Filter app #104
Filter app #104
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…elds when a new context is selected
| from app.components.references import ReferencesComponent | ||
| from app.components.sliders import SlidersComponent | ||
| from app.components.trivia import TriviaComponent | ||
| from app.utils import EvolutionHandler |
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We get rid of all the old components but reuse some in our new components. The components now correspond to portions of the page rather than by functionality which makes things neater
| intro_component = IntroComponent() | ||
| context_component = ContextComponent(app_df, evolution_handler) | ||
| filter_component = FilterComponent(evolution_handler) | ||
| dms_component = DMSComponent(app_df, evolution_handler) |
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We pass our app df and evolution handler to the components so that they have the ability to prescribe, look at context, etc.
| in tons of carbon per hectare) | ||
| context_component.register_callbacks(app) | ||
| filter_component.register_callbacks(app) | ||
| dms_component.register_callbacks(app) |
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Instead of all the long series of callbacks we now register all the callbacks at once with these simple functions. We could abstract these into a general Component class later and just call register_callbacks on them all.
| dms_component.get_div(), | ||
| references_component.get_references_div() | ||
| ] | ||
| ) |
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Layout is now super clean. Just calls get_div from each of our components in the order they should display on the apge
| window.dccFunctions = window.dccFunctions || {}; | ||
| window.dccFunctions.percentSlider = function(value) { | ||
| return Math.round(value * 100); | ||
| } |
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So that our sliders show as percents and not proportions (10% vs. 0.1)
| Component containing map as well as dropdowns and input fields for picking a more specific context. | ||
| """ | ||
| def __init__(self, app_df: pd.DataFrame, handler: EvolutionHandler): | ||
| self.map_component = MapComponent(app_df) |
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We reuse the old map component in our new context component
| def create_label_and_value(self, label: str, value: html.Div) -> html.Div: | ||
| """ | ||
| Standard dash function that pairs a label with any arbitrary value Div. | ||
| """ |
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This function lines up a label with a div so we reuse it for the Lat/dropdown, lon/dropdown, year/input
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Trimmed a lot of the fat off and only kept what we use in the new app
| Input("year-input", "value"), | ||
| Input("lat-dropdown", "value"), | ||
| Input("lon-dropdown", "value") | ||
| ) | ||
| def update_context_chart(chart_type, year, lat, lon): | ||
| def update_context_chart(year, lat, lon): |
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We don't use the pie charts anymore but may add them back in later
| presc = pd.Series(sliders, index=constants.RECO_COLS) | ||
| # If we have no prescription just show the context chart | ||
| bad_sliders = sum(sliders) < -0.01 or sum(sliders) > 0.01 | ||
| if all(slider == 0 for slider in sliders) or bad_sliders: |
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We add a prescribe + predict button instead of automatically updating the chart so we don't have to validate that the sliders sum to 100 on the fly
| Output("pbar", "children"), | ||
| [Input({"type": "diff-slider", "index": ALL}, "value")] | ||
| ) | ||
| def update_pbar(sliders) -> int: |
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We use a progress bar to show how much land is used
| class FilterComponent: | ||
| """ | ||
| Component with sliders/preset buttons to filter outcomes by as well as visualizing the selected prescriptors. | ||
| """ |
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This is almost the exact same as the climate change demo
| """ | ||
| Plots the prescriptions for the selected candidates and grays the rest out, shifting them to the right. | ||
| Also adds the original context as the far leftmost bar. | ||
| """ |
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Logic for our nice stacked bar chart
| def select_preset(n_clicks, results_json): | ||
| """ | ||
| Selects a preset for the filter sliders from the 3 presets. | ||
| """ |
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We make the presets just slice the outcomes into thirds and each third is our preset
| @@ -1,4 +1,4 @@ | |||
| dash==2.10.2 | |||
| dash==2.15 | |||
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Needed to update dash to get functionality to have the sliders show as percentages
| hf_hub_download(repo_id="projectresilience/land-use-app-data", | ||
| filename="app_data.csv", | ||
| local_dir="app/data", | ||
| repo_type="dataset") |
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Our process data script no longer downloads the entire dataset then processes it. It simply downloads a preprocessed file I left in huggingface
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Scaler for our predictor/prescriptors
| def add_nonland(context_actions_df: pd.DataFrame) -> pd.DataFrame: | ||
| """ | ||
| Adds a nonland column to the context_actions_df | ||
| """ |
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This function is modified to take a dataframe now instead of a series
| cand_path = self.prescriptor_path / f"{cand_id}.pt" | ||
| candidate = Candidate(**candidate_params, cand_id=cand_id) | ||
| candidate.load_state_dict(torch.load(cand_path)) | ||
| prescriptors[cand_id] = LandUsePrescriptor(candidate, encoder) |
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For now the prescriptors are hard-coded and saved in the repo. In the future these should live in HuggingFace
| @@ -8,7 +8,7 @@ dask==2023.6.1 | |||
| datasets==2.16.1 | |||
| flake8==7.1.0 | |||
| Flask==2.2.5 | |||
| geopandas==0.13.2 | |||
| geopandas==0.14.4 | |||
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Updated geopandas because we were getting an error with the fiona package
| @@ -20,7 +20,7 @@ pandas==1.5.3 | |||
| plotly==5.14.1 | |||
| protobuf==3.20.3 | |||
| -e git+https://github.com/Project-Resilience/sdk.git@c0e5300e56f5fce2f6cf55c328c83e7055e541be#egg=prsdk | |||
| pylint==2.17.4 | |||
| pylint==3.3.1 | |||
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Updated pylint as we were linting unnecessary files
use_cases/eluc/app/app.py
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| if __name__ == '__main__': | ||
| app.run_server(host='0.0.0.0', debug=False, port=4057, use_reloader=False, threaded=False) | ||
| app.run_server(host='0.0.0.0', debug=False, port=4057, use_reloader=True, threaded=True) |
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Did you mean to leave user_reloader to True?
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According to https://dash.plotly.com/devtools you're not changing the code in the deployed app, and it makes it slower to start, so I'll leave it to False
Redid app to use our new flow of selecting a context -> filtering prescriptors by desired outcome -> choosing a prescriptor -> play with the actions -> see the outcomes.
Additionally updated deployment to download a smaller, preprocessed dataset instead of the entire thing. We also save the demo models in the repo for simplicity.