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gristlabs_grist-core/sandbox/grist/formula_prompt.py

285 lines
9.0 KiB

import ast
import json
import re
import textwrap
import asttokens
import asttokens.util
import six
import attribute_recorder
import objtypes
from codebuilder import make_formula_body
from column import is_visible_column, BaseReferenceColumn
from objtypes import RaisedException
import records
def column_type(engine, table_id, col_id):
col_rec = engine.docmodel.get_column_rec(table_id, col_id)
typ = col_rec.type
parts = typ.split(":")
if parts[0] == "Ref":
return parts[1]
elif parts[0] == "RefList":
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
return "list[{}]".format(parts[1])
elif typ == "Choice":
return choices(col_rec)
elif typ == "ChoiceList":
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
return "tuple[{}, ...]".format(choices(col_rec))
elif typ == "Any":
table = engine.tables[table_id]
col = table.get_column(col_id)
values = [col.raw_get(row_id) for row_id in table.row_ids]
return values_type(values)
else:
return dict(
Text="str",
Numeric="float",
Int="int",
Bool="bool",
Date="datetime.date",
DateTime="datetime.datetime",
Any="Any",
Attachments="Any",
)[parts[0]]
def choices(col_rec):
try:
widget_options = json.loads(col_rec.widgetOptions)
return "Literal{}".format(widget_options["choices"])
except (ValueError, KeyError):
return 'str'
def values_type(values):
types = set(type(v) for v in values) - {RaisedException}
optional = type(None) in types # pylint: disable=unidiomatic-typecheck
types.discard(type(None))
if types == {int, float}:
types = {float}
if len(types) != 1:
return "Any"
[typ] = types
val = next(v for v in values if isinstance(v, typ))
if isinstance(val, records.Record):
type_name = val._table.table_id
elif isinstance(val, records.RecordSet):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
type_name = "list[{}]".format(val._table.table_id)
elif isinstance(val, list):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
type_name = "list[{}]".format(values_type(val))
elif isinstance(val, set):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
type_name = "set[{}]".format(values_type(val))
elif isinstance(val, tuple):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
type_name = "tuple[{}, ...]".format(values_type(val))
elif isinstance(val, dict):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
type_name = "dict[{}, {}]".format(values_type(val.keys()), values_type(val.values()))
else:
type_name = typ.__name__
if optional:
type_name = "Optional[{}]".format(type_name)
return type_name
def referenced_tables(engine, table_id):
result = set()
queue = [table_id]
while queue:
cur_table_id = queue.pop()
if cur_table_id in result:
continue
result.add(cur_table_id)
for col_id, col in visible_columns(engine, cur_table_id):
if isinstance(col, BaseReferenceColumn):
target_table_id = col._target_table.table_id
if not target_table_id.startswith("_"):
queue.append(target_table_id)
return result - {table_id}
def all_other_tables(engine, table_id):
result = set(t for t in engine.tables.keys() if not t.startswith('_grist'))
return result - {table_id} - {'GristDocTour'}
def visible_columns(engine, table_id):
return [
(col_id, col)
for col_id, col in engine.tables[table_id].all_columns.items()
if is_visible_column(col_id)
]
def class_schema(engine, table_id, exclude_col_id=None, lookups=False):
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
result = "class {}:\n".format(table_id)
if lookups:
