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@@ -42,3 +42,9 @@ apps/gradio_app/assets/examples/license_plate_detector_ocr/2/lp_video.mp4 filter
 assets/examples/license_plate_detector_ocr/2/lp_video_output.mp4 filter=lfs diff=lfs merge=lfs -text
 assets/examples/license_plate_detector_ocr/2/lp_video.mp4 filter=lfs diff=lfs merge=lfs -text
 assets/gradio_app_demo.jpg filter=lfs diff=lfs merge=lfs -text
+apps/gradio_app/assets/examples/license_plate_detector_ocr/1/lp_image_output.jpg filter=lfs diff=lfs merge=lfs -text
+apps/gradio_app/assets/examples/license_plate_detector_ocr/2/lp_video_output.gif filter=lfs diff=lfs merge=lfs -text
+apps/gradio_app/assets/examples/license_plate_detector_ocr/2/lp_video.gif filter=lfs diff=lfs merge=lfs -text
+assets/examples/license_plate_detector_ocr/1/lp_image_output.jpg filter=lfs diff=lfs merge=lfs -text
+assets/examples/license_plate_detector_ocr/2/lp_video_output.gif filter=lfs diff=lfs merge=lfs -text
+assets/examples/license_plate_detector_ocr/2/lp_video.gif filter=lfs diff=lfs merge=lfs -text

apps/gradio_app.py

@@ -1,141 +1,141 @@
-import gradio as gr
-import os
-from gradio_app.config import setup_logging, setup_sys_path
-from gradio_app.processor import gradio_process, update_preview, update_visibility, clear_preview_data
-
-# Initialize logging and sys.path
-setup_logging()
-setup_sys_path()
-
-# Load custom CSS
-custom_css = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "styles.css"), "r").read()
-
-# Define example files
-examples = [
-    {
-        "input_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "1", "lp_image.jpg"),
-        "output_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "1", "lp_image_output.jpg"),
-        "input_type": "Image"
-    },
-    {
-        "input_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "2", "lp_video.mp4"),
-        "output_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "2", "lp_video_output.mp4"),
-        "input_type": "Video"
-    }
-]
-
-# Function to handle example selection
-def load_example(evt: gr.SelectData):
-    index = evt.index[0] if evt.index else 0
-    example = examples[index]
-    input_file = example["input_file"]
-    output_file = example["output_file"]
-    input_type = example["input_type"]
-    
-    # Update visibility based on input type
-    input_preview_image, input_preview_video, output_image, output_video = update_visibility(input_type)
-    
-    # Update preview based on input file and type
-    input_preview_image, input_preview_video = update_preview(input_file, input_type)
-    
-    return (
-        input_file,
-        input_type,
-        input_preview_image,
-        input_preview_video,
-        output_file if input_type == "Image" else None,
-        output_file if input_type == "Video" else None,
-        "Example loaded - click Submit to process"
-    )
-
-# Gradio Interface
-with gr.Blocks(css=custom_css) as iface:
-    gr.Markdown(
-        """
-        # License Plate Detection and OCR
-        Detect license plates from images or videos and read their text using 
-        advanced computer vision and OCR for accurate identification.
-        You can explore source code and contribute to the project on [![GitHub Repo](https://img.shields.io/badge/GitHub-danhtran2mind%2FLicense--Plate--Detector--OCR-blue?style=flat&logo=github)](https://github.com/danhtran2mind/License-Plate-Detector-OCR).
