Update app.py

app.py

@@ -1,449 +1,153 @@
-import os
-import json
-import tempfile
-import zipfile
-from datetime import datetime
-
 import gradio as gr
-import numpy as np
-import torch
-from PIL import Image
-
-# Program A imports
 from utils import MEGABenchEvalDataLoader
-from constants import *  # This is assumed to define CITATION_BUTTON_TEXT, CITATION_BUTTON_LABEL, TABLE_INTRODUCTION, LEADERBOARD_INTRODUCTION, DATA_INFO, SUBMIT_INTRODUCTION, BASE_MODEL_GROUPS, etc.
-
-# Program B imports
-import spaces
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, Qwen2_5_VLForConditionalGeneration
-from qwen_vl_utils import process_vision_info
-from gliner import GLiNER
+import os
+from constants import *
 
-# ----------------------------------------------------------------
-# Combined CSS
-# ----------------------------------------------------------------
+# Get the directory of the current script
 current_dir = os.path.dirname(os.path.abspath(__file__))
-with open(os.path.join(current_dir, "static", "css", "style.css"), "r") as f:
+
+# Construct paths to CSS files
+base_css_file = os.path.join(current_dir, "static", "css", "style.css")
+table_css_file = os.path.join(current_dir, "static", "css", "table.css")
+
+# Read CSS files
+with open(base_css_file, "r") as f:
     base_css = f.read()
-with open(os.path.join(current_dir, "static", "css", "table.css"), "r") as f:
+with open(table_css_file, "r") as f:
     table_css = f.read()
 
-css_program_b = """
-  /* Program B CSS */
-  .gradio-container {
-    max-width: 1200px !important;
-    margin: 0 auto;
-    padding: 20px;
-    background-color: #f8f9fa;
-  }
-  .tabs {
-    border-radius: 8px;
-    background: white;
-    padding: 20px;
-    box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1);
-  }
-  .input-container, .output-container {
-    background: white;
-    border-radius: 8px;
-    padding: 15px;
-    margin: 10px 0;
-    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-  }
-  .submit-btn {
-    background-color: #2d31fa !important;
-    border: none !important;
-    padding: 8px 20px !important;
-    border-radius: 6px !important;
-    color: white !important;
-    transition: all 0.3s ease !important;
-  }
-  .submit-btn:hover {
-    background-color: #1f24c7 !important;
-    transform: translateY(-1px);
-  }
-  #output {
-    height: 500px;
-    overflow: auto;
-    border: 1px solid #e0e0e0;
-    border-radius: 6px;
-    padding: 15px;
-    background: #ffffff;
-    font-family: 'Arial', sans-serif;
-  }
-  .gr-dropdown {
-    border-radius: 6px !important;
-    border: 1px solid #e0e0e0 !important;
-  }
-  .gr-image-input {
-    border: 2px dashed #ccc;
-    border-radius: 8px;
-    padding: 20px;
-    transition: all 0.3s ease;
-  }
-  .gr-image-input:hover {
-    border-color: #2d31fa;
-  }
-"""
-css_global = base_css + "\n" + table_css + "\n" + css_program_b
-
-# ----------------------------------------------------------------
-# Program A Global Initializations
-# ----------------------------------------------------------------
+# Initialize data loaders
 default_loader = MEGABenchEvalDataLoader("./static/eval_results/Default")
 si_loader = MEGABenchEvalDataLoader("./static/eval_results/SI")
 
