Adding Widget for Viewing PDFs

docs/configuration.mdx

@@ -79,6 +79,19 @@ path = "https://storage.googleapis.com/test/sample_data.csv"
 
 If the CSV file is located in a subdirectory, make sure the `path` is correct relative to the root directory.
 
+### PDF Example: `[data.sample_pdf]`
+
+#### Fields:
+
+- **type:** Use `"pdf"`
+- **path:** Relative path to the PDF file.
+
+#### Example PDF Connections:
+
+[docs.report_pdf]
+type = "pdf"
+path = "docs/report.pdf"
+
 ### JSON Example: `[data.sample_json]`
 
 You can use a JSON file as a data source with options to normalize nested structures.

examples/document/.gitignore

@@ -0,0 +1,3 @@
+secrets.toml
+.preswald_deploy
+.env.structured

examples/document/README.md

@@ -0,0 +1,6 @@
+# Preswald Project
+
+## Setup
+1. Configure your data connections in `preswald.toml`
+2. Add sensitive information (passwords, API keys) to `secrets.toml`
+3. Run your app with `preswald run`

examples/document/data/iris.csv

@@ -0,0 +1,151 @@
+"sepal.length","sepal.width","petal.length","petal.width","variety"
+5.1,3.5,1.4,.2,"Setosa"
+4.9,3,1.4,.2,"Setosa"
+4.7,3.2,1.3,.2,"Setosa"
+4.6,3.1,1.5,.2,"Setosa"
+5,3.6,1.4,.2,"Setosa"
+5.4,3.9,1.7,.4,"Setosa"
+4.6,3.4,1.4,.3,"Setosa"
+5,3.4,1.5,.2,"Setosa"
+4.4,2.9,1.4,.2,"Setosa"
+4.9,3.1,1.5,.1,"Setosa"
+5.4,3.7,1.5,.2,"Setosa"
+4.8,3.4,1.6,.2,"Setosa"
+4.8,3,1.4,.1,"Setosa"
+4.3,3,1.1,.1,"Setosa"
+5.8,4,1.2,.2,"Setosa"
+5.7,4.4,1.5,.4,"Setosa"
+5.4,3.9,1.3,.4,"Setosa"
+5.1,3.5,1.4,.3,"Setosa"
+5.7,3.8,1.7,.3,"Setosa"
+5.1,3.8,1.5,.3,"Setosa"
+5.4,3.4,1.7,.2,"Setosa"
+5.1,3.7,1.5,.4,"Setosa"
+4.6,3.6,1,.2,"Setosa"
+5.1,3.3,1.7,.5,"Setosa"
+4.8,3.4,1.9,.2,"Setosa"
+5,3,1.6,.2,"Setosa"
+5,3.4,1.6,.4,"Setosa"
+5.2,3.5,1.5,.2,"Setosa"
+5.2,3.4,1.4,.2,"Setosa"
+4.7,3.2,1.6,.2,"Setosa"
+4.8,3.1,1.6,.2,"Setosa"
+5.4,3.4,1.5,.4,"Setosa"
+5.2,4.1,1.5,.1,"Setosa"
+5.5,4.2,1.4,.2,"Setosa"
+4.9,3.1,1.5,.2,"Setosa"
+5,3.2,1.2,.2,"Setosa"
+5.5,3.5,1.3,.2,"Setosa"
+4.9,3.6,1.4,.1,"Setosa"
+4.4,3,1.3,.2,"Setosa"
+5.1,3.4,1.5,.2,"Setosa"
+5,3.5,1.3,.3,"Setosa"
+4.5,2.3,1.3,.3,"Setosa"
+4.4,3.2,1.3,.2,"Setosa"
+5,3.5,1.6,.6,"Setosa"
+5.1,3.8,1.9,.4,"Setosa"
+4.8,3,1.4,.3,"Setosa"
+5.1,3.8,1.6,.2,"Setosa"
+4.6,3.2,1.4,.2,"Setosa"
+5.3,3.7,1.5,.2,"Setosa"
+5,3.3,1.4,.2,"Setosa"
+7,3.2,4.7,1.4,"Versicolor"
+6.4,3.2,4.5,1.5,"Versicolor"
+6.9,3.1,4.9,1.5,"Versicolor"
+5.5,2.3,4,1.3,"Versicolor"
+6.5,2.8,4.6,1.5,"Versicolor"
+5.7,2.8,4.5,1.3,"Versicolor"
+6.3,3.3,4.7,1.6,"Versicolor"
+4.9,2.4,3.3,1,"Versicolor"
+6.6,2.9,4.6,1.3,"Versicolor"
+5.2,2.7,3.9,1.4,"Versicolor"
