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AkashWudali12AkashWudali12
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adding pdf widget, fixed json id gen
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‎docs/configuration.mdx

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If the CSV file is located in a subdirectory, make sure the `path` is correct relative to the root directory.
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### PDF Example: `[data.sample_pdf]`
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#### Fields:
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- **type:** Use `"pdf"`
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- **path:** Relative path to the PDF file.
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#### Example PDF Connections:
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[docs.report_pdf]
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type = "pdf"
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path = "docs/report.pdf"
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### JSON Example: `[data.sample_json]`
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You can use a JSON file as a data source with options to normalize nested structures.

‎examples/document/.gitignore

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secrets.toml
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.preswald_deploy
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.env.structured

‎examples/document/README.md

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# Preswald Project
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## Setup
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1. Configure your data connections in `preswald.toml`
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2. Add sensitive information (passwords, API keys) to `secrets.toml`
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3. Run your app with `preswald run`

‎examples/document/data/iris.csv

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"sepal.length","sepal.width","petal.length","petal.width","variety"
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5.1,3.5,1.4,.2,"Setosa"
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4.9,3,1.4,.2,"Setosa"
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4.7,3.2,1.3,.2,"Setosa"
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4.6,3.1,1.5,.2,"Setosa"
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5,3.6,1.4,.2,"Setosa"
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5.4,3.9,1.7,.4,"Setosa"
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4.6,3.4,1.4,.3,"Setosa"
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5,3.4,1.5,.2,"Setosa"
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4.4,2.9,1.4,.2,"Setosa"
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4.9,3.1,1.5,.1,"Setosa"
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5.4,3.7,1.5,.2,"Setosa"
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4.8,3.4,1.6,.2,"Setosa"
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4.8,3,1.4,.1,"Setosa"
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4.3,3,1.1,.1,"Setosa"
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5.8,4,1.2,.2,"Setosa"
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5.7,4.4,1.5,.4,"Setosa"
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5.4,3.9,1.3,.4,"Setosa"
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5.1,3.5,1.4,.3,"Setosa"
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5.7,3.8,1.7,.3,"Setosa"
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5.1,3.8,1.5,.3,"Setosa"
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5.4,3.4,1.7,.2,"Setosa"
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5.1,3.7,1.5,.4,"Setosa"
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4.6,3.6,1,.2,"Setosa"
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5.1,3.3,1.7,.5,"Setosa"
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4.8,3.4,1.9,.2,"Setosa"
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5,3,1.6,.2,"Setosa"
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5,3.4,1.6,.4,"Setosa"
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5.2,3.5,1.5,.2,"Setosa"
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5.2,3.4,1.4,.2,"Setosa"
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4.7,3.2,1.6,.2,"Setosa"
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4.8,3.1,1.6,.2,"Setosa"
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5.4,3.4,1.5,.4,"Setosa"
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5.2,4.1,1.5,.1,"Setosa"
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5.5,4.2,1.4,.2,"Setosa"
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4.9,3.1,1.5,.2,"Setosa"
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5,3.2,1.2,.2,"Setosa"
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5.5,3.5,1.3,.2,"Setosa"
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4.9,3.6,1.4,.1,"Setosa"
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4.4,3,1.3,.2,"Setosa"
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5.1,3.4,1.5,.2,"Setosa"
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5,3.5,1.3,.3,"Setosa"
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4.5,2.3,1.3,.3,"Setosa"
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4.4,3.2,1.3,.2,"Setosa"
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5,3.5,1.6,.6,"Setosa"
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5.1,3.8,1.9,.4,"Setosa"
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4.8,3,1.4,.3,"Setosa"
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5.1,3.8,1.6,.2,"Setosa"
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4.6,3.2,1.4,.2,"Setosa"
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5.3,3.7,1.5,.2,"Setosa"
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5,3.3,1.4,.2,"Setosa"
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7,3.2,4.7,1.4,"Versicolor"
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6.4,3.2,4.5,1.5,"Versicolor"
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6.9,3.1,4.9,1.5,"Versicolor"
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5.5,2.3,4,1.3,"Versicolor"
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6.5,2.8,4.6,1.5,"Versicolor"
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5.7,2.8,4.5,1.3,"Versicolor"
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6.3,3.3,4.7,1.6,"Versicolor"
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4.9,2.4,3.3,1,"Versicolor"
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6.6,2.9,4.6,1.3,"Versicolor"
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5.2,2.7,3.9,1.4,"Versicolor"
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5,2,3.5,1,"Versicolor"
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5.9,3,4.2,1.5,"Versicolor"
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6,2.2,4,1,"Versicolor"
