertySheet component to its
+            configuration. The configuration dictionary should contain:
+            - "type" (Type): The dataclass type to construct (e.g., `PropertyConfig`).
+            - "prefixes" (List[str]): A list of keys from the `metadata` dictionary
+              whose values should be used to build the metadata key prefix.
+              Example: `{"type": MyDataClass, "prefixes": ["Restorer", "Image Restore Engine"]}`
 
     Returns:
-        List of values (strings for Textbox, nested dictionary for PropertySheet) in the same order as output_fields.
+        List[Any]: A list of `gr.update` objects or `gr.skip()` values, ready to be
+                   returned by a Gradio event listener function.
     """
+    if propertysheet_map is None:
+        propertysheet_map = {}
+        
     output_values = [None] * len(output_fields)
-    for i, component in enumerate(output_fields):
-        label = getattr(component, 'label', None)
-
-        # Check if the component is a PropertySheet via root_label attribute
-        is_property_sheet = hasattr(component, 'root_label')
-        if is_property_sheet:
-            # Create nested dictionary for PropertySheet
-            updated_config = {}
-            for dataclass_label, field_path in dataclass_fields.items():
-                value = str(metadata.get(dataclass_label, 'None'))
-                value = None if value == 'None' else value
-                # Split field_path into parts (e.g., 'image_settings.model' -> ['image_settings', 'model'])
-                parts = field_path.split('.')
-                # Build nested structure in dictionary
-                current = updated_config
-                for part in parts[:-1]:
-                    if part not in current:
-                        current[part] = {}
-                    current = current[part]
-                # Assign value to final field
-                current[parts[-1]] = value
-            output_values[i] = updated_config
-        else:
-            # For other components (e.g., Textbox), assign raw value
-            value = str(metadata.get(label, None)) if label else None
-            value = None if value == 'None' else value
-            output_values[i] = value
+    component_to_index = {id(comp): i for i, comp in enumerate(output_fields)}
+
+    base_label_map = {}
+    for key, value in metadata.items():
+        base_label = key.rsplit(' - ', 1)[-1]
+        base_label_map[base_label] = value
     
+    for component in output_fields:
+        comp_id = id(component)
+        output_index = component_to_index.get(comp_id)
+        if output_index is None:
+            continue
+            
+        # --- Logic for PropertySheets ---
+        if comp_id in propertysheet_map:
+            sheet_info = propertysheet_map[comp_id]
+            dc_type = sheet_info.get("type")
+            prefix_keys = sheet_info.get("prefixes", [])
+            
+            # Build the list of actual prefix strings by looking up their values in the metadata
+            prefix_values = [metadata.get(key, "") for key in prefix_keys]
+            prefix_values = [p for p in prefix_values if p]
+
+            if not dc_type or not is_dataclass(dc_type):
+                continue
+            
+            # Build the map from the dataclass structure to the expected metadata keys
+            path_to_key_map = build_path_to_metadata_key_map(dc_type, prefix_values)
+            
+            # Get the base instance to start populating
+            instance_to_populate = getattr(component, '_dataclass_value', None)
+            if not is_dataclass(instance_to_populate):
+                 instance_to_populate = dc_type() # Create a new instance if the current one is invalid
+
+            # Populate the instance by iterating through the path map
+            for path, metadata_key in path_to_key_map.items():
+                if metadata_key in metadata:
+                    value_from_meta = metadata[metadata_key]
+                    
+                    parts = path.split('.')
+                    obj_to_set = instance_to_populate
+                    try:
+                        for part in parts[:-1]:
+                            obj_to_set = getattr(obj_to_set, part)
+                        
+                        final_field_name = parts[-1]
+                        converted_value = infer_type(value_from_meta)
+                        setattr(obj_to_set, final_field_name, converted_value)
+                    except (AttributeError, KeyError, ValueError, TypeError) as e:
+                        print(f"Warning (transfer_metadata): Could not set value for path '{path}'. Error: {e}")
+            
+            output_values[output_index] = value=instance_to_populate
+            
+        # --- Logic for Standard Gradio Components ---
+        else:
+            label = getattr(component, 'label', None)
+            if label and label in base_label_map:
+                value = base_label_map[label]
+                value = None if value == 'None' else value
+                output_values[output_index] = infer_type(value)
+
     return output_values

src/demo/app.py

@@ -4,10 +4,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -25,101 +24,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     """Root configuration for image properties, including nested image settings."""
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    """
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    """
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     """
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     """
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     """
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     """
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -226,7 +198,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

src/demo/space.py

@@ -44,10 +44,9 @@ import gradio as gr
 from gradio_imagemeta import ImageMeta
 from gradio_imagemeta.helpers import extract_metadata, add_metadata, transfer_metadata
 from gradio_propertysheet import PropertySheet
-from gradio_propertysheet.helpers import build_dataclass_fields, create_dataclass_instance
+from gradio_propertysheet.helpers import  flatten_dataclass_with_labels
 from pathlib import Path
 
-
 output_dir = Path("outputs")
 output_dir.mkdir(exist_ok=True)
 
@@ -65,101 +64,74 @@ class ImageSettings:
 @dataclass
 class PropertyConfig:
     \"\"\"Root configuration for image properties, including nested image settings.\"\"\"
-    image_settings: ImageSettings = field(default_factory=ImageSettings)
+    image_settings: ImageSettings = field(default_factory=ImageSettings,  metadata={"label": "Image Settings"})
     description: str = field(default="", metadata={"label": "Description"})
 
