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= [ # "altair>=5,<7", # "fsspec[http]>=2025.3", # "jevframe[pandas]==0.1.0", # "marimo>=0.24,<1", # "typesafe-sdk>=0.7,<0.8", # ] # /// import marimo __generated_with = "0.24.2" app = marimo.App(width="medium") with app.setup: import os import altair as alt import marimo as mo import pandas as pd from typesafe_sdk import Choice, Noul import jevframe.pandas # noqa: F401 from jevframe import MemoryCache @app.cell(hide_code=True) def brand_banner(): # Official jevframe SVG is embedded so this notebook also works as a standalone file. # Cloud artwork from https://typesafe.ai/ mo.Html("""

JEVFRAME × TYPESAFE AISEMANTIC DATAFRAMES

jevframe brings TypeSafe AI’s Jev to pandas and Polars.
Ask natural-language questions. Get structured decisions for every row.

""") return @app.cell def _(): mo.md(""" # Semantic questions over real customer reviews Turn customer feedback into columns you can filter, sort, and plot with `jevframe`. Ask whether a customer is **dissatisfied**, whether they report a **product defect**, and **what the review is about**. Edit the questions, then click **Evaluate reviews** to see the answers and their full probability distributions. Choose separate columns or one structured column containing the same answers. """) return @app.cell(hide_code=True) def _(): mo.md(r""" ## Typesafe AI key - **Locally**: `export TYPESAFE_API_KEY='your-key'` before starting marimo - **On molab:** open Development Panel in the bottom left corner, click the **Secrets** tab and add `TYPESAFE_API_KEY`. """) return @app.cell def review_storage(): import fsspec # Keep the filesystem as a notebook variable for marimo's Remote Storage panel. # Cache the small archive locally: UCI's HTTP response is not seekable. review_files = fsspec.filesystem( "zip", fo=( "simplecache::https://archive.ics.uci.edu/static/public/331/" "sentiment+labelled+sentences.zip" ), target_options={"https": {"timeout": 30}}, ) return (review_files,) @app.cell def _(review_files): import csv @mo.cache def load_public_reviews(): with review_files.open( "sentiment labelled sentences/amazon_cells_labelled.txt", "rb" ) as source: frame = pd.read_csv( source, sep="\t", names=["review", "reference_sentiment"], quoting=csv.QUOTE_NONE, dtype={"review": "string", "reference_sentiment": "int64"}, ) if len(frame) != 1000 or set(frame["reference_sentiment"]) != {0, 1}: raise ValueError("The UCI download does not match the expected review dataset.") frame["reference_sentiment"] = frame["reference_sentiment"].map( {0: "negative", 1: "positive"} ) frame.index = pd.RangeIndex(1, len(frame) + 1, name="review_id") return frame all_reviews = load_public_reviews().sample(frac=1, random_state=42) cache = MemoryCache(max_entries=10_000) mo.md(""" ## Real customer feedback **1,000 Amazon review excerpts** from [UCI's Sentiment Labelled Sentences](https://doi.org/10.24432/C57604). Start with 50 rows, or increase the sample below. The shuffle is fixed, so larger samples include the same earlier rows and can reuse cached answers. Browse the source files under **Files → Remote Storage → review_files**. The public ZIP is downloaded once to a temporary cache by fsspec. The original sentiment labels are shown for comparison; **only the review text is sent to Jev**. This dataset deliberately contains 500 positive and 500 negative excerpts, so it does not estimate the rate of unhappy customers. Source: Kotzias et al. (2015), *From Group to Individual Labels using Deep Features*. Dataset licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). """) return all_reviews, cache @app.cell(hide_code=True) def sample_size_control(all_reviews): sample_size = mo.ui.slider( start=10, stop=len(all_reviews), step=10, value=50, label="Reviews to evaluate", show_value=True, full_width=True, ) sample_size # noqa: B018 return (sample_size,) @app.cell(hide_code=True) def sample_reviews(all_reviews, sample_size): # Ordinary pandas row selection; preserve each review's original row number. reviews = all_reviews.head(sample_size.value).copy() mo.vstack( [ mo.md( f"**{len(reviews):,} reviews selected.