# Build a lookupRecords and lookupOne method for each table, providing some arguments hints
# for the columns that are visible.
lookupRecords_args = []
lookupOne_args = []
for col_id, col in visible_columns(engine, table_id):
if col_id != exclude_col_id:
lookupOne_args.append(col_id + '=None')
lookupRecords_args.append('%s=%s' % (col_id, col_id))
lookupOne_args.append('sort_by=None')
lookupRecords_args.append('sort_by=sort_by')
lookupOne_args_line = ', '.join(lookupOne_args)
lookupRecords_args_line = ', '.join(lookupRecords_args)
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
result += " def __len__(self):\n"
result += " return len(%s.lookupRecords())\n" % table_id
result += " @staticmethod\n"
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
result += " def lookupRecords(%s) -> list[%s]:\n" % (lookupOne_args_line, table_id)
result += " ...\n"
result += " @staticmethod\n"
result += " def lookupOne(%s) -> %s:\n" % (lookupOne_args_line, table_id)
result += " '''\n"
result += " Filter for one result matching the keys provided.\n"
result += " To control order, use e.g. `sort_by='Key' or `sort_by='-Key'`.\n"
result += " '''\n"
result += " return %s.lookupRecords(%s)[0]\n" % (table_id, lookupRecords_args_line)
result += "\n"
for col_id, col in visible_columns(engine, table_id):
if col_id != exclude_col_id:
result += " {}: {}\n".format(col_id, column_type(engine, table_id, col_id))
result += "\n"
return result
(core) Modify prompt so that model may say it cannot help with certain requests. Summary: This tweaks the prompting so that the user's message is given on its own instead of as a docstring within Python. This is so that the prompt makes sense when: - the user asks a question such as "Can you write me a formula which does ...?" rather than describing their formula as a docstring would, or - the user sends a message that doesn't ask for a formula at all (https://grist.slack.com/archives/C0234CPPXPA/p1687699944315069?thread_ts=1687698078.832209&cid=C0234CPPXPA) Also added wording for the model to refuse when the user asks for something that the model cannot do. Because the code (and maybe in some cases the model) for non-ChatGPT models relies on the prompt consisting entirely of Python code produced by the data engine (which no longer contains the user's message) those code paths have been disabled for now. Updating them now seems like undesirable drag, I think it'd be better to revisit this when iteration/experimentation has slowed down and stabilised. Test Plan: Added entries to the formula dataset where the response shouldn't contain a formula, indicated by the value `1` for the new column `no_formula`. This is somewhat successful, as the model does refuse to help in some of the new test cases, but not all. Performance on existing entries also seems a bit worse, but it's hard to distinguish this from random noise. Hopefully this can be remedied in the future with more work, e.g. automatic followup messages containing example inputs and outputs. Reviewers: paulfitz Reviewed By: paulfitz Subscribers: dsagal Differential Revision: https://phab.getgrist.com/D3936
1 year ago
def get_formula_prompt(engine, table_id, col_id, _description,
include_all_tables=True,
lookups=True):
result = ""
other_tables = (all_other_tables(engine, table_id)
if include_all_tables else referenced_tables(engine, table_id))
for other_table_id in sorted(other_tables):
result += class_schema(engine, other_table_id, None, lookups)
result += class_schema(engine, table_id, col_id, lookups)
return_type = column_type(engine, table_id, col_id)
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
result += "def {}(rec: {}) -> {}:\n".format(col_id, table_id, return_type)
return result
def indent(text, prefix, predicate=None):
"""
Copied from https://github.com/python/cpython/blob/main/Lib/textwrap.py for python2 compatibility.
"""
if six.PY3:
return textwrap.indent(text, prefix, predicate) # pylint: disable = no-member
if predicate is None:
def predicate(line):
return line.strip()
def prefixed_lines():
for line in text.splitlines(True):
yield (prefix + line if predicate(line) else line)
return ''.join(prefixed_lines())
def convert_completion(completion):
# Extract code from a markdown code block if needed.
match = re.search(r"```\w*\n(.*)```", completion, re.DOTALL)
if match:
completion = match.group(1)
result = textwrap.dedent(completion)
atok = asttokens.ASTText(result)
try:
# Constructing ASTText doesn't parse the code, but the .tree property does.
stmts = atok.tree.body
except SyntaxError:
# If we don't have valid Python code, don't suggest a formula at all
return ""
