From cfd457fe663ec363514f90e0d34b6fc3fc0bcd30 Mon Sep 17 00:00:00 2001
From: Tyrin Todd <tyrin@modaic.dev>
Date: Thu, 2 Apr 2026 18:18:59 -0700
Subject: [PATCH] (no commit message)

---
 optimize.py | 49 -------------------------------------------------
 program.py  |  6 +++++-
 2 files changed, 5 insertions(+), 50 deletions(-)
 delete mode 100644 optimize.py

diff --git a/optimize.py b/optimize.py
deleted file mode 100644
index c1d066d..0000000
--- a/optimize.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import json
-
-import dspy
-
-from program import DocExtract
-
-
-def load_dataset(path):
-    with open(path) as f:
-        return [
-            dspy.Example(**json.loads(line)).with_inputs("context")
-            for line in f
-            if line.strip()
-        ]
-
-
-def classification_metric(
-    gold: dspy.Example,
-    pred: dspy.Prediction,
-    trace=None,
-    pred_name: str | None = None,
-    pred_trace=None,
-) -> dspy.Prediction:
-    del trace, pred_name, pred_trace
-
-    if getattr(pred, "label", None) == gold.label:
-        return dspy.Prediction(score=1.0, feedback="Correct.")
-
-    return dspy.Prediction(
-        score=0.0,
-        feedback=f"Expected `{gold.label}` because {gold.reason}",
-    )
-
-
-if __name__ == "__main__":
-    program = DocExtract.from_precompiled("tyrin/doc-extract")
-    dspy.configure(lm=dspy.LM("openai/gpt-4.1-mini", temperature=0.0))
-
-    optimized_program = dspy.GEPA(
-        metric=classification_metric,
-        reflection_lm=dspy.LM("openai/gpt-4.1-mini", temperature=1.0, max_tokens=8000),
-        auto="light",
-    ).compile(
-        program,
-        trainset=load_dataset("data/train.jsonl"),
-        valset=load_dataset("data/val.jsonl"),
-    )
-
-    optimized_program.push_to_hub("tyrin/doc-extract")
diff --git a/program.py b/program.py
index 31caa37..ff94dbb 100644
--- a/program.py
+++ b/program.py
@@ -26,4 +26,8 @@ class DocExtract(PrecompiledProgram):
 
 
 if __name__ == "__main__":
-    DocExtract().push_to_hub("tyrin/doc-extract")
+    program = DocExtract()
+    dspy.configure(lm=dspy.LM(model="gpt-5-mini"))
+    result = program(context="This is a legal document")
+    print(result.label)
+    program.push_to_hub("tyrin/doc-extract")