** Click **Evaluate reviews** below " "to ask all three questions. Each uncached review uses one request." ), mo.ui.table(reviews, selection=None, page_size=5), ] ) return (reviews,) @app.cell def _(): question = mo.ui.text_area( value="Is this customer dissatisfied?", label="Dissatisfaction · yes/no probability", full_width=True, ) defect_question = mo.ui.text_area( value=( "Does this review report that the product is broken, unreliable, " "or does not work as expected?" ), label="Product defect · yes/no probability", full_width=True, ) topic_question = mo.ui.text_area( value="What is the main topic of this review?", label="Topic · functionality / usability / value / service / other", full_width=True, ) evaluate = mo.ui.run_button(label="Evaluate reviews") output_layout = mo.ui.dropdown( options={"Separate columns": "columns", "One structured column": "struct"}, value="Separate columns", label="Output layout", ) mo.vstack([question, defect_question, topic_question, output_layout, evaluate]) return defect_question, evaluate, output_layout, question, topic_question @app.cell async def _( cache, defect_question, evaluate, output_layout, question, reviews, topic_question, ): mo.stop(not evaluate.value) mo.stop( not os.environ.get("TYPESAFE_API_KEY", "").strip(), mo.md( "Add **TYPESAFE_API_KEY** in molab's **Secrets** sidebar, then click " "**Evaluate reviews** again. Locally, export the variable before starting marimo." ).callout(kind="warn"), ) mo.stop( not all(_input.value.strip() for _input in [question, defect_question, topic_question]), mo.md("Enter all three questions first."), ) with mo.status.progress_bar(total=len(reviews), title="Evaluating reviews") as _bar: decisions = await reviews.jev.evaluate( # Keep the reference labels out of the model's context. state="review", questions={ "dissatisfied": Noul(instructions=question.value), "defect": Noul(instructions=defect_question.value), "topic": Choice( instructions=topic_question.value, criteria={ "functionality": ( "Performance, reliability, defects, or whether the product works" ), "usability": "Ease of use, setup, comfort, fit, or design", "value": "Price, affordability, or value for money", "service": "Customer support, delivery, seller service, or returns", "other": "General praise, recommendations, or another topic", }, ), }, output=output_layout.value, cache=cache, errors="coerce", max_concurrency=4, progress=lambda update: _bar.update(), ) results = pd.concat([reviews, decisions], axis=1) _failed = decisions.isna().all(axis=1) results.insert(0, "evaluation_status", _failed.map({False: "ok", True: "failed"})) _message = ( f"**{len(results) - int(_failed.sum()):,} of {len(results):,} " "reviews evaluated successfully.**" ) if _failed.any(): _message += ( f" {int(_failed.sum()):,} failed rows are marked in the table and keep missing " "probabilities. The histogram includes valid results only. " "See the evaluation warning for failure positions." ) mo.md(_message).callout(kind="warn" if _failed.any() else "success") return decisions, results @app.cell def _(results): mo.ui.table(results, selection=None) return @app.cell def _(decisions): # Unpack for plotting only; inference and the result table use the selected layout. _columns = ( pd.json_normalize(decisions["result"].tolist()).set_axis(decisions.index) if "result" in decisions.columns else decisions ) mo.stop( _columns.empty or _columns.dropna(how="all").empty, mo.md("No valid results to plot. Check the evaluation warning before trying again."), ) _distribution = _columns.melt( value_vars=["dissatisfied__probability", "defect__probability"], var_name="question", value_name="probability", ) _distribution["question"] = _distribution["question"].map( {"dissatisfied__probability": "Dissatisfied", "defect__probability": "Product defect"} ) alt.Chart(_distribution.dropna(subset=["probability"])).mark_bar(color="#267f79").encode( x=alt.X( "probability:Q", bin=alt.Bin(step=0.1, extent=[0, 1]), scale=alt.Scale(domain=[0, 1]), title="Probability of yes", ), y=alt.Y("count():Q", title="Reviews", axis=alt.Axis(tickMinStep=1)), tooltip=[alt.Tooltip("count():Q", title="Reviews")], ).properties(width=260, height=240).facet(column=alt.Column("question:N", title=None)) return @app.cell def _(reviews): mo.md(f""" ### One row, one request, several answers Each review is sent in one request containing all three questions. 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