-# ----------------------------------------------------------------
-# Program B Global Initializations
-# ----------------------------------------------------------------
-gliner_model = GLiNER.from_pretrained("knowledgator/modern-gliner-bi-large-v1.0")
-DEFAULT_NER_LABELS = "person, organization, location, date, event"
-
-models = {
-    "Qwen/Qwen2.5-VL-7B-Instruct": Qwen2_5_VLForConditionalGeneration.from_pretrained(
-        "Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True, torch_dtype="auto"
-    ).cuda().eval()
-}
-processors = {
-    "Qwen/Qwen2.5-VL-7B-Instruct": AutoProcessor.from_pretrained(
-        "Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True
+with gr.Blocks() as block:
+    # Add a style element that we'll update
+    css_style = gr.HTML(
+        f"<style>{base_css}\n{table_css}</style>",
+        visible=False
     )
-}
+    
+    gr.Markdown(
+        LEADERBOARD_INTRODUCTION
+    )
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("📊 MEGA-Bench", elem_id="qa-tab-table1", id=1):
+            with gr.Row():
+                with gr.Accordion("Citation", open=False):
+                    citation_button = gr.Textbox(
+                        value=CITATION_BUTTON_TEXT,
+                        label=CITATION_BUTTON_LABEL,
+                        elem_id="citation-button",
+                        lines=10,
+                    )
+            gr.Markdown(
+                TABLE_INTRODUCTION
+            )
 
-user_prompt = '<|user|>\n'
-assistant_prompt = '<|assistant|>\n'
-prompt_suffix = "<|end|>\n"
+            with gr.Row():
+                table_selector = gr.Radio(
+                    choices=["Default", "Single Image"],
+                    label="Select table to display. Default: all MEGA-Bench tasks; Single Image: single-image tasks only.",
+                    value="Default"
+                )
 
-# A simple metadata container for OCR results and entity information.
-class TextWithMetadata(list):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args)
-        self.original_text = kwargs.get('original_text', '')
-        self.entities = kwargs.get('entities', [])
+            # Define different captions for each table
+            default_caption = "**Table 1: MEGA-Bench full results.** The number in the parentheses is the number of tasks of each keyword. <br> The Core set contains $N_{\\text{core}} = 440$ tasks evaluated by rule-based metrics, and the Open-ended set contains $N_{\\text{open}} = 65$ tasks evaluated by a VLM judge (we use GPT-4o-0806). <br> Different from the results in our paper, we only use the Core results with CoT prompting here for clarity and compatibility with the released data. <br> $\\text{Overall} \\ = \\ \\frac{\\text{Core} \\ \\cdot \\ N_{\\text{core}} \\ + \\ \\text{Open-ended} \\ \\cdot \\ N_{\\text{open}}}{N_{\\text{core}} \\ + \\ N_{\\text{open}}}$ <br> * indicates self-reported results from the model authors."
 