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6.1,2.9,4.7,1.4,"Versicolor"
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5.6,2.9,3.6,1.3,"Versicolor"
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6.7,3.1,4.4,1.4,"Versicolor"
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5.6,3,4.5,1.5,"Versicolor"
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5.8,2.7,4.1,1,"Versicolor"
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6.2,2.2,4.5,1.5,"Versicolor"
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5.6,2.5,3.9,1.1,"Versicolor"
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5.9,3.2,4.8,1.8,"Versicolor"
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6.1,2.8,4,1.3,"Versicolor"
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6.3,2.5,4.9,1.5,"Versicolor"
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6.1,2.8,4.7,1.2,"Versicolor"
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6.4,2.9,4.3,1.3,"Versicolor"
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6.6,3,4.4,1.4,"Versicolor"
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6.8,2.8,4.8,1.4,"Versicolor"
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6.7,3,5,1.7,"Versicolor"
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6,2.9,4.5,1.5,"Versicolor"
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5.7,2.6,3.5,1,"Versicolor"
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5.5,2.4,3.8,1.1,"Versicolor"
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5.5,2.4,3.7,1,"Versicolor"
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5.8,2.7,3.9,1.2,"Versicolor"
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6,2.7,5.1,1.6,"Versicolor"
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5.4,3,4.5,1.5,"Versicolor"
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6,3.4,4.5,1.6,"Versicolor"
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6.7,3.1,4.7,1.5,"Versicolor"
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6.3,2.3,4.4,1.3,"Versicolor"
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5.6,3,4.1,1.3,"Versicolor"
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5.5,2.5,4,1.3,"Versicolor"
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5.5,2.6,4.4,1.2,"Versicolor"
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6.1,3,4.6,1.4,"Versicolor"
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5.8,2.6,4,1.2,"Versicolor"
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5,2.3,3.3,1,"Versicolor"
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5.6,2.7,4.2,1.3,"Versicolor"
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5.7,3,4.2,1.2,"Versicolor"
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5.7,2.9,4.2,1.3,"Versicolor"
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6.2,2.9,4.3,1.3,"Versicolor"
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5.1,2.5,3,1.1,"Versicolor"
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5.7,2.8,4.1,1.3,"Versicolor"
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6.3,3.3,6,2.5,"Virginica"
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5.8,2.7,5.1,1.9,"Virginica"
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7.1,3,5.9,2.1,"Virginica"
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6.3,2.9,5.6,1.8,"Virginica"
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6.5,3,5.8,2.2,"Virginica"
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7.6,3,6.6,2.1,"Virginica"
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4.9,2.5,4.5,1.7,"Virginica"
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7.3,2.9,6.3,1.8,"Virginica"
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6.7,2.5,5.8,1.8,"Virginica"
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7.2,3.6,6.1,2.5,"Virginica"
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6.5,3.2,5.1,2,"Virginica"
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6.4,2.7,5.3,1.9,"Virginica"
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6.8,3,5.5,2.1,"Virginica"
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5.7,2.5,5,2,"Virginica"
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5.8,2.8,5.1,2.4,"Virginica"
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6.4,3.2,5.3,2.3,"Virginica"
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6.5,3,5.5,1.8,"Virginica"
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7.7,3.8,6.7,2.2,"Virginica"
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7.7,2.6,6.9,2.3,"Virginica"
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6,2.2,5,1.5,"Virginica"
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6.9,3.2,5.7,2.3,"Virginica"
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5.6,2.8,4.9,2,"Virginica"
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7.7,2.8,6.7,2,"Virginica"
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6.3,2.7,4.9,1.8,"Virginica"
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6.7,3.3,5.7,2.1,"Virginica"
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7.2,3.2,6,1.8,"Virginica"
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6.2,2.8,4.8,1.8,"Virginica"
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6.1,3,4.9,1.8,"Virginica"
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6.4,2.8,5.6,2.1,"Virginica"
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7.2,3,5.8,1.6,"Virginica"
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7.4,2.8,6.1,1.9,"Virginica"
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7.9,3.8,6.4,2,"Virginica"
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6.4,2.8,5.6,2.2,"Virginica"
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6.3,2.8,5.1,1.5,"Virginica"
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6.1,2.6,5.6,1.4,"Virginica"
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7.7,3,6.1,2.3,"Virginica"
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6.3,3.4,5.6,2.4,"Virginica"
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6.4,3.1,5.5,1.8,"Virginica"
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6,3,4.8,1.8,"Virginica"
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6.9,3.1,5.4,2.1,"Virginica"
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6.7,3.1,5.6,2.4,"Virginica"
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6.9,3.1,5.1,2.3,"Virginica"
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5.8,2.7,5.1,1.9,"Virginica"
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6.8,3.2,5.9,2.3,"Virginica"
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6.7,3.3,5.7,2.5,"Virginica"
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6.7,3,5.2,2.3,"Virginica"
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6.3,2.5,5,1.9,"Virginica"
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6.5,3,5.2,2,"Virginica"
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6.2,3.4,5.4,2.3,"Virginica"
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5.9,3,5.1,1.8,"Virginica"

