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Update app.py
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app.py
CHANGED
@@ -4,67 +4,20 @@ import gradio as gr
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import pandas as pd
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import plotly.express as px
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import time
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import json
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from datasets import load_dataset
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# --- Constants ---
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PARAM_CHOICES = ['< 1B', '1B', '5B', '12B', '32B', '64B', '128B', '256B', '> 500B']
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#
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TOP_K_CHOICES = list(range(5, 51, 5))
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HF_DATASET_ID = "evijit/orgstats_daily_data"
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TAG_FILTER_CHOICES = [ "Audio & Speech", "Time series", "Robotics", "Music", "Video", "Images", "Text", "Biomedical", "Sciences" ]
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PIPELINE_TAGS = [ 'text-generation', 'text-to-image', 'text-classification', 'text2text-generation', 'audio-to-audio', 'feature-extraction', 'image-classification', 'translation', 'reinforcement-learning', 'fill-mask', 'text-to-speech', 'automatic-speech-recognition', 'image-text-to-text', 'token-classification', 'sentence-similarity', 'question-answering', 'image-feature-extraction', 'summarization', 'zero-shot-image-classification', 'object-detection', 'image-segmentation', 'image-to-image', 'image-to-text', 'audio-classification', 'visual-question-answering', 'text-to-video', 'zero-shot-classification', 'depth-estimation', 'text-ranking', 'image-to-video', 'multiple-choice', 'unconditional-image-generation', 'video-classification', 'text-to-audio', 'time-series-forecasting', 'any-to-any', 'video-text-to-text', 'table-question-answering' ]
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# --- Custom HTML, CSS, and JavaScript for the Slider ---
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custom_slider_js = """
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function createCustomSlider() {{
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const paramChoices = {js_param_choices};
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const slider = document.getElementById('noui-slider-container');
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if (slider.noUiSlider) {{ return; }} // Don't re-create if it already exists
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noUiSlider.create(slider, {{
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start: [0, paramChoices.length - 1],
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connect: true,
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step: 1,
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range: {{ 'min': 0, 'max': paramChoices.length - 1 }},
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pips: {{
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mode: 'values',
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values: Array.from(Array(paramChoices.length).keys()),
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density: 100 / (paramChoices.length - 1),
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format: {{ to: function(value) {{ return paramChoices[value]; }} }}
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}}
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}});
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const paramRangeStateInput = document.querySelector('#param-range-state-js textarea');
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const resetBtn = document.getElementById('reset-params-btn');
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const initialRange = [0, paramChoices.length - 1];
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function update(values) {{
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const intValues = values.map(v => parseInt(v, 10));
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const isDefault = intValues[0] === initialRange[0] && intValues[1] === initialRange[1];
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// Show/hide reset button
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resetBtn.style.display = isDefault ? 'none' : 'inline-block';
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// Update hidden state for Python
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const newValue = JSON.stringify(intValues);
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if (paramRangeStateInput.value !== newValue) {{
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paramRangeStateInput.value = newValue;
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const event = new Event('input', {{ bubbles: true }});
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paramRangeStateInput.dispatchEvent(event);
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}}
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}}
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slider.noUiSlider.on('update', update);
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// The reset button in the HTML calls this JS function directly
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window.resetSlider = function() {{
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slider.noUiSlider.set(initialRange);
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}}
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}}
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"""
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def load_models_data():
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overall_start_time = time.time()
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@@ -80,7 +33,9 @@ def load_models_data():
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print(msg)
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return df, True, msg
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except Exception as e:
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def get_param_range_values(param_range_labels):
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min_label, max_label = param_range_labels
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@@ -123,12 +78,7 @@ def create_treemap(treemap_data, count_by, title=None):
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fig.update_traces(textinfo="label+value+percent root", hovertemplate="<b>%{label}</b><br>%{value:,} " + count_by + "<br>%{percentRoot:.2%} of total<extra></extra>")
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return fig
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/noUiSlider/15.7.1/nouislider.min.css">
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<script src="https://cdnjs.cloudflare.com/ajax/libs/noUiSlider/15.7.1/nouislider.min.js"></script>
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"""
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with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_head) as demo:
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models_data_state = gr.State(pd.DataFrame())
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loading_complete_state = gr.State(False)
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@@ -143,25 +93,20 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_he
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pipeline_filter_dropdown = gr.Dropdown(label="Select Pipeline Tag", choices=PIPELINE_TAGS, value=None, visible=False)
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with gr.Group():
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gr.