-
-        You can explore HuggingFace Model Hub on [![HuggingFace Model Hub](https://img.shields.io/badge/HuggingFace-danhtran2mind%2Flicense--plate--detector--ocr-yellow?style=flat&logo=huggingface)](https://huggingface.co/danhtran2mind/license-plate-detector-ocr)
-        """,
-        elem_classes="markdown-title"
-    )
-
-        
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_file = gr.File(label="Upload Image or Video", elem_classes="custom-file-input")
-            input_type = gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image", elem_classes="custom-radio")
-            with gr.Blocks():
-                input_preview_image = gr.Image(label="Input Preview", visible=True, elem_classes="custom-image")
-                input_preview_video = gr.Video(label="Input Preview", visible=False, elem_classes="custom-video")
-            with gr.Row():
-                clear_button = gr.Button("Clear", variant="secondary", elem_classes="custom-button secondary")
-                submit_button = gr.Button("Submit", variant="primary", elem_classes="custom-button primary")
-        with gr.Column(scale=1):
-            with gr.Blocks():
-                output_image = gr.Image(label="Processed Output (Image)", type="numpy", visible=True, elem_classes="custom-image")
-                output_video = gr.Video(label="Processed Output (Video)", visible=False, elem_classes="custom-video")
-            output_text = gr.Textbox(label="Detected License Plates", lines=10, elem_classes="custom-textbox")
-
-    # Update preview and output visibility when input type changes
-    input_type.change(
-        fn=update_visibility,
-        inputs=input_type,
-        outputs=[input_preview_image, input_preview_video, output_image, output_video]
-    )
-
-    # Update preview when file is uploaded
-    input_file.change(
-        fn=update_preview,
-        inputs=[input_file, input_type],
-        outputs=[input_preview_image, input_preview_video]
-    )
-    
-    # Bind the processing function
-    submit_button.click(
-        fn=gradio_process,
-        inputs=[input_file, input_type],
-        outputs=[output_image, output_video, output_text, input_preview_image, input_preview_video]
-    )
-    
-    # Clear button functionality
-    clear_button.click(
-        fn=lambda: (None, None, None, "Image", None, None, None, None),
-        outputs=[input_file, output_image, output_video, input_type, input_preview_image, input_preview_video, output_image, output_video]
-    ).then(
-        fn=clear_preview_data,
-        inputs=None,
-        outputs=None
-    )
-
-    # Examples table
-    with gr.Row():
-        gr.Markdown("### Examples")
-
-    with gr.Row():
-        example_table = gr.Dataframe(
-            value=[[i, ex["input_type"], os.path.basename(ex["input_file"])] for i, ex in enumerate(examples)],
-            headers=["Index", "Type", "File"],
-            datatype=["number", "str", "str"],
-            interactive=True,
-            elem_classes="custom-table"
-        )
-    with gr.Row():
-        gr.Markdown("""
-                    This project utilizes:
-
-                    - **Detection task**: YOLOv12 architecture model (YOLO12n) from [![GitHub Repo](https://img.shields.io/badge/GitHub-sunsmarterjie%2Fyolov12-blue?style=flat&logo=github)](https://github.com/sunsmarterjie/yolov12) and documentation at [![Ultralytics YOLO12](https://img.shields.io/badge/Ultralytics-YOLO12-purple?style=flat)](https://docs.ultralytics.com/models/yolo12/), powered by the Ultralytics platform: [![Ultralytics  Inc.](https://img.shields.io/badge/Ultralytics-Inc.-purple?style=flat)](https://docs.ultralytics.com).
-
-                    - **OCR task**: PaddleOCR v2.9 from [![GitHub Repo](https://img.shields.io/badge/GitHub-PaddlePaddle%2FPaddleOCR%2Frelease%2F2.9-blue?style=flat&logo=github)](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.9), with the main repository at [![GitHub Repo](https://img.shields.io/badge/GitHub-PaddlePaddle%2FPaddleOCR-blue?style=flat&logo=github)](https://github.com/PaddlePaddle/PaddleOCR) for OCR inference. Explore more about PaddleOCR at [![PaddleOCR Website](https://img.shields.io/badge/PaddleOCR-Website-purple?style=flat)](https://www.paddleocr.ai/main/en/index.html).