-# ----------------------------------------------------------------
-# UI DEFINITION (placed at the top)
-# ----------------------------------------------------------------
-with gr.Blocks(css=css_global) as demo:
-    with gr.Tabs():
-        # -------------------------
-        # Tab 1: Dashboard (Program A)
-        # -------------------------
-        with gr.TabItem("Dashboard"):
-            with gr.Tabs(elem_classes="tab-buttons") as dashboard_tabs:
-                # --- MEGA-Bench Leaderboard Tab ---
-                with gr.TabItem("📊 MEGA-Bench"):
-                    # Inject table CSS (will be updated when the table is refreshed)
-                    css_style = gr.HTML(f"<style>{base_css}\n{table_css}</style>", visible=False)
-                    
-                    # Define captions for default vs. single-image tables
-                    default_caption = ("**Table 1: MEGA-Bench full results.** The number in the parentheses is the number of tasks "
-                                       "of each keyword. <br> The Core set contains $N_{\\text{core}} = 440$ tasks evaluated by "
-                                       "rule-based metrics, and the Open-ended set contains $N_{\\text{open}} = 65$ tasks evaluated by a "
-                                       "VLM judge (we use GPT-4o-0806). <br> Different from the results in our paper, we only use the Core "
-                                       "results with CoT prompting here for clarity and compatibility with the released data. <br> "
-                                       "$\\text{Overall} \\ = \\ \\frac{\\text{Core} \\ \\cdot \\ N_{\\text{core}} \\ + \\ \\text{Open-ended} "
-                                       "\\ \\cdot \\ N_{\\text{open}}}{N_{\\text{core}} \\ + \\ N_{\\text{open}}}$ <br> * indicates self-reported "
-                                       "results from the model authors.")
-                    single_image_caption = ("**Table 2: MEGA-Bench Single-image setting results.** The number in the parentheses is the number of tasks "
-                                            "in each keyword. <br> This subset contains 273 single-image tasks from the Core set and 42 single-image tasks "
-                                            "from the Open-ended set. For open-source models, we drop the image input in the 1-shot demonstration example so that "
-                                            "the entire query contains a single image only. <br> Compared to the default table, some models with only "
-                                            "single-image support are added.")
-                    
-                    caption_component = gr.Markdown(
-                        value=default_caption,
-                        elem_classes="table-caption",
-                        latex_delimiters=[{"left": "$", "right": "$", "display": False}],
-                    )
-                    
-                    with gr.Row():
-                        super_group_selector = gr.Radio(
-                            choices=list(default_loader.SUPER_GROUPS.keys()),
-                            label="Select a dimension to display breakdown results. We use different column colors to distinguish the overall benchmark scores and breakdown results.",
-                            value=list(default_loader.SUPER_GROUPS.keys())[0]
-                        )
-                        model_group_selector = gr.Radio(
-                            choices=list(BASE_MODEL_GROUPS.keys()),
-                            label="Select a model group",
-                            value="All"
-                        )
-                    
-                    initial_headers, initial_data = default_loader.get_leaderboard_data(
-                        list(default_loader.SUPER_GROUPS.keys())[0], "All"
-                    )
-                    data_component = gr.Dataframe(
-                        value=initial_data,
-                        headers=initial_headers,
-                        datatype=["number", "html"] + ["number"] * (len(initial_headers) - 2),
-                        interactive=True,
-                        elem_classes="custom-dataframe",
-                        max_height=2400,
-                        column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(initial_headers) - 5),
-                    )
-                    
-                    with gr.Row():
-                        with gr.Accordion("Citation", open=False):
-                            citation_button = gr.Textbox(
-                                value=CITATION_BUTTON_TEXT,
-                                label=CITATION_BUTTON_LABEL,
-                                elem_id="citation-button",
-                                lines=10,
-                            )
-                    gr.Markdown(TABLE_INTRODUCTION)
-                    
-                    with gr.Row():
-                        table_selector = gr.Radio(
-                            choices=["Default", "Single Image"],
-                            label="Select table to display. Default: all MEGA-Bench tasks; Single Image: single-image tasks only.",
-                            value="Default"
-                        )
-                    
-                    refresh_button = gr.Button("Refresh")
-                    
-                    # Wire up event handlers (functions defined below)
-                    refresh_button.click(
-                        fn=update_table_and_caption, 
-                        inputs=[table_selector, super_group_selector, model_group_selector], 
-                        outputs=[data_component, caption_component, css_style]
