Merge pull request #24 from Significant-Gravitas/swiftyos/agpt-276

added formatting workflow

.github/workflows/formatting.yml

@@ -0,0 +1,34 @@
+name: Python Code Quality
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  code-quality:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.11
+
+    - name: Install Dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install isort ruff
+
+    - name: Run isort
+      run: isort .
+
+    - name: Run ruff formatter
+      run: ruff format .
+
+    - uses: stefanzweifel/git-auto-commit-action@v4
+      with:
+        commit_message: 'style fixes by ruff'

.ruff.toml

@@ -0,0 +1,4 @@
+
+line-length = 88
+target-version = "py39"
+select = ["E", "W"]

codex/__main__.py

@@ -77,12 +77,12 @@ def process_app(app_name: str) -> None:
 @cli.command()
 def serve() -> None:
     import uvicorn
+
     from codex.app import app
 
     uvicorn.run(app, host="0.0.0.0", port=os.environ.get("PORT", 8000))
 
 
-
 if __name__ == "__main__":
     setup_logging(local=os.environ.get("ENV", "CLOUD").lower() == "local")
     cli()

codex/prompts/claude/requirements/AskFunction.py

@@ -1,6 +1,5 @@
 from typing import Callable
 
-
 ASK_FUNCTION_AUTO = """
 Human: You are a professional answer. Your answers are always well thought out and thorough, but say no more than they need to answer the question. When you answer the question, you provide your thoughts first, set off by <thoughts> tags.
 

codex/prompts/claude/requirements/ClarificationsIntoProduct.py

@@ -6,9 +6,9 @@
 
 Assistant: Okay, ask away.
 
-Human: You are an expert product owner. 
+Human: You are an expert product owner.
 
-Given the basic project description and the clarification, Q&A write a detailed product description. 
+Given the basic project description and the clarification, Q&A write a detailed product description.
 The project description should be at least 750 words. Be clear and exhaustive such that it is easy to determine the functional and non-functional requirements of the product.
 You should carefully think about it step by step and share those answers in a block set off by  <think>thoughts here</think>.
 

codex/prompts/claude/requirements/EndpointGeneration.py

@@ -77,7 +77,7 @@ class ResponseModel(BaseModel):
 
 
 Example for `GET /user_id_by_discord_id/{id}`:
-<think>I'll need to leverage the context provided to ensure the UserDTO object makes sense. I'll also provide any models needed for my return types if they aren't based on the db model from prisma</think> 
+<think>I'll need to leverage the context provided to ensure the UserDTO object makes sense. I'll also provide any models needed for my return types if they aren't based on the db model from prisma</think>
 <db_models_needed>
   [User]
 </db_models_needed>

codex/prompts/claude/requirements/ModuleRefinement.py

@@ -28,7 +28,7 @@
                 </endpoint>
                 ... repeat for all related endpoints in a group ie /user GET PUT
             </endoint_group>
-            ... repeat endpoints and groups until ALL requirements are satisfied 
+            ... repeat endpoints and groups until ALL requirements are satisfied
         </endpoints>
     </module>
     ... repeat for each module

codex/prompts/claude/requirements/NestJSDocs.py

@@ -1818,7 +1818,7 @@ class SequelizeConfigService implements SequelizeOptionsFactory {
 
 """
 
-NEST_JS_CLI_RULES = """
+NEST_JS_CLI_RULES = r"""
 ### CLI command reference
 
 #### nest new

codex/prompts/claude/requirements/ProductIntoRequirement.py

@@ -14,7 +14,7 @@
 > Is monetization implemented through authorization? [Yes, No, N/A]
 > Do we need Authentication?  [Yes, No]
 > Do we need authorization? [Yes, No, N/A]
-> What authorization roles do we need? For this question, answer an array of One Word Roles such as [UserType1, UserType2, UserType3, N/A]. 
+> What authorization roles do we need? For this question, answer an array of One Word Roles such as [UserType1, UserType2, UserType3, N/A].
 """
 
 FEATURE_BASELINE_CHECKS = """
@@ -31,12 +31,12 @@
 Assistant: Okay, ask away.
 