-                    """)
-    # Example table click handler
-    example_table.select(
-        fn=load_example,
-        inputs=None,
-        outputs=[input_file, input_type, input_preview_image, input_preview_video, output_image, output_video, output_text]
-    )
-
-if __name__ == "__main__":
+import gradio as gr
+import os
+from gradio_app.config import setup_logging, setup_sys_path
+from gradio_app.processor import gradio_process, update_preview, update_visibility, clear_preview_data
+
+# Initialize logging and sys.path
+setup_logging()
+setup_sys_path()
+
+# Load custom CSS
+custom_css = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "styles.css"), "r").read()
+
+# Define example files
+examples = [
+    {
+        "input_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "1", "lp_image.jpg"),
+        "output_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "1", "lp_image_output.jpg"),
+        "input_type": "Image"
+    },
+    {
+        "input_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "2", "lp_video.mp4"),
+        "output_file": os.path.join(os.path.dirname(__file__), "gradio_app", "assets", "examples", "license_plate_detector_ocr", "2", "lp_video_output.mp4"),
+        "input_type": "Video"
+    }
+]
+
+# Function to handle example selection
+def load_example(evt: gr.SelectData):
+    index = evt.index[0] if evt.index else 0
+    example = examples[index]
+    input_file = example["input_file"]
+    output_file = example["output_file"]
+    input_type = example["input_type"]
+    
+    # Update visibility based on input type
+    input_preview_image, input_preview_video, output_image, output_video = update_visibility(input_type)
+    
+    # Update preview based on input file and type
+    input_preview_image, input_preview_video = update_preview(input_file, input_type)
+    
+    return (
+        input_file,
+        input_type,
+        input_preview_image,
+        input_preview_video,
+        output_file if input_type == "Image" else None,
+        output_file if input_type == "Video" else None,
+        "Example loaded - click Submit to process"
+    )
+
+# Gradio Interface
+with gr.Blocks(css=custom_css) as iface:
+    gr.Markdown(
+        """
+        # License Plate Detection and OCR
+        Detect license plates from images or videos and read their text using 
+        advanced computer vision and OCR for accurate identification.
+        You can explore source code and contribute to the project on [![GitHub Repo](https://img.shields.io/badge/GitHub-danhtran2mind%2FLicense--Plate--Detector--OCR-blue?style=flat&logo=github)](https://github.com/danhtran2mind/License-Plate-Detector-OCR).
+
+        You can explore HuggingFace Model Hub on [![HuggingFace Model Hub](https://img.shields.io/badge/HuggingFace-danhtran2mind%2Flicense--plate--detector--ocr-yellow?style=flat&logo=huggingface)](https://huggingface.co/danhtran2mind/license-plate-detector-ocr)
+        """,
+        elem_classes="markdown-title"
+    )
+
+        
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_file = gr.File(label="Upload Image or Video", elem_classes="custom-file-input")
+            input_type = gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image", elem_classes="custom-radio")
+            with gr.Blocks():
+                input_preview_image = gr.Image(label="Input Preview", visible=True, elem_classes="custom-image")
+                input_preview_video = gr.Video(label="Input Preview", visible=False, elem_classes="custom-video")
+            with gr.Row():
+                clear_button = gr.Button("Clear", variant="secondary", elem_classes="custom-button secondary")
+                submit_button = gr.Button("Submit", variant="primary", elem_classes="custom-button primary")
+        with gr.Column(scale=1):
+            with gr.Blocks():
+                output_image = gr.Image(label="Processed Output (Image)", type="numpy", visible=True, elem_classes="custom-image")
+                output_video = gr.Video(label="Processed Output (Video)", visible=False, elem_classes="custom-video")
+            output_text = gr.Textbox(label="Detected License Plates", lines=10, elem_classes="custom-textbox")
+
+    # Update preview and output visibility when input type changes
+    input_type.change(
+        fn=update_visibility,
+        inputs=input_type,
+        outputs=[input_preview_image, input_preview_video, output_image, output_video]