-                    )
-                    super_group_selector.change(
-                        fn=update_table_and_caption, 
-                        inputs=[table_selector, super_group_selector, model_group_selector], 
-                        outputs=[data_component, caption_component, css_style]
-                    )
-                    model_group_selector.change(
-                        fn=update_table_and_caption, 
-                        inputs=[table_selector, super_group_selector, model_group_selector], 
-                        outputs=[data_component, caption_component, css_style]
-                    )
-                    table_selector.change(
-                        fn=update_selectors,
-                        inputs=[table_selector],
-                        outputs=[super_group_selector, model_group_selector]
-                    ).then(
-                        fn=update_table_and_caption,
-                        inputs=[table_selector, super_group_selector, model_group_selector],
-                        outputs=[data_component, caption_component, css_style]
-                    )
-                    
-                # --- Introduction Tab ---
-                with gr.TabItem("📚 Introduction"):
-                    gr.Markdown(LEADERBOARD_INTRODUCTION)
-                # --- Data Information Tab ---
-                with gr.TabItem("📝 Data Information"):
-                    gr.Markdown(DATA_INFO, elem_classes="markdown-text")
-                # --- Submit Tab ---
-                with gr.TabItem("🚀 Submit"):
-                    with gr.Row():
-                        gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
-        
-        # -------------------------
-        # Tab 2: Image Processing (Program B)
-        # -------------------------
-        with gr.TabItem("Image Processing"):
-            # A default image is shown for context.
-            gr.Image("Caracal.jpg", interactive=False)
-            # It is important to create a state variable to store the OCR/NER result.
-            ocr_state = gr.State()
-            with gr.Tab(label="Image Input", elem_classes="tabs"):
-                with gr.Row():
-                    with gr.Column(elem_classes="input-container"):
-                        input_img = gr.Image(label="Input Picture", elem_classes="gr-image-input")
-                        model_selector = gr.Dropdown(
-                            choices=list(models.keys()),
-                            label="Model",
-                            value="Qwen/Qwen2.5-VL-7B-Instruct",
-                            elem_classes="gr-dropdown"
-                        )
-                        with gr.Row():
-                            ner_checkbox = gr.Checkbox(label="Run Named Entity Recognition", value=False)
-                            ner_labels = gr.Textbox(
-                                label="NER Labels (comma-separated)", 
-                                value=DEFAULT_NER_LABELS,
-                                visible=False
-                            )
-                        submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
-                    with gr.Column(elem_classes="output-container"):
-                        output_text = gr.HighlightedText(label="Output Text", elem_id="output")
-                # Toggle visibility of the NER labels textbox.
-                ner_checkbox.change(
-                    lambda x: gr.update(visible=x),
-                    inputs=[ner_checkbox],
-                    outputs=[ner_labels]
+            single_image_caption = "**Table 2: MEGA-Bench Single-image setting results.** The number in the parentheses is the number of tasks in each keyword. <br> This subset contains 273 single-image tasks from the Core set and 42 single-image tasks from the Open-ended set. For open-source models, we drop the image input in the 1-shot demonstration example so that the entire query contains a single image only. <br> Compared to the default table, some models with only single-image support are added."
+
+            caption_component = gr.Markdown(
+                value=default_caption,
+                elem_classes="table-caption",
+                latex_delimiters=[{"left": "$", "right": "$", "display": False}],
+            )
+
+            with gr.Row():
+                super_group_selector = gr.Radio(
+                    choices=list(default_loader.SUPER_GROUPS.keys()),
+                    label="Select a dimension to display breakdown results. We use different column colors to distinguish the overall benchmark scores and breakdown results.",
+                    value=list(default_loader.SUPER_GROUPS.keys())[0]
                 )
-                submit_btn.click(
-                    fn=run_example,
-                    inputs=[input_img, model_selector, ner_checkbox, ner_labels],
-                    outputs=[output_text, ocr_state]
+                model_group_selector = gr.Radio(
+                    choices=list(BASE_MODEL_GROUPS.keys()),
+                    label="Select a model group",
+                    value="All"
                 )
-            with gr.Row():
-                filename = gr.Textbox(label="Save filename (without extension)", placeholder="Enter filename to save")
-                download_btn = gr.Button("Download Image & Text", elem_classes="submit-btn")
-                download_output = gr.File(label="Download")
-            download_btn.click(
-                fn=create_zip,
-                inputs=[input_img, filename, ocr_state],
-                outputs=[download_output]
-            )
 
