example: add generative ui example (#2769)

examples/ai/chat/README.md

@@ -6,11 +6,13 @@ These examples show how to make chatbots with marimo, using [`mo.ui.chat`](https
 - `openai_example.py` shows how to make a chatbot powered by OpenAI models.
 - `anthropic_example.py` shows how to make a chatbot powered by Anthropic models.
 - `gemini.py` shows how to make a chatbot powered by Google models like Gemini.
+- `groq_example.py` shows how to make a chatbot powered by Groq models.
 - `mlx_chat.py` shows a simple chatbot using local on-device models with Apple's [MLX](https://github.com/ml-explore/mlx), a machine learning framework from Apple that is similar to JAX and PyTorch. This specific example uses the [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms) library. Note that Apple Silicon chips are required for using MLX.
 - `llm_datasette.py` shows how to make a chatbot powered by Simon W's LLM library.
 - `dagger_code_interpreter.py` shows how to make a basic code-interpreter chatbot powered by Dagger containers.
 - `recipe_bot.py` shows how to make a chatbot that can parse recipes from images.
 - `simplemind_example.py` shows how to integrate [simplemind](https://github.com/kennethreitz/simplemind).
+- `generative_ui.py` shows how to make a chatbot that can generate UI code.
 
 Chatbot's in marimo are _reactive_: when the chatbot responds with a message,
 all other cells referencing the chatbot are automatically run or marked

examples/ai/chat/generative_ui.py

@@ -0,0 +1,95 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "ell-ai==0.0.14",
+#     "marimo",
+#     "openai==1.53.0",
+#     "polars==1.12.0",
+# ]
+# ///
+
+import marimo
+
+__generated_with = "0.9.14"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import polars as pl
+    import marimo as mo
+    import os
+
+    has_api_key = os.environ.get("OPENAI_API_KEY") is not None
+    mo.stop(
+        not has_api_key,
+        mo.md("Please set the `OPENAI_API_KEY` environment variable").callout(),
+    )
+
+    # Grab a dataset
+    df = pl.read_csv("hf://datasets/scikit-learn/Fish/Fish.csv")
+    return df, has_api_key, mo, os, pl
+
+
+@app.cell
+def __(df, mo):
+    import ell
+
+
+    @ell.tool()
+    def chart_data(x_encoding: str, y_encoding: str, color: str):
+        """Generate an altair chart"""
+        import altair as alt
+
+        return (
+            alt.Chart(df)
+            .mark_circle()
+            .encode(x=x_encoding, y=y_encoding, color=color)
+            .properties(width=500)
+        )
+
+
+    @ell.tool()
+    def filter_dataset(sql_query: str):
+        """
+        Filter a polars dataframe using SQL. Please only use fields from the schema.
+        When referring to the table in SQL, call it 'data'.
+        """
+        filtered = df.sql(sql_query, table_name="data")
+        return mo.ui.table(
+            filtered,
+            label=f"```sql\n{sql_query}\n```",
+            selection=None,
+            show_column_summaries=False,
+        )
+    return chart_data, ell, filter_dataset
+
+
+@app.cell
+def __(chart_data, df, ell, filter_dataset, mo):
+    @ell.complex(model="gpt-4o", tools=[chart_data, filter_dataset])
+    def analyze_dataset(prompt: str) -> str:
+        """You are a data scientist that can analyze a dataset"""
+        return f"I have a dataset with schema: {df.schema}. \n{prompt}"
+
+
+    def my_model(messages):
+        response = analyze_dataset(messages)
+        if response.tool_calls:
+            return response.tool_calls[0]()
+        return response.text
+
+
+    mo.ui.chat(
+        my_model,
+        prompts=[
+            "Can you chart two columns of your choosing?",
+            "Can you find the min, max of all numeric fields?",
+            "What is the sum of {{column}}?",
+        ],
+    )
+    return analyze_dataset, my_model
+
+
+if __name__ == "__main__":
+    app.run()

examples/ai/chat/recipe_bot.py

@@ -1,3 +1,10 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "marimo",
+#     "openai==1.53.0",
+# ]
+# ///
 import marimo
 
 __generated_with = "0.9.10"