+    )
+
+    # Update preview when file is uploaded
+    input_file.change(
+        fn=update_preview,
+        inputs=[input_file, input_type],
+        outputs=[input_preview_image, input_preview_video]
+    )
+    
+    # Bind the processing function
+    submit_button.click(
+        fn=gradio_process,
+        inputs=[input_file, input_type],
+        outputs=[output_image, output_video, output_text, input_preview_image, input_preview_video]
+    )
+    
+    # Clear button functionality
+    clear_button.click(
+        fn=lambda: (None, None, None, "Image", None, None, None, None),
+        outputs=[input_file, output_image, output_video, input_type, input_preview_image, input_preview_video, output_image, output_video]
+    ).then(
+        fn=clear_preview_data,
+        inputs=None,
+        outputs=None
+    )
+
+    # Examples table
+    with gr.Row():
+        gr.Markdown("### Examples")
+
+    with gr.Row():
+        example_table = gr.Dataframe(
+            value=[[i, ex["input_type"], os.path.basename(ex["input_file"])] for i, ex in enumerate(examples)],
+            headers=["Index", "Type", "File"],
+            datatype=["number", "str", "str"],
+            interactive=True,
+            elem_classes="custom-table"
+        )
+    with gr.Row():
+        gr.Markdown("""
+                    This project utilizes:
+
+                    - **Detection task**: YOLOv12 architecture model (YOLO12n) from [![GitHub Repo](https://img.shields.io/badge/GitHub-sunsmarterjie%2Fyolov12-blue?style=flat&logo=github)](https://github.com/sunsmarterjie/yolov12) and documentation at [![Ultralytics YOLO12](https://img.shields.io/badge/Ultralytics-YOLO12-purple?style=flat)](https://docs.ultralytics.com/models/yolo12/), powered by the Ultralytics platform: [![Ultralytics  Inc.](https://img.shields.io/badge/Ultralytics-Inc.-purple?style=flat)](https://docs.ultralytics.com).
+
+                    - **OCR task**: PaddleOCR v2.9 from [![GitHub Repo](https://img.shields.io/badge/GitHub-PaddlePaddle%2FPaddleOCR%2Frelease%2F2.9-blue?style=flat&logo=github)](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.9), with the main repository at [![GitHub Repo](https://img.shields.io/badge/GitHub-PaddlePaddle%2FPaddleOCR-blue?style=flat&logo=github)](https://github.com/PaddlePaddle/PaddleOCR) for OCR inference. Explore more about PaddleOCR at [![PaddleOCR Website](https://img.shields.io/badge/PaddleOCR-Website-purple?style=flat)](https://www.paddleocr.ai/main/en/index.html).
+                    """)
+    # Example table click handler
+    example_table.select(
+        fn=load_example,
+        inputs=None,
+        outputs=[input_file, input_type, input_preview_image, input_preview_video, output_image, output_video, output_text]
+    )
+
+if __name__ == "__main__":
     iface.launch()

apps/gradio_app/assets/examples/license_plate_detector_ocr/2/lp_video_output.mp4

@@ -1,3 +1,3 @@
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-oid sha256:72dececeb4cc1ce1da5264211578c9331a3fb31d36bf21ac2f40471d70e2121d
-size 4984385
+oid sha256:386eaac8240eaafb06fde82b4a049bfd687ae84b5f2fea73d570070ef67088e5
+size 3284659

apps/gradio_app/old-processor.py

@@ -0,0 +1,109 @@
+import os
+import sys
+import shutil
+import traceback
+import logging
+import gradio as gr
+from gradio_app.utils import convert_to_supported_format
+# Adjust sys.path to include the src directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                              '..', '..', 'src', 'license_plate_detector_ocr')))
+from infer import infer, is_image_file
+
+
+
+def gradio_process(input_file, input_type):
+    """Process the input file (image or video) for license plate detection and OCR."""
+    try:
+        logging.debug(f"Input file path: {input_file.name}")
+        print(f"Input file path: {input_file.name}")
+        
+        # Copy input file to temp_data directory to ensure stability
+        temp_input_dir = "apps/gradio_app/temp_data"
+        os.makedirs(temp_input_dir, exist_ok=True)
+        temp_input_path = os.path.join(temp_input_dir, os.path.basename(input_file.name))
+        shutil.copy(input_file.name, temp_input_path)
+        logging.debug(f"Copied input file to: {temp_input_path}")
+        
+        # Verify input file exists
+        if not os.path.exists(temp_input_path):
+            error_msg = f"Error: Input file {temp_input_path} does not exist."