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<div style=
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.noUi-handle::after, .noUi-handle::before { display: none; }
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</style>
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""")
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param_range_state_js = gr.Textbox(value=PARAM_STATE_DEFAULT_JSON, visible=False, elem_id="param-range-state-js")
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top_k_dropdown = gr.Dropdown(label="Number of Top Organizations", choices=TOP_K_CHOICES, value=25)
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skip_orgs_textbox = gr.Textbox(label="Organizations to Skip (comma-separated)", value="TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski")
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generate_plot_button = gr.Button(value="Generate Plot", variant="primary", interactive=False)
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@@ -170,6 +115,23 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_he
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plot_output = gr.Plot()
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status_message_md = gr.Markdown("Initializing...")
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data_info_md = gr.Markdown("")
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def _update_button_interactivity(is_loaded_flag): return gr.update(interactive=is_loaded_flag)
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loading_complete_state.change(fn=_update_button_interactivity, inputs=loading_complete_state, outputs=generate_plot_button)
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@@ -194,11 +156,14 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_he
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data_info_text = f"### Data Load Failed\n- {status_msg_from_load}"
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status_msg_ui = status_msg_from_load
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except Exception as e:
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status_msg_ui
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return current_df, load_success_flag, data_info_text, status_msg_ui
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def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice,
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if df_current_models is None or df_current_models.empty:
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return create_treemap(pd.DataFrame(), metric_choice, "Error: Model Data Not Loaded"), "Model data is not loaded."
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@@ -207,7 +172,6 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_he
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pipeline_to_use = pipeline_choice if filter_type == "Pipeline Filter" else None
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orgs_to_skip = [org.strip() for org in skip_orgs_input.split(',') if org.strip()]
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param_range_indices = json.loads(param_range_json)
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min_label = PARAM_CHOICES[int(param_range_indices[0])]
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max_label = PARAM_CHOICES[int(param_range_indices[1])]
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param_labels_for_filtering = [min_label, max_label]
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@@ -227,23 +191,21 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, head=custom_he
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plot_stats_md = f"## Plot Statistics\n- **Models shown**: {total_items_in_plot:,}\n- **Total {metric_choice}**: {int(total_value_in_plot):,}"
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return plotly_fig, plot_stats_md
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final_js = custom_slider_js.format(js_param_choices=json.dumps(PARAM_CHOICES))
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demo.load(
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fn=ui_load_data_controller,
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inputs=[],
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outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md]
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js=f"() => {{ {final_js} }}"
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)
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generate_plot_button.click(
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fn=ui_generate_plot_controller,
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inputs=[count_by_dropdown, filter_choice_radio, tag_filter_dropdown, pipeline_filter_dropdown,
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outputs=[plot_output, status_message_md]
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)
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if __name__ == "__main__":
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print(f"Application starting...")
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demo.queue().launch(
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# --- END OF FINAL, POLISHED FILE app.py ---
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import pandas as pd
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import plotly.express as px
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import time
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from datasets import load_dataset
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# Using the stable, community-built RangeSlider component
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from gradio_rangeslider import RangeSlider
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# --- Constants ---
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PARAM_CHOICES = ['< 1B', '1B', '5B', '12B', '32B', '64B', '128B', '256B', '> 500B']
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# The RangeSlider component uses a tuple for its default value
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PARAM_CHOICES_DEFAULT_INDICES = (0, len(PARAM_CHOICES) - 1)
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TOP_K_CHOICES = list(range(5, 51, 5))
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HF_DATASET_ID = "evijit/orgstats_daily_data"
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TAG_FILTER_CHOICES = [ "Audio & Speech", "Time series", "Robotics", "Music", "Video", "Images", "Text", "Biomedical", "Sciences" ]
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PIPELINE_TAGS = [ 'text-generation', 'text-to-image', 'text-classification', 'text2text-generation', 'audio-to-audio', 'feature-extraction', 'image-classification', 'translation', 'reinforcement-learning', 'fill-mask', 'text-to-speech', 'automatic-speech-recognition', 'image-text-to-text', 'token-classification', 'sentence-similarity', 'question-answering', 'image-feature-extraction', 'summarization', 'zero-shot-image-classification', 'object-detection', 'image-segmentation', 'image-to-image', 'image-to-text', 'audio-classification', 'visual-question-answering', 'text-to-video', 'zero-shot-classification', 'depth-estimation', 'text-ranking', 'image-to-video', 'multiple-choice', 'unconditional-image-generation', 'video-classification', 'text-to-audio', 'time-series-forecasting', 'any-to-any', 'video-text-to-text', 'table-question-answering' ]
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def load_models_data():
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overall_start_time = time.time()
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print(msg)
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return df, True, msg
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except Exception as e:
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err_msg = f"Failed to load dataset. Error: {e}"
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print(err_msg)