# If the code starts with imports, save them for later.
# In particular, the model may return something like:
# from datetime import date
# def my_column():
# ...
# We want to return just the function body, but we need to keep the import,
# i.e. move it 'inside the function'.
imports = ""
while stmts and isinstance(stmts[0], (ast.Import, ast.ImportFrom)):
imports += atok.get_text(stmts.pop(0)) + "\n"
(core) Prevent the AI assistant response from including class definitions Summary: Sometimes the model repeats the classes given in the prompt which would mess up extracting the actual formula. This diff solves this by: 1. Changes the generated Python schema so that (a) the thing that needs completing is a plain top level function instead of a property/method inside the class and (2) the classes are fully valid syntax, which makes it easier to 2. Remove classes from the parsed Python code when converting the completion to a formula. 3. Tweak the prompt wording to discourage including classes in general, especially because sometimes the model tries to solve the problem by defining extra methods/attributes/classes. While I was at it, I changed type hints to use builtins (e.g. `list` instead of `List`) to prevent `from typing import List` which was happening sometimes and would look weird in a formula. Similarly I removed `@dataclass` since that also implies an import, and this also fits with the tweaked wording that the classes are fake. Test Plan: Added a new test case to the formula dataset which triggers the unwanted behaviour. The factors that seem to trigger the problem are (1) a small schema so the classes are easier to repeat and (2) the need to import modules, which the model wants to place before all other code. The case failed before this diff and succeeded after. The tweaked wording reduces the chances of repeating the classes but didn't eliminate it, so forcibly removing the classes in Python was needed. There were also a couple of other existing cases where repeating the classes was observed before but not after. Overall the score increased from 49 to 51 out of 69 (including the new case). At one point the score was 53, but changes in whitespace were enough to make it drop again. Reviewers: georgegevoian Reviewed By: georgegevoian Differential Revision: https://phab.getgrist.com/D4000
1 year ago
# Sometimes the model repeats the provided classes, remove them.
stmts = [stmt for stmt in stmts if not isinstance(stmt, ast.ClassDef)]
# If the remaining code consists only of a function definition, extract the body.
if len(stmts) == 1 and isinstance(stmts[0], ast.FunctionDef):
func_body_stmts = stmts[0].body
if (
len(func_body_stmts) > 1 and
isinstance(func_body_stmts[0], ast.Expr) and
isinstance(func_body_stmts[0].value, ast.Str)
):
# Skip the docstring.
first_stmt = func_body_stmts[1]
else:
first_stmt = func_body_stmts[0]
result_lines = result.splitlines()[first_stmt.lineno - 1:]
result = "\n".join(result_lines)
result = textwrap.dedent(result)
if imports:
result = imports + "\n" + result
# Now convert `rec.` to `$` and remove redundant `return ` at the end.
atok = asttokens.ASTText(result)
try:
# Constructing ASTText doesn't parse the code, but the .tree property does.
tree = atok.tree
except SyntaxError:
# In case the above extraction somehow messed things up
return ""
replacements = []
for node in ast.walk(tree):
if isinstance(node, ast.Attribute):
start, end = atok.get_text_range(node.value)
end += 1
if result[start:end] == "rec.":
replacements.append((start, end, "$"))
last_stmt = tree.body[-1]
if isinstance(last_stmt, ast.Return):
start, _ = atok.get_text_range(last_stmt)
expected = "return "
end = start + len(expected)
if result[start:end] == expected:
replacements.append((start, end, ""))
result = asttokens.util.replace(result, replacements)
return result.strip()
def evaluate_formula(engine, table_id, col_id, row_id):
grist_formula = engine.docmodel.get_column_rec(table_id, col_id).formula
assert grist_formula
plain_formula = make_formula_body(grist_formula, default_value=None).get_text()
attributes = {}
result = engine.get_formula_value(table_id, col_id, row_id, record_attributes=attributes)
if isinstance(result, objtypes.RaisedException):
name, message = result.encode_args()[:2]
result = "%s: %s" % (name, message)
error = True
else:
result = attribute_recorder.safe_repr(result)
error = False
return dict(
error=error,
formula=plain_formula,
result=result,
attributes=attributes,
)