+            logging.error(error_msg)
+            return None, None, error_msg, None, None
+        
+        # Set output path
+        output_dir = "apps/gradio_app/temp_data"
+        os.makedirs(output_dir, exist_ok=True)
+        output_filename = os.path.splitext(os.path.basename(temp_input_path))[0] + ('_output.jpg' if is_image_file(temp_input_path) else '_output.mp4')
+        output_path = os.path.join(output_dir, output_filename)
+        logging.debug(f"Output path: {output_path}")
+        
+        # Call the infer function
+        result_array, plate_texts = infer(temp_input_path, output_path)
+        
+        if result_array is None and is_image_file(temp_input_path):
+            error_msg = f"Error: Processing failed for {temp_input_path}. 'infer' returned None."
+            logging.error(error_msg)
+            return None, None, error_msg, None, None
+        
+        # Validate output file for videos
+        if not is_image_file(temp_input_path):
+            if not os.path.exists(output_path):
+                error_msg = f"Error: Output video file {output_path} was not created."
+                logging.error(error_msg)
+                return None, None, error_msg, None, None
+            # Convert output video to supported format
+            converted_output_path = os.path.join(output_dir, f"converted_{os.path.basename(output_path)}")
+            converted_path = convert_to_supported_format(output_path, converted_output_path)
+            if converted_path is None:
+                error_msg = f"Error: Failed to convert output video {output_path} to supported format."
+                logging.error(error_msg)
+                return None, None, error_msg, None, None
+            output_path = converted_path
+        
+        # Format plate texts
+        if is_image_file(temp_input_path):
+            formatted_texts = "\n".join(plate_texts) if plate_texts else "No plates detected"
+            logging.debug(f"Image processed successfully. Plate texts: {formatted_texts}")
+            return result_array, None, formatted_texts, temp_input_path, None
+        else:
+            formatted_texts = []
+            for i, texts in enumerate(plate_texts):
+                if texts:
+                    formatted_texts.append(f"Frame {i+1}: {', '.join(texts)}")
+            formatted_texts = "\n".join(formatted_texts) if formatted_texts else "No plates detected"
+            logging.debug(f"Video processed successfully. Plate texts: {formatted_texts}")
+            return None, output_path, formatted_texts, None, temp_input_path
+    except Exception as e:
+        error_message = f"Error processing {input_file.name}: {str(e)}\n{traceback.format_exc()}"
+        logging.error(error_message)
+        print(error_message)
+        return None, None, error_message, None, None
+
+def update_preview(file, input_type):
+    """Return file path for the appropriate preview component based on input type."""
+    if not file:
+        logging.debug("No file provided for preview.")
+        return None, None
+    logging.debug(f"Updating preview for {input_type}: {file.name}")
+    # Verify file exists
+    if not os.path.exists(file.name):
+        logging.error(f"Input file {file.name} does not exist.")
+        return None, None
+    # Check if video format is supported
+    if input_type == "Video" and not file.name.lower().endswith(('.mp4', '.webm')):
+        logging.error(f"Unsupported video format for {file.name}. Use MP4 or WebM.")
+        return None, None
+    return file.name if input_type == "Image" else None, file.name if input_type == "Video" else None
+
+def update_visibility(input_type):
+    """Update visibility of input/output components based on input type."""
+    logging.debug(f"Updating visibility for input type: {input_type}")
+    is_image = input_type == "Image"
+    is_video = input_type == "Video"
+    return (
+        gr.update(visible=is_image),
+        gr.update(visible=is_video),
+        gr.update(visible=is_image),
+        gr.update(visible=is_video)
+    )

apps/old-gradio_app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import os
+import sys
+
+# Adjust sys.path to include the src directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src', 'license_plate_detector_ocr')))
+from infer import infer, is_image_file
+
+def gradio_process(input_file, input_type):
+    """Process the input file (image or video) for license plate detection and OCR."""