-# ----------------------------------------------------------------
-# FUNCTION DEFINITIONS
-# ----------------------------------------------------------------
+            initial_headers, initial_data = default_loader.get_leaderboard_data(list(default_loader.SUPER_GROUPS.keys())[0], "All")
+            data_component = gr.Dataframe(
+                value=initial_data,
+                headers=initial_headers,
+                datatype=["number", "html"] + ["number"] * (len(initial_headers) - 2),
+                interactive=False,
+                elem_classes="custom-dataframe",
+                max_height=2400,
+                column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(initial_headers) - 5),
+            )
 
-def update_table_and_caption(table_type, super_group, model_group):
-    """
-    Updates the leaderboard DataFrame, caption and CSS based on the table type and selectors.
-    """
-    if table_type == "Default":
-        headers, data = default_loader.get_leaderboard_data(super_group, model_group)
-        caption = ("**Table 1: MEGA-Bench full results.** The number in the parentheses is the number of tasks "
-                   "of each keyword. <br> The Core set contains $N_{\\text{core}} = 440$ tasks evaluated by rule-based metrics, and the "
-                   "Open-ended set contains $N_{\\text{open}} = 65$ tasks evaluated by a VLM judge (we use GPT-4o-0806). <br> "
-                   "Different from the results in our paper, we only use the Core results with CoT prompting here for clarity and compatibility "
-                   "with the released data. <br> $\\text{Overall} \\ = \\ \\frac{\\text{Core} \\ \\cdot \\ N_{\\text{core}} \\ + \\ \\text{Open-ended} "
-                   "\\ \\cdot \\ N_{\\text{open}}}{N_{\\text{core}} \\ + \\ N_{\\text{open}}}$ <br> * indicates self-reported results from the model authors.")
-    else:  # Single-image table
-        headers, data = si_loader.get_leaderboard_data(super_group, model_group)
-        caption = ("**Table 2: MEGA-Bench Single-image setting results.** The number in the parentheses is the number of tasks "
-                   "in each keyword. <br> This subset contains 273 single-image tasks from the Core set and 42 single-image tasks from the Open-ended set. "
-                   "For open-source models, we drop the image input in the 1-shot demonstration example so that the entire query contains a single image only. <br> "
-                   "Compared to the default table, some models with only single-image support are added.")
-    
-    dataframe = gr.Dataframe(
-        value=data,
-        headers=headers,
-        datatype=["number", "html"] + ["number"] * (len(headers) - 2),
-        interactive=True,
-        column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(headers) - 5),
-    )
-    style_html = f"<style>{base_css}\n{table_css}</style>"
-    return dataframe, caption, style_html
+            def update_table_and_caption(table_type, super_group, model_group):
+                if table_type == "Default":
+                    headers, data = default_loader.get_leaderboard_data(super_group, model_group)
+                    caption = default_caption
+                else:  # Single-image
+                    headers, data = si_loader.get_leaderboard_data(super_group, model_group)
+                    caption = single_image_caption
+                
+                return [
+                    gr.Dataframe(
+                        value=data,
+                        headers=headers,
+                        datatype=["number", "html"] + ["number"] * (len(headers) - 2),
+                        interactive=False,
+                        column_widths=["100px", "240px"] + ["160px"] * 3 + ["210px"] * (len(headers) - 5),
+                    ),
+                    caption,
+                    f"<style>{base_css}\n{table_css}</style>"
+                ]
+
+            def update_selectors(table_type):
+                loader = default_loader if table_type == "Default" else si_loader
+                return [
+                    gr.Radio(choices=list(loader.SUPER_GROUPS.keys())),
+                    gr.Radio(choices=list(loader.MODEL_GROUPS.keys()))
+                ]
+
+            refresh_button = gr.Button("Refresh")
+            
+            # Update click and change handlers to include caption updates
+            refresh_button.click(
+                fn=update_table_and_caption, 
+                inputs=[table_selector, super_group_selector, model_group_selector], 
+                outputs=[data_component, caption_component, css_style]
+            )
+            super_group_selector.change(
+                fn=update_table_and_caption, 
+                inputs=[table_selector, super_group_selector, model_group_selector], 
+                outputs=[data_component, caption_component, css_style]
+            )
+            model_group_selector.change(
+                fn=update_table_and_caption, 
+                inputs=[table_selector, super_group_selector, model_group_selector], 
+                outputs=[data_component, caption_component, css_style]
+            )
+            table_selector.change(
+                fn=update_selectors,
+                inputs=[table_selector],
+                outputs=[super_group_selector, model_group_selector]
+            ).then(
+                fn=update_table_and_caption,
+                inputs=[table_selector, super_group_selector, model_group_selector],
+                outputs=[data_component, caption_component, css_style]
+            )
 
-def update_selectors(table_type):
-    """
-    Updates the options in the radio selectors based on the selected table type.
-    """
-    loader = default_loader if table_type == "Default" else si_loader
-    return [gr.Radio.update(choices=list(loader.SUPER_GROUPS.keys())),
-            gr.Radio.update(choices=list(loader.MODEL_GROUPS.keys()))]
+        with gr.TabItem("📝 Data Information", elem_id="qa-tab-table2", id=2):
+            gr.Markdown(DATA_INFO, elem_classes="markdown-text")
 