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return pd.DataFrame(), False, err_msg
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def get_param_range_values(param_range_labels):
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min_label, max_label = param_range_labels
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fig.update_traces(textinfo="label+value+percent root", hovertemplate="<b>%{label}</b><br>%{value:,} " + count_by + "<br>%{percentRoot:.2%} of total<extra></extra>")
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return fig
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with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True) as demo:
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models_data_state = gr.State(pd.DataFrame())
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loading_complete_state = gr.State(False)
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pipeline_filter_dropdown = gr.Dropdown(label="Select Pipeline Tag", choices=PIPELINE_TAGS, value=None, visible=False)
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with gr.Group():
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with gr.Row():
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gr.Markdown("<div style='font-weight: 500; padding-top: 10px;'>Parameters</div>")
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reset_params_button = gr.Button("🔄 Reset", visible=False, size="sm", min_width=80)
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param_range_slider = RangeSlider(
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minimum=0,
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maximum=len(PARAM_CHOICES) - 1,
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value=PARAM_CHOICES_DEFAULT_INDICES,
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step=1,
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label=None,
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show_label=False,
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)
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param_range_display = gr.Markdown(f"Range: `{PARAM_CHOICES[0]}` to `{PARAM_CHOICES[-1]}`")
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top_k_dropdown = gr.Dropdown(label="Number of Top Organizations", choices=TOP_K_CHOICES, value=25)
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skip_orgs_textbox = gr.Textbox(label="Organizations to Skip (comma-separated)", value="TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski")
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generate_plot_button = gr.Button(value="Generate Plot", variant="primary", interactive=False)
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plot_output = gr.Plot()
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status_message_md = gr.Markdown("Initializing...")
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data_info_md = gr.Markdown("")
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def update_param_ui(value: tuple):
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min_idx, max_idx = int(value[0]), int(value[1])
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is_default = (min_idx == 0 and max_idx == len(PARAM_CHOICES) - 1)
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display_text = f"Range: `{PARAM_CHOICES[min_idx]}` to `{PARAM_CHOICES[max_idx]}`"
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button_visibility = gr.update(visible=not is_default)
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return display_text, button_visibility
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param_range_slider.change(update_param_ui, param_range_slider, [param_range_display, reset_params_button])
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def reset_params():
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default_text = f"Range: `{PARAM_CHOICES[0]}` to `{PARAM_CHOICES[-1]}`"
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return PARAM_CHOICES_DEFAULT_INDICES, default_text, gr.update(visible=False)
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reset_params_button.click(reset_params, outputs=[param_range_slider, param_range_display, reset_params_button])
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def _update_button_interactivity(is_loaded_flag): return gr.update(interactive=is_loaded_flag)
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loading_complete_state.change(fn=_update_button_interactivity, inputs=loading_complete_state, outputs=generate_plot_button)
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data_info_text = f"### Data Load Failed\n- {status_msg_from_load}"
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status_msg_ui = status_msg_from_load
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except Exception as e:
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status_msg_ui = f"An unexpected error occurred: {str(e)}"
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data_info_text = f"### Critical Error\n- {status_msg_ui}"
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load_success_flag = False
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print(f"Critical error in ui_load_data_controller: {e}")
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return current_df, load_success_flag, data_info_text, status_msg_ui
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def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice,
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param_range_indices, k_orgs, skip_orgs_input, df_current_models, progress=gr.Progress()):
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if df_current_models is None or df_current_models.empty:
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return create_treemap(pd.DataFrame(), metric_choice, "Error: Model Data Not Loaded"), "Model data is not loaded."
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pipeline_to_use = pipeline_choice if filter_type == "Pipeline Filter" else None
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orgs_to_skip = [org.strip() for org in skip_orgs_input.split(',') if org.strip()]
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min_label = PARAM_CHOICES[int(param_range_indices[0])]
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max_label = PARAM_CHOICES[int(param_range_indices[1])]
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param_labels_for_filtering = [min_label, max_label]
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plot_stats_md = f"## Plot Statistics\n- **Models shown**: {total_items_in_plot:,}\n- **Total {metric_choice}**: {int(total_value_in_plot):,}"
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return plotly_fig, plot_stats_md
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demo.load(
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fn=ui_load_data_controller,
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inputs=[],
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outputs=[models_data_state, loading_complete_state, data_info_md, status_message_md]
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)
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generate_plot_button.click(
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fn=ui_generate_plot_controller,
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inputs=[count_by_dropdown, filter_choice_radio, tag_filter_dropdown, pipeline_filter_dropdown,
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param_range_slider, top_k_dropdown, skip_orgs_textbox, models_data_state],
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outputs=[plot_output, status_message_md]
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)
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if __name__ == "__main__":
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print(f"Application starting...")
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demo.queue().launch()
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# --- END OF FINAL, POLISHED FILE app.py ---
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