+    # Debugging: Print input file path
+    print(f"Input file path: {input_file.name}")
+    
+    # Set default output path in apps/gradio_app/temp_data/
+    output_dir = "apps/gradio_app/temp_data"
+    os.makedirs(output_dir, exist_ok=True)
+    output_filename = os.path.splitext(os.path.basename(input_file.name))[0] + ('_output.jpg' if is_image_file(input_file.name) else '_output.mp4')
+    output_path = os.path.join(output_dir, output_filename)
+    
+    # Call the infer function from infer.py
+    result_array, plate_texts = infer(input_file.name, output_path)
+    
+    if result_array is None:
+        return None, f"Error: Processing failed for {input_file.name}"
+    
+    # Format plate texts for output
+    if is_image_file(input_file.name):
+        formatted_texts = "\n".join(plate_texts) if plate_texts else "No plates detected"
+        return result_array, formatted_texts
+    else:
+        # For videos, plate_texts is a list of lists (per frame)
+        formatted_texts = []
+        for i, texts in enumerate(plate_texts):
+            if texts:
+                formatted_texts.append(f"Frame {i+1}: {', '.join(texts)}")
+        formatted_texts = "\n".join(formatted_texts) if formatted_texts else "No plates detected"
+        return output_path, formatted_texts
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=gradio_process,
+    inputs=[
+        gr.File(label="Upload Image or Video"),
+        gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image")
+    ],
+    outputs=[
+        gr.Image(label="Processed Output", type="numpy"),
+        gr.Textbox(label="Detected License Plates")
+    ],
+    title="License Plate Detection and OCR",
+    description="Upload an image or video to detect and read license plates. Outputs are saved in apps/gradio_app/temp_data/."
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)

apps/old2-gradio_app.py

@@ -0,0 +1,183 @@
+import gradio as gr
+import os
+import sys
+import traceback
+import logging
+import shutil
+import ffmpeg
+
+# Set up logging to a file for debugging
+logging.basicConfig(
+    filename="apps/gradio_app/debug.log",
+    level=logging.DEBUG,
+    format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+# Adjust sys.path to include the src directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src', 'license_plate_detector_ocr')))
+from infer import infer, is_image_file
+
+def convert_to_supported_format(input_path, output_path):
+    """Convert video to a browser-compatible format (MP4 with H.264 codec)."""
+    try:
+        stream = ffmpeg.input(input_path)
+        stream = ffmpeg.output(stream, output_path, vcodec='h264', acodec='aac', format='mp4', loglevel='quiet')
+        ffmpeg.run(stream)
+        logging.debug(f"Converted video to {output_path}")
+        return output_path
+    except Exception as e:
+        logging.error(f"Error converting video {input_path}: {str(e)}")
+        return None
+
+def gradio_process(input_file, input_type):
+    """Process the input file (image or video) for license plate detection and OCR."""
+    try:
+        logging.debug(f"Input file path: {input_file.name}")
+        print(f"Input file path: {input_file.name}")
+        
+        # Copy input file to temp_data directory to ensure stability
+        temp_input_dir = "apps/gradio_app/temp_data"
+        os.makedirs(temp_input_dir, exist_ok=True)
+        temp_input_path = os.path.join(temp_input_dir, os.path.basename(input_file.name))
+        shutil.copy(input_file.name, temp_input_path)
+        logging.debug(f"Copied input file to: {temp_input_path}")
+        
+        # Verify input file exists
+        if not os.path.exists(temp_input_path):
+            error_msg = f"Error: Input file {temp_input_path} does not exist."
+            logging.error(error_msg)
+            return None, None, error_msg, None, None
+        
+        # Set output path
+        output_dir = "apps/gradio_app/temp_data"
+        os.makedirs(output_dir, exist_ok=True)
+        output_filename = os.path.splitext(os.path.basename(temp_input_path))[0] + ('_output.jpg' if is_image_file(temp_input_path) else '_output.mp4')
+        output_path = os.path.join(output_dir, output_filename)
+        logging.debug(f"Output path: {output_path}")
+        
+        # Call the infer function
+        result_array, plate_texts = infer(temp_input_path, output_path)
+        
+        if result_array is None and is_image_file(temp_input_path):
+            error_msg = f"Error: Processing failed for {temp_input_path}. 'infer' returned None."