+5,2,3.5,1,"Versicolor"
+5.9,3,4.2,1.5,"Versicolor"
+6,2.2,4,1,"Versicolor"
+6.1,2.9,4.7,1.4,"Versicolor"
+5.6,2.9,3.6,1.3,"Versicolor"
+6.7,3.1,4.4,1.4,"Versicolor"
+5.6,3,4.5,1.5,"Versicolor"
+5.8,2.7,4.1,1,"Versicolor"
+6.2,2.2,4.5,1.5,"Versicolor"
+5.6,2.5,3.9,1.1,"Versicolor"
+5.9,3.2,4.8,1.8,"Versicolor"
+6.1,2.8,4,1.3,"Versicolor"
+6.3,2.5,4.9,1.5,"Versicolor"
+6.1,2.8,4.7,1.2,"Versicolor"
+6.4,2.9,4.3,1.3,"Versicolor"
+6.6,3,4.4,1.4,"Versicolor"
+6.8,2.8,4.8,1.4,"Versicolor"
+6.7,3,5,1.7,"Versicolor"
+6,2.9,4.5,1.5,"Versicolor"
+5.7,2.6,3.5,1,"Versicolor"
+5.5,2.4,3.8,1.1,"Versicolor"
+5.5,2.4,3.7,1,"Versicolor"
+5.8,2.7,3.9,1.2,"Versicolor"
+6,2.7,5.1,1.6,"Versicolor"
+5.4,3,4.5,1.5,"Versicolor"
+6,3.4,4.5,1.6,"Versicolor"
+6.7,3.1,4.7,1.5,"Versicolor"
+6.3,2.3,4.4,1.3,"Versicolor"
+5.6,3,4.1,1.3,"Versicolor"
+5.5,2.5,4,1.3,"Versicolor"
+5.5,2.6,4.4,1.2,"Versicolor"
+6.1,3,4.6,1.4,"Versicolor"
+5.8,2.6,4,1.2,"Versicolor"
+5,2.3,3.3,1,"Versicolor"
+5.6,2.7,4.2,1.3,"Versicolor"
+5.7,3,4.2,1.2,"Versicolor"
+5.7,2.9,4.2,1.3,"Versicolor"
+6.2,2.9,4.3,1.3,"Versicolor"
+5.1,2.5,3,1.1,"Versicolor"
+5.7,2.8,4.1,1.3,"Versicolor"
+6.3,3.3,6,2.5,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+7.1,3,5.9,2.1,"Virginica"
+6.3,2.9,5.6,1.8,"Virginica"
+6.5,3,5.8,2.2,"Virginica"
+7.6,3,6.6,2.1,"Virginica"
+4.9,2.5,4.5,1.7,"Virginica"
+7.3,2.9,6.3,1.8,"Virginica"
+6.7,2.5,5.8,1.8,"Virginica"
+7.2,3.6,6.1,2.5,"Virginica"
+6.5,3.2,5.1,2,"Virginica"
+6.4,2.7,5.3,1.9,"Virginica"
+6.8,3,5.5,2.1,"Virginica"
+5.7,2.5,5,2,"Virginica"
+5.8,2.8,5.1,2.4,"Virginica"
+6.4,3.2,5.3,2.3,"Virginica"
+6.5,3,5.5,1.8,"Virginica"
+7.7,3.8,6.7,2.2,"Virginica"
+7.7,2.6,6.9,2.3,"Virginica"
+6,2.2,5,1.5,"Virginica"
+6.9,3.2,5.7,2.3,"Virginica"
+5.6,2.8,4.9,2,"Virginica"
+7.7,2.8,6.7,2,"Virginica"
+6.3,2.7,4.9,1.8,"Virginica"
+6.7,3.3,5.7,2.1,"Virginica"
+7.2,3.2,6,1.8,"Virginica"
+6.2,2.8,4.8,1.8,"Virginica"
+6.1,3,4.9,1.8,"Virginica"
+6.4,2.8,5.6,2.1,"Virginica"
+7.2,3,5.8,1.6,"Virginica"
+7.4,2.8,6.1,1.9,"Virginica"
+7.9,3.8,6.4,2,"Virginica"
+6.4,2.8,5.6,2.2,"Virginica"
+6.3,2.8,5.1,1.5,"Virginica"
+6.1,2.6,5.6,1.4,"Virginica"
+7.7,3,6.1,2.3,"Virginica"
+6.3,3.4,5.6,2.4,"Virginica"
+6.4,3.1,5.5,1.8,"Virginica"
+6,3,4.8,1.8,"Virginica"
+6.9,3.1,5.4,2.1,"Virginica"
+6.7,3.1,5.6,2.4,"Virginica"
+6.9,3.1,5.1,2.3,"Virginica"
+5.8,2.7,5.1,1.9,"Virginica"
+6.8,3.2,5.9,2.3,"Virginica"
+6.7,3.3,5.7,2.5,"Virginica"
+6.7,3,5.2,2.3,"Virginica"
+6.3,2.5,5,1.9,"Virginica"
+6.5,3,5.2,2,"Virginica"
+6.2,3.4,5.4,2.3,"Virginica"
+5.9,3,5.1,1.8,"Virginica"

