Update README.md

README.md

@@ -13,7 +13,7 @@ short_description: A 1.5B agent that writes, runs and repairs its own EDA code
 
 Point it at any Hugging Face dataset. It extracts the schema, writes pandas /
 matplotlib analysis code, executes that code, repairs it from the traceback if it
-crashes, and assembles a Markdown + PDF report with tables and figures.
+crashes, and assembles a Markdown + PDF report with tables and plots.
 
 Built on **Qwen2.5-Coder-1.5B-Instruct**, small enough to run on a free GPU.
 
@@ -27,7 +27,7 @@ model, so the app is usable even when a visitor's daily GPU quota is exhausted.
 | 1. Load + normalize | Python | `load_dataset` with a raw-file fallback; quoted nulls converted to real `NaN`; index-like columns dropped |
 | 2. Context card | Python | One line per column: dtype, null count, cardinality, range |
 | 3. Code generation | **LLM** | Schema + instruction → one fenced Python block |
-| 4. Execution | Python | `exec` in an isolated namespace, stdout captured, figures collected |
+| 4. Execution | Python | `exec` in an isolated namespace, stdout captured, plots collected |
 | 5. Repair (×1) | **LLM** | Cleaned traceback (offending line + message) → corrected script |
 | 6. Facts + tables | Python | `idxmax`, `describe`, `corr` → Markdown tables |
 | 7. Narrative | **LLM** | One sentence per pre-computed fact |
@@ -35,7 +35,7 @@ model, so the app is usable even when a visitor's daily GPU quota is exhausted.
 ## Design note
 
 The model owns two jobs: writing analysis code and repairing it. Everything else —
-table rendering, figure placement, and every "which is highest" lookup — is computed
+table rendering, plot placement, and every "which is highest" lookup — is computed
 in Python and handed to the model as a stated fact.
 
 This was arrived at empirically. A 1.5B model reliably produced correct *descriptions*
@@ -50,7 +50,7 @@ instruction to it.
 repair loop: it failed on both test datasets, and the model fixed it correctly once
 (`select_dtypes(...).corr()`) and incorrectly once (`dropna(subset=[...]).corr()`).
 
-## Figure and table quality
+## Plot and table quality
 
 Generated plotting code is wrapped rather than trusted. `seaborn.heatmap`,
 `seaborn.histplot` and `plt.savefig` are patched around execution so that, on any
@@ -69,7 +69,7 @@ section, since they are almost always duplicate encodings rather than findings.
 
 ## Known limitations
 
-- The model is text-only and never sees the figures it generates; it is explicitly
+- The model is text-only and never sees the plots it generates; it is explicitly
   prevented from describing them.
 - `audit_numbers` flags numerals absent from the evidence. It detects fabrication,
   not misinterpretation, and false-positives on derived values.