+            logging.error(error_msg)
+            return None, None, error_msg, None, None
+        
+        # Validate output file for videos
+        if not is_image_file(temp_input_path):
+            if not os.path.exists(output_path):
+                error_msg = f"Error: Output video file {output_path} was not created."
+                logging.error(error_msg)
+                return None, None, error_msg, None, None
+            # Convert output video to supported format
+            converted_output_path = os.path.join(output_dir, f"converted_{os.path.basename(output_path)}")
+            converted_path = convert_to_supported_format(output_path, converted_output_path)
+            if converted_path is None:
+                error_msg = f"Error: Failed to convert output video {output_path} to supported format."
+                logging.error(error_msg)
+                return None, None, error_msg, None, None
+            output_path = converted_path
+        
+        # Format plate texts
+        if is_image_file(temp_input_path):
+            formatted_texts = "\n".join(plate_texts) if plate_texts else "No plates detected"
+            logging.debug(f"Image processed successfully. Plate texts: {formatted_texts}")
+            return result_array, None, formatted_texts, temp_input_path, None
+        else:
+            formatted_texts = []
+            for i, texts in enumerate(plate_texts):
+                if texts:
+                    formatted_texts.append(f"Frame {i+1}: {', '.join(texts)}")
+            formatted_texts = "\n".join(formatted_texts) if formatted_texts else "No plates detected"
+            logging.debug(f"Video processed successfully. Plate texts: {formatted_texts}")
+            return None, output_path, formatted_texts, None, temp_input_path
+    except Exception as e:
+        error_message = f"Error processing {input_file.name}: {str(e)}\n{traceback.format_exc()}"
+        logging.error(error_message)
+        print(error_message)
+        return None, None, error_message, None, None
+
+def update_preview(file, input_type):
+    """Return file path for the appropriate preview component based on input type."""
+    if not file:
+        logging.debug("No file provided for preview.")
+        return None, None
+    logging.debug(f"Updating preview for {input_type}: {file.name}")
+    # Verify file exists
+    if not os.path.exists(file.name):
+        logging.error(f"Input file {file.name} does not exist.")
+        return None, None
+    # Check if video format is supported
+    if input_type == "Video" and not file.name.lower().endswith(('.mp4', '.webm')):
+        logging.error(f"Unsupported video format for {file.name}. Use MP4 or WebM.")
+        return None, None
+    return file.name if input_type == "Image" else None, file.name if input_type == "Video" else None
+
+def update_visibility(input_type):
+    """Update visibility of input/output components based on input type."""
+    logging.debug(f"Updating visibility for input type: {input_type}")
+    is_image = input_type == "Image"
+    is_video = input_type == "Video"
+    return (
+        gr.update(visible=is_image),
+        gr.update(visible=is_video),
+        gr.update(visible=is_image),
+        gr.update(visible=is_video)
+    )
+
+# Gradio Interface
+with gr.Blocks() as iface:
+    gr.Markdown(
+        """
+        # License Plate Detection and OCR
+        Upload an image or video to detect and read license plates. Outputs are saved in `apps/gradio_app/temp_data/`.
+        Debug logs are saved in `apps/gradio_app/debug.log`.