 Human: You are an expert product manager who works to help us ensure our MVPs are reasonable.
-You should carefully think about it step by step, and share those answers in a block set off by <think>thoughts here</think>. 
-Each question will be in the following form: >Question? [ValidAnswer1, ValidAnswer2, ...]. 
-You should answer with only one of the valid options next to each question. 
+You should carefully think about it step by step, and share those answers in a block set off by <think>thoughts here</think>.
+Each question will be in the following form: >Question? [ValidAnswer1, ValidAnswer2, ...].
+You should answer with only one of the valid options next to each question.
 Set the answer off by
 <answer>
-<wrapper> 
+<wrapper>
 <question>question</question>
 <think>thoughts on this question. Don't answer the question here, just put the raw thoughts</think>
 <answer>valid answer</answer>

codex/prompts/claude/requirements/RequirementIntoModule.py

@@ -1,11 +1,10 @@
 from codex.prompts.claude.requirements.NestJSDocs import (
     NEST_JS_CRUD_GEN,
+    NEST_JS_FIRST_STEPS,
     NEST_JS_MODULES,
     NEST_JS_SQL,
-    NEST_JS_FIRST_STEPS,
 )
 
-
 # This is a two part prompt!!!
 REQUIREMENTS_INTO_MODULES = """
 Human: Here is some background that is useful:
@@ -84,7 +83,7 @@
 </think_anti>
 <answer> <module> <name> name of the module </name> <description> a description of the module </description> <command> command to generate the module in the following format `nest g module cats` </command> </module> ... repeat for other modules </answer> <concluding_think> thoughts </concluding_think>
 
-Assistant: 
+Assistant:
 
 """
 # becomes modules

codex/prompts/claude/requirements/TaskIntoClarifcations.py

@@ -14,15 +14,15 @@
 
 Only reply with the specified tags.
 
-Assistant: 
+Assistant:
 """
 
 FRONTEND_QUESTION = "Do we need a front end?"
 
 MORE_INFO_BASE_CLARIFICATIONS_FRONTEND = """
 Human: You are an expert product manager who works to help us ensure our projects are reasonable. You should carefully think about it step by step, and share those answers in a block set off by <think>thoughts here</think>. You should answer with only a yes or no. Set the answer off by <answer>Yes/No</answer>
 
-Do we need a front end for this: 
+Do we need a front end for this:
 "{project_description}"
 
 Assistant: <think>
@@ -41,7 +41,7 @@
 
 Assistant: Okay, ask away.
 
-Human: You are an expert product manager who works to help us ensure our projects are reasonable. You should carefully think about it step by step, and share those answers in a block set off by <think>thoughts here</think>. You should answer with only an expected user persona or personas. Set the answer off by <answer>reply here</answer>. 
+Human: You are an expert product manager who works to help us ensure our projects are reasonable. You should carefully think about it step by step, and share those answers in a block set off by <think>thoughts here</think>. You should answer with only an expected user persona or personas. Set the answer off by <answer>reply here</answer>.
 
 Who is the expected user of this: "{project_description}"
 
@@ -57,9 +57,9 @@
 
 Assistant: Okay, ask away.
 
-Human: You are an expert product manager who works to help us ensure our projects are reasonable. You should carefully think about it step by step, and share those answers in a block set off by  <think>thoughts here</think>. You should answer with only an expected skill level of the user persona or personas. Set the answer off by <answer>reply here</answer>. 
+Human: You are an expert product manager who works to help us ensure our projects are reasonable. You should carefully think about it step by step, and share those answers in a block set off by  <think>thoughts here</think>. You should answer with only an expected skill level of the user persona or personas. Set the answer off by <answer>reply here</answer>.
 
-What is the skill level of the expected user of this: 
+What is the skill level of the expected user of this:
 "{project_description}"
 
 Assistant: <think>
@@ -72,12 +72,12 @@
 
 Assistant: Okay, ask away.
 