examples/document/hello.py

@@ -0,0 +1,33 @@
+# import fastplotlib as fpl
+# import imageio.v3 as iio
+# import numpy as np
+import plotly.express as px
+
+from preswald import (
+    document,
+    get_df,
+    plotly,
+    sidebar,
+    text,
+)
+
+
+sidebar()
+
+
+# 2. Histogram of Sepal Length
+text(
+    "## 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."
+)
+
+document("sample.pdf", "Original Research Paper")
+
+text(
+    "## 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."
+)
+
+df = get_df("iris_csv")
+
+fig = px.scatter(df, x="sepal.length", y="sepal.width", color="variety")
+
+plotly(fig)

examples/document/preswald.toml

@@ -0,0 +1,24 @@
+[project]
+title = "Preswald Project"
+version = "0.1.0"
+port = 8501
+slug = "document display"
+entrypoint = "hello.py"
+
+[branding]
+name = "Preswald Project"
+logo = "images/logo.png"
+favicon = "images/favicon.ico"
+primaryColor = "#F89613"
+
+[docs.sample_pdf]
+type = "pdf"
+path = "docs/sample.pdf"
+
+[data.iris_csv]
+type = "csv"
+path = "data/iris.csv"
+
+[logging]
+level = "INFO"  # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL
+format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

examples/document/pyproject.toml

@@ -0,0 +1,23 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "preswald-app"
+version = "0.1.0"
+description = "A Preswald application"
+requires-python = ">=3.8"
+dependencies = [
+    "preswald"
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+
+[tool.black]
+line-length = 88
+target-version = ['py38']
+
+[tool.isort]
+profile = "black"
+multi_line_output = 3

examples/iris/.gitignore

@@ -1,3 +1,3 @@
 secrets.toml
 .preswald_deploy
-.env.structured
+.env.structured

examples/user_event/hello.py

@@ -13,6 +13,8 @@
 
 # Create a scatter plot using the flattened data.
 # Assuming the JSON file has been flattened to include "user" and "details.clicks"
+print(type(df))
+
 fig = px.scatter(
     df,
     x="user",