+        """,
+        elem_classes="markdown-title"
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_file = gr.File(label="Upload Image or Video")
+            input_type = gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image")
+            with gr.Blocks():
+                input_preview_image = gr.Image(label="Input Preview", visible=True)
+                input_preview_video = gr.Video(label="Input Preview", visible=False)
+            with gr.Row():
+                clear_button = gr.Button("Clear", variant="secondary")
+                submit_button = gr.Button("Submit", variant="primary")
+        with gr.Column(scale=2):
+            with gr.Blocks():
+                output_image = gr.Image(label="Processed Output (Image)", type="numpy", visible=True)
+                output_video = gr.Video(label="Processed Output (Video)", visible=False)
+            output_text = gr.Textbox(label="Detected License Plates", lines=10)
+
+    # Update preview and output visibility when input type changes
+    input_type.change(
+        fn=update_visibility,
+        inputs=input_type,
+        outputs=[input_preview_image, input_preview_video, output_image, output_video]
+    )
+
+    # Update preview when file is uploaded
+    input_file.change(
+        fn=update_preview,
+        inputs=[input_file, input_type],
+        outputs=[input_preview_image, input_preview_video]
+    )
+    
+    # Bind the processing function
+    submit_button.click(
+        fn=gradio_process,
+        inputs=[input_file, input_type],
+        outputs=[output_image, output_video, output_text, input_preview_image, input_preview_video]
+    )
+    
+    # Clear button functionality
+    clear_button.click(
+        fn=lambda: (None, None, None, "Image", None, None, None, None),
+        outputs=[input_file, output_image, output_video, input_type, input_preview_image, input_preview_video, output_image, output_video]
+    )
+
+if __name__ == "__main__":
+    iface.launch(share=True)

apps/old3-gradio_app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import os
+from gradio_app.config import setup_logging, setup_sys_path
+from gradio_app.processor import gradio_process, update_preview, update_visibility
+
+# Initialize logging and sys.path
+setup_logging()
+setup_sys_path()
+
+# Load custom CSS
+custom_css = open(os.path.join(os.path.dirname(__file__), "gradio_app", "static", "styles.css"), "r").read()
+
+# Gradio Interface
+with gr.Blocks(css=custom_css) as iface:
+    gr.Markdown(
+        """
+        # License Plate Detection and OCR
+        Detect license plates from images or videos and read their text using 
+        advanced computer vision and OCR for accurate identification.
+        """,
+        elem_classes="markdown-title"
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_file = gr.File(label="Upload Image or Video", elem_classes="custom-file-input")
+            input_type = gr.Radio(choices=["Image", "Video"], label="Input Type", value="Image", elem_classes="custom-radio")
+            with gr.Blocks():
+                input_preview_image = gr.Image(label="Input Preview", visible=True, elem_classes="custom-image")
+                input_preview_video = gr.Video(label="Input Preview", visible=False, elem_classes="custom-video")
+            with gr.Row():
+                clear_button = gr.Button("Clear", variant="secondary", elem_classes="custom-button secondary")
+                submit_button = gr.Button("Submit", variant="primary", elem_classes="custom-button primary")
+        with gr.Column(scale=2):
+            with gr.Blocks():
+                output_image = gr.Image(label="Processed Output (Image)", type="numpy", visible=True, elem_classes="custom-image")
+                output_video = gr.Video(label="Processed Output (Video)", visible=False, elem_classes="custom-video")
+            output_text = gr.Textbox(label="Detected License Plates", lines=10, elem_classes="custom-textbox")
+
+    # Update preview and output visibility when input type changes
+    input_type.change(
+        fn=update_visibility,
+        inputs=input_type,
+        outputs=[input_preview_image, input_preview_video, output_image, output_video]
+    )
+
+    # Update preview when file is uploaded
+    input_file.change(
+        fn=update_preview,
+        inputs=[input_file, input_type],
+        outputs=[input_preview_image, input_preview_video]
+    )
+    
+    # Bind the processing function
+    submit_button.click(
+        fn=gradio_process,
+        inputs=[input_file, input_type],
+        outputs=[output_image, output_video, output_text, input_preview_image, input_preview_video]
+    )
+    
+    # Clear button functionality
+    clear_button.click(
+        fn=lambda: (None, None, None, "Image", None, None, None, None),
+        outputs=[input_file, output_image, output_video, input_type, input_preview_image, input_preview_video, output_image, output_video]
+    )
+
+if __name__ == "__main__":
+    iface.launch(share=True)

assets/examples/license_plate_detector_ocr/2/lp_video_output.mp4

@@ -1,3 +1,3 @@
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-size 4984385
+oid sha256:386eaac8240eaafb06fde82b4a049bfd687ae84b5f2fea73d570070ef67088e5
+size 3284659