-def array_to_image_path(image_array):
-    """
-    Converts a NumPy image array to a PIL Image, saves it to disk, and returns its path.
-    """
-    img = Image.fromarray(np.uint8(image_array))
-    img.thumbnail((1024, 1024))
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"image_{timestamp}.png"
-    img.save(filename)
-    return os.path.abspath(filename)
+        with gr.TabItem("🚀 Submit", elem_id="submit-tab", id=3):
+            with gr.Row():
+                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")
 
-@spaces.GPU
-def run_example(image, model_id="Qwen/Qwen2.5-VL-7B-Instruct", run_ner=False, ner_labels=DEFAULT_NER_LABELS):
-    """
-    Given an input image, uses the selected VL model to perform OCR (and optionally NER).
-    Returns the highlighted text and stores the raw OCR output in state.
-    """
-    text_input = "Convert the image to text."
-    image_path = array_to_image_path(image)
-    
-    model = models[model_id]
-    processor = processors[model_id]
-    
-    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
-    image_pil = Image.fromarray(image).convert("RGB")
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image_path},
-                {"type": "text", "text": text_input},
-            ],
-        }
-    ]
-    # Prepare text and vision inputs
-    text_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text_full],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to("cuda")
-    
-    # Generate model output
-    generated_ids = model.generate(**inputs, max_new_tokens=1024)
-    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
-    ocr_text = output_text[0]
-    
-    if run_ner:
-        ner_results = gliner_model.predict_entities(ocr_text, ner_labels.split(","), threshold=0.3)
-        highlighted_text = []
-        last_end = 0
-        for entity in sorted(ner_results, key=lambda x: x["start"]):
-            if last_end < entity["start"]:
-                highlighted_text.append((ocr_text[last_end:entity["start"]], None))
-            highlighted_text.append((ocr_text[entity["start"]:entity["end"]], entity["label"]))
-            last_end = entity["end"]
-        if last_end < len(ocr_text):
-            highlighted_text.append((ocr_text[last_end:], None))
-        result = TextWithMetadata(highlighted_text, original_text=ocr_text, entities=ner_results)
-        return result, result  # one for display, one for state
-    result = TextWithMetadata([(ocr_text, None)], original_text=ocr_text, entities=[])
-    return result, result
 
-def create_zip(image, fname, ocr_result):
-    """
-    Creates a zip file containing the saved image, the OCR text, and a JSON of the OCR output.
-    """
-    if not fname or image is None:
-        return None
-    try:
-        if isinstance(image, np.ndarray):
-            image_pil = Image.fromarray(image)
-        elif isinstance(image, Image.Image):
-            image_pil = image
-        else:
-            return None
-        
-        with tempfile.TemporaryDirectory() as temp_dir:
-            img_path = os.path.join(temp_dir, f"{fname}.png")
-            image_pil.save(img_path)
-            
-            original_text = ocr_result.original_text if ocr_result else ""
-            txt_path = os.path.join(temp_dir, f"{fname}.txt")
-            with open(txt_path, 'w', encoding='utf-8') as f:
-                f.write(original_text)
-            
-            json_data = {
-                "text": original_text,
-                "entities": ocr_result.entities if ocr_result else [],
-                "image_file": f"{fname}.png"
-            }
-            json_path = os.path.join(temp_dir, f"{fname}.json")
-            with open(json_path, 'w', encoding='utf-8') as f:
-                json.dump(json_data, f, indent=2, ensure_ascii=False)
-            
-            output_dir = "downloads"
-            os.makedirs(output_dir, exist_ok=True)
-            zip_path = os.path.join(output_dir, f"{fname}.zip")
-            with zipfile.ZipFile(zip_path, 'w') as zipf:
-                zipf.write(img_path, os.path.basename(img_path))
-                zipf.write(txt_path, os.path.basename(txt_path))
-                zipf.write(json_path, os.path.basename(json_path))
-            return zip_path
-    except Exception as e:
-        print(f"Error creating zip: {str(e)}")
-        return None
 
-# ----------------------------------------------------------------
-# Launch the merged Gradio app
-# ----------------------------------------------------------------
 if __name__ == "__main__":
-    demo.queue(api_open=False)
-    demo.launch(debug=True)
+    block.launch(share=True)