‎examples/document/docs/sample.pdf

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‎examples/document/hello.py

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# import fastplotlib as fpl
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# import imageio.v3 as iio
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# import numpy as np
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import plotly.express as px
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from preswald import (
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document,
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get_df,
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plotly,
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sidebar,
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text,
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)
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sidebar()
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# 2. Histogram of Sepal Length
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text(
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"## Iris Flower Classification Research Paper \n This research paper discusses the seminal work on iris flower classification, including Fisher's original 1936 paper that introduced this famous dataset for pattern recognition and machine learning."
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)
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document("sample.pdf", "Original Research Paper")
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text(
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"## Visualizing the Dataset Features \n Below we can see a scatter plot visualization of two key features used in iris classification: sepal length vs sepal width. The clear clustering shows why this dataset became a standard test case for machine learning algorithms."
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)
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df = get_df("iris_csv")
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fig = px.scatter(df, x="sepal.length", y="sepal.width", color="variety")
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plotly(fig)

‎examples/document/images/favicon.ico

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‎examples/document/images/logo.png

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‎examples/document/preswald.toml

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[project]
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title = "Preswald Project"
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version = "0.1.0"
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port = 8501
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slug = "document display"
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entrypoint = "hello.py"
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[branding]
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name = "Preswald Project"
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logo = "images/logo.png"
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favicon = "images/favicon.ico"
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primaryColor = "#F89613"
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[docs.sample_pdf]
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type = "pdf"
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path = "docs/sample.pdf"
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[data.iris_csv]
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type = "csv"
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path = "data/iris.csv"
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[logging]
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level = "INFO" # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
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format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

‎examples/document/pyproject.toml

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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "preswald-app"
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version = "0.1.0"
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description = "A Preswald application"
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requires-python = ">=3.8"
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dependencies = [
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"preswald"
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]
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[tool.hatch.build.targets.wheel]
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packages = ["."]
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[tool.black]
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line-length = 88
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target-version = ['py38']
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[tool.isort]
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profile = "black"
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multi_line_output = 3

‎examples/iris/.gitignore

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secrets.toml
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.preswald_deploy
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.env.structured
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.env.structured

‎examples/user_event/hello.py

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# Create a scatter plot using the flattened data.
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# Assuming the JSON file has been flattened to include "user" and "details.clicks"
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print(type(df))
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fig = px.scatter(
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df,
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x="user",

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