-Human: 
+Human:
 Answer as an expert product owner.
 For the following project description, ask a series of at least 10 questions that will clarify what is being requested.
 Think carefully, ensuring questions do not overlap, and all ambiguities are covered. Then, generate your best guess at the answer, basing it on your knowledge and best practices. You should carefully think about it step by step and share those thoughts in a block set off by  <think>thoughts here</think>. For each reply, you should set the answer off by <answer>reply here</answer>. Pick questions that directly affect the direction of the product rather than marketing, budgets, or timeframes, stakeholders etc as those are already answered in the Q&A.
 
-An example of that would be 
+An example of that would be
 <think>your thoughts on the overall process, thinking step by step</think>
 <answer>
 <wrapper>

codex/requirements/agent.py

@@ -1,11 +1,21 @@
-from asyncio import run
 import logging
+from asyncio import run
 
 import openai
 import prisma
 from prisma.enums import AccessLevel
 
 from codex.common.ai_block import Indentifiers
+from codex.prompts.claude.requirements.AskFunction import *
+from codex.prompts.claude.requirements.ClarificationsIntoProduct import *
+from codex.prompts.claude.requirements.EndpointGeneration import *
+from codex.prompts.claude.requirements.ModuleIntoDatabase import *
+from codex.prompts.claude.requirements.ModuleRefinement import *
+from codex.prompts.claude.requirements.ProductIntoRequirement import *
+from codex.prompts.claude.requirements.QAFormat import *
+from codex.prompts.claude.requirements.RequirementIntoModule import *
+from codex.prompts.claude.requirements.SearchFunction import *
+from codex.prompts.claude.requirements.TaskIntoClarifcations import *
 from codex.requirements import flatten_endpoints
 from codex.requirements.ai_clarify import (
     FrontendClarificationBlock,
@@ -14,6 +24,10 @@
     UserSkillClarificationBlock,
 )
 from codex.requirements.ai_feature import FeatureGenerationBlock
+from codex.requirements.build_requirements_refinement_object import (
+    RequirementsRefined,
+    convert_requirements,
+)
 from codex.requirements.complete import complete_and_parse, complete_anth
 from codex.requirements.database import create_spec
 from codex.requirements.gather_task_info import gather_task_info_loop
@@ -44,26 +58,8 @@
     ResponseModel,
     StateObj,
 )
-
-from codex.prompts.claude.requirements.ClarificationsIntoProduct import *
-from codex.prompts.claude.requirements.ProductIntoRequirement import *
-from codex.prompts.claude.requirements.RequirementIntoModule import *
-from codex.prompts.claude.requirements.TaskIntoClarifcations import *
-from codex.prompts.claude.requirements.ModuleRefinement import *
-from codex.prompts.claude.requirements.ModuleIntoDatabase import *
-from codex.prompts.claude.requirements.EndpointGeneration import *
-from codex.prompts.claude.requirements.AskFunction import *
-from codex.prompts.claude.requirements.QAFormat import *
-from codex.prompts.claude.requirements.SearchFunction import *
-from codex.requirements.build_requirements_refinement_object import (
-    convert_requirements,
-    RequirementsRefined,
-)
 from codex.requirements.parser import parse
-from codex.requirements.unwrap_schemas import (
-    convert_endpoint,
-    unwrap_db_schema,
-)
+from codex.requirements.unwrap_schemas import convert_endpoint, unwrap_db_schema
 
 logger = logging.getLogger(__name__)
 
@@ -421,11 +417,11 @@ async def populate_database_specs():
 
     oai = openai.OpenAI()
 
-    task = """The Tutor App is an app designed for tutors to manage their clients, schedules, and invoices. 
+    task = """The Tutor App is an app designed for tutors to manage their clients, schedules, and invoices.
 
 It must support both the client and tutor scheduling, rescheduling and canceling appointments, and sending invoices after the appointment has passed.
 
-Clients can sign up with OAuth2 or with traditional sign-in authentication. If they sign up with traditional authentication, it must be safe and secure. There will need to be password reset and login capabilities. 
+Clients can sign up with OAuth2 or with traditional sign-in authentication. If they sign up with traditional authentication, it must be safe and secure. There will need to be password reset and login capabilities.
 