-def infer_type(s: str):
-    \"\"\"
-    Infers and converts a string to the most likely data type.
-
-    It attempts conversions in the following order:
-    1. Integer
-    2. Float
-    3. Boolean (case-insensitive 'true' or 'false')
-    If all conversions fail, it returns the original string.
-
-    Args:
-        s: The input string to be converted.
-
-    Returns:
-        The converted value (int, float, bool) or the original string.
-    \"\"\"
-    if not isinstance(s, str):
-        # If the input is not a string, return it as is.
-        return s
-
-    # 1. Try to convert to an integer
-    try:
-        return int(s)
-    except ValueError:
-        # Not an integer, continue...
-        pass
-
-    # 2. Try to convert to a float
-    try:
-        return float(s)
-    except ValueError:
-        # Not a float, continue...
-        pass
-    
-    # 3. Check for a boolean value
-    # This explicit check is important because bool('False') evaluates to True.
-    s_lower = s.lower()
-    if s_lower == 'true':
-        return True
-    if s_lower == 'false':
-        return False
-        
-    # 4. If nothing else worked, return the original string
-    return s
-
 def handle_load_metadata(image_data: ImageMeta | None) -> List[Any]:
     \"\"\"
-    Processes image metadata and maps it to output components.
-
-    Args:
-        image_data: ImageMeta object containing image data and metadata, or None.
-
-    Returns:
-        A list of values for output components (Textbox, Slider, or PropertySheet instances).
+    Processes image metadata by calling the agnostic `transfer_metadata` helper.
     \"\"\"
-    if not image_data:
-        return [gr.Textbox(value="") for _ in output_fields]
+    if not image_data or not hasattr(image_data, 'path'):
+        return [gr.skip()] * len(output_fields)
 
     metadata = extract_metadata(image_data, only_custom_metadata=True)
-    dataclass_fields = build_dataclass_fields(PropertyConfig)
-    raw_values = transfer_metadata(output_fields, metadata, dataclass_fields)
-
-    output_values = [gr.skip()] * len(output_fields)
-    for i, (component, value) in enumerate(zip(output_fields, raw_values)):        
-        if hasattr(component, 'root_label'):
-            output_values[i] = create_dataclass_instance(PropertyConfig, value)
-        else:
-            output_values[i] = gr.update(value=infer_type(value))
+    if not metadata:
+        return [gr.skip()] * len(output_fields)
+        
+    # --- UI-Specific Configuration ---
+    # Define the map that tells the helper how to process the PropertySheet.
+    sheet_map = {
+        id(property_sheet): {
+            "type": PropertyConfig, 
+            "prefixes": [] # No prefixes needed for this simple case
+        }
+    }
     
-    return output_values
+    # Call the agnostic helper function to do the heavy lifting.
+    return transfer_metadata(
+        output_fields=output_fields,
+        metadata=metadata,
+        propertysheet_map=sheet_map
+    )
 
-def save_image_with_metadata(image_data: Any, *inputs: Any) -> str | None:
+def save_image_with_metadata(
+    image_data: Any, 
+    sheet_state: PropertyConfig,
+    model: str, 
+    f_number: str, 
+    iso: str, 
+    s_churn_val: float, 
+    description: str
+) -> str | None:
     \"\"\"
-    Saves an image with updated metadata to a file.
-
-    Args:
-        image_data: Input image data (e.g., file path or PIL Image).
-        *inputs: Variable number of input values from UI components (Textbox, Slider).
-
-    Returns:
-        The file path of the saved image, or None if no image is provided.
+    Saves an image with updated metadata, merging data from the PropertySheet
+    and individual UI components.
+    This example deals with the PropertySheet component and the individual gradio components. 
+    Since they have the same labels here, we'll simply replace the metadata with each other's values.
     \"\"\"
     if not image_data:
         return None
-    
-    params = list(inputs)
-    image_params = dict(zip(input_fields.keys(), params))    
-    metadata = {label: image_params.get(label, "") for label in image_params.keys()}
+   
+   
+    metadata = flatten_dataclass_with_labels(sheet_state)
+    individual_component_values = {
+        "Model": model,
+        "FNumber": f_number,
+        "ISOSpeedRatings": iso,
+        "Schurn": s_churn_val,
+        "Description": description
+    }
+   
+    metadata["Image Settings - Model"] = individual_component_values["Model"]
+    metadata["Image Settings - FNumber"] = individual_component_values["FNumber"]
+    metadata["Image Settings - ISOSpeedRatings"] = individual_component_values["ISOSpeedRatings"]
+    metadata["Image Settings - Schurn"] = individual_component_values["Schurn"]
+    metadata["Description"] = individual_component_values["Description"]
+   
+    final_metadata = {str(key): value for key, value in metadata.items()}
     
     new_filepath = output_dir / "image_with_meta.png"
-    
-    add_metadata(image_data, new_filepath, metadata)
+    add_metadata(image_data, new_filepath, final_metadata)
     
     return str(new_filepath)
 
@@ -266,7 +238,7 @@ with gr.Blocks() as demo:
     )
     save_button.click(
         save_image_with_metadata,
-        inputs=[img_custom, *input_fields.values()],
+        inputs=[img_custom, property_sheet, *input_fields.values()],
         outputs=[saved_file_output]
     )
     

src/pyproject.toml

@@ -8,7 +8,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "gradio_imagemeta"
-version = "0.0.4"
+version = "0.0.6"
 description = "Image Preview with Metadata for Gradio Interface"
 readme = "README.md"
 license = "apache-2.0"