a2a-source-edit: write agent.py

agent.py

@@ -12,7 +12,6 @@ import io
 import json
 import os
 import re
-import uuid
 from datetime import datetime, timezone
 from decimal import Decimal, InvalidOperation
 from typing import Annotated, Any
@@ -31,7 +30,6 @@ from a2a_pack import (
     Pricing,
     Resources,
     RunContext,
-    State,
     UploadedFile,
 )
 
@@ -95,7 +93,6 @@ class CsvAnswersStudioV1(A2AAgent[CsvAnswersStudioV1Config, PlatformUserAuth]):
     config_model = CsvAnswersStudioV1Config
     auth_model = PlatformUserAuth
 
-    state = State.DURABLE
     resources = Resources(cpu="500m", memory="512Mi", max_runtime_seconds=120)
     pricing = Pricing(
         price_per_call_usd=0.0,
@@ -329,19 +326,21 @@ def _analyze_csv_text(*, analysis_id: str, csv_text: str, question: str) -> CsvA
     numeric_column = _choose_numeric_column(question, numeric_columns)
     row_label_column = _choose_label_column(header, numeric_columns)
 
+    best_index = -1
     best_record: dict[str, str] | None = None
     best_value: Decimal | None = None
-    for record in records:
+    for idx, record in enumerate(records):
         value = _parse_number(record.get(numeric_column, ""))
         if value is None:
             continue
         if best_value is None or value > best_value:
             best_value = value
             best_record = record
+            best_index = idx
     if best_record is None or best_value is None:
         return _error(analysis_id, "no_numeric_values", f"Column {numeric_column!r} does not contain numeric values.")
 
-    label = best_record.get(row_label_column) or f"row {records.index(best_record) + 1}"
+    label = best_record.get(row_label_column) or f"row {best_index + 1}"
     value_text = _format_decimal(best_value)
     answer = f"{label} has the highest {numeric_column} at {value_text}."
     chart_data = []
@@ -357,7 +356,7 @@ def _analyze_csv_text(*, analysis_id: str, csv_text: str, question: str) -> CsvA
             )
     source_rows = [
         {
-            "row_number": records.index(best_record) + 1,
+            "row_number": best_index + 1,
             "values": best_record,
             "matched_column": numeric_column,
             "matched_value": float(best_value),