 There will need to be authorization for identifying clients vs the tutor.
 

codex/requirements/ai_clarify.py

@@ -3,8 +3,12 @@
 
 from pydantic import BaseModel
 
-from codex.common.ai_block import Indentifiers
-from codex.common.ai_block import AIBlock, ValidatedResponse, ValidationError
+from codex.common.ai_block import (
+    AIBlock,
+    Indentifiers,
+    ValidatedResponse,
+    ValidationError,
+)
 from codex.requirements.model import Clarification, QandA, QandAResponses
 
 

codex/requirements/ai_feature.py

@@ -3,8 +3,12 @@
 
 from pydantic import BaseModel
 
-from codex.common.ai_block import Indentifiers
-from codex.common.ai_block import AIBlock, ValidatedResponse, ValidationError
+from codex.common.ai_block import (
+    AIBlock,
+    Indentifiers,
+    ValidatedResponse,
+    ValidationError,
+)
 from codex.requirements.model import (
     Clarification,
     FeaturesSuperObject,

codex/requirements/build_requirements_refinement_object.py

@@ -1,11 +1,10 @@
 import enum
 from typing import List
+
+from fuzzywuzzy import fuzz, process
 from pydantic import BaseModel
-from pydantic import BaseModel
-import enum
+
 from codex.requirements.model import ReplyEnum, RequirementsRefined
-from fuzzywuzzy import fuzz
-from fuzzywuzzy import process
 
 
 def convert_requirements(requirements_qa) -> RequirementsRefined:

codex/requirements/choose_tool.py

@@ -1,5 +1,6 @@
 # Tools
 from typing import Callable, Optional
+
 from codex.requirements.complete import complete_anth
 
 

codex/requirements/complete.py

@@ -1,7 +1,8 @@
 # Anthropic Completion
 from enum import Enum
 from typing import Type, TypeVar
-from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+
+from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic
 from anthropic.types import Completion as ACompletion
 from pydantic import BaseModel, ValidationError
 

codex/requirements/flatten_endpoints.py

@@ -1,11 +1,6 @@
-from fuzzywuzzy import fuzz
-from fuzzywuzzy import process
+from fuzzywuzzy import fuzz, process
 
-from codex.requirements.model import (
-    Endpoint,
-    EndpointGroupWrapper,
-    EndpointWrapper,
-)
+from codex.requirements.model import Endpoint, EndpointGroupWrapper, EndpointWrapper
 
 
 def flatten_endpoints(

codex/requirements/gather_task_info.py

@@ -1,9 +1,9 @@
 # Task Breakdown Micro Agent
 from typing import Callable, Optional
+
 from anthropic import AI_PROMPT, HUMAN_PROMPT
-from codex.prompts.claude.requirements.TaskIntoClarifcations import (
-    TASK_INTO_MORE_INFO,
-)
+
+from codex.prompts.claude.requirements.TaskIntoClarifcations import TASK_INTO_MORE_INFO
 from codex.requirements.choose_tool import choose_tool
 from codex.requirements.complete import complete_anth
 
@@ -17,7 +17,7 @@ def gather_task_info_loop(
         print(x)
         response = complete_anth(running_message)
         print(response)
-        if not "finished:" in response.strip():
+        if "finished:" not in response.strip():
             next_message = choose_tool(raw_prompt=response, ask_callback=ask_callback)
             print(next_message)
             running_message += response + HUMAN_PROMPT + next_message + AI_PROMPT

codex/requirements/matching.py

@@ -1,4 +1,4 @@
-from fuzzywuzzy import process, fuzz
+from fuzzywuzzy import fuzz, process
 
 
 def find_best_match(target: str, choices: list[str], threshold: int = 80):

codex/requirements/model.py

@@ -1,5 +1,5 @@
-from dataclasses import dataclass
 import enum
+from dataclasses import dataclass
 from typing import List, Literal, Optional
 
 from prisma.enums import AccessLevel

codex/requirements/parser.py

@@ -1,6 +1,7 @@
 from typing import Type, TypeVar
-from gravitasml.token import tokenize, Token
+
 from gravitasml.parser import Parser
+from gravitasml.token import Token, tokenize
 from pydantic import BaseModel
 
 T = TypeVar("T", bound=BaseModel)