feat(global): add functional mvp
This commit is contained in:
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.env.example
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.env.example
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GOOGLE__API_KEY=<GOOGLE__API_KEY>
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GOOGLE__MODEL=gemini-3-pro-preview
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17
.gitignore
vendored
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17
.gitignore
vendored
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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# Environment files
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.env
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# Development
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.idea
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t
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19
.pre-commit-config.yaml
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.pre-commit-config.yaml
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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.14.2
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hooks:
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- id: ruff-check
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args: [ --fix ]
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- repo: https://github.com/psf/black-pre-commit-mirror
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rev: 25.9.0
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hooks:
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- id: black
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language_version: python3.13
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- repo: https://github.com/pycqa/isort
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rev: 7.0.0
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hooks:
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- id: isort
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name: isort (python)
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language_version: python3.13
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1
.python-version
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1
.python-version
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3.13
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11
README.md
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11
README.md
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# stealth-ai-agent
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AI agent that lives in macOS menubar. Analyzes screen content and solves tasks.
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- Uses Gemini 3 Pro (Preview) by default, can be changed in .env
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- Set your own API key in .env before usage
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- Only compatible with Google models (provided by google-genai)
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- Shortcut: ⇧⌃⌥⌘A for low reasoning
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- Shortcut: ⇧⌃⌥⌘H for high reasoning
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- ⇧⌃⌥⌘ is Raycast's Hyper key
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<sub><sup>MVP. Worst code I've written in the last 5 years. Sorry for ts. But it does what it should.</sup></sub>
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34
pyproject.toml
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34
pyproject.toml
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[project]
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name = "stealth-ai-agent"
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version = "0.1.0"
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description = "AI agent that lives in macOS menubar"
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readme = "README.md"
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authors = [
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{ name = "h", email = "h@kotikot.com" }
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]
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requires-python = ">=3.13"
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dependencies = [
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"pillow>=12.0.0",
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"pydantic-ai-slim[google]>=1.25.0",
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"pydantic-settings>=2.12.0",
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"pyobjc-core>=12.1",
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"pyperclip>=1.11.0",
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"pyscreenshot>=3.1",
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"quickmachotkey>=2025.7.28",
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"rumps>=0.4.0",
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]
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[dependency-groups]
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dev = [
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"rich>=14.2.0",
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]
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[build-system]
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requires = ["uv_build>=0.9.13,<0.10.0"]
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build-backend = "uv_build"
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[tool.uv.build-backend]
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module-name = ["app"]
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[tool.isort]
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profile = "black"
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src/app/__init__.py
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src/app/__init__.py
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from rumps import App
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from app.ui.items import default_items
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def init_app(app_: App):
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import app.hotkeys # noqa
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import app.ui.handlers # noqa
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app_.menu = default_items
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def main():
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from .app import app
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init_app(app)
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app.run()
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src/app/__main__.py
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src/app/__main__.py
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from . import main
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main()
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src/app/app.py
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src/app/app.py
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from rumps import App
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app = App("stealth-ai-agent", title="hi")
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3
src/app/hotkeys/__init__.py
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src/app/hotkeys/__init__.py
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from . import agent
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__all__ = ["agent"]
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54
src/app/hotkeys/agent.py
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src/app/hotkeys/agent.py
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from pydantic_ai import BinaryImage
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from quickmachotkey import mask, quickHotKey
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from quickmachotkey.constants import (
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cmdKey,
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controlKey,
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kVK_ANSI_A,
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kVK_ANSI_H,
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optionKey,
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shiftKey,
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)
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from rumps import Timer
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from app.modules.ai import AgentAnswer, agent, model_high, model_low
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from app.modules.screenshot import take_screenshot
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from app.ui.items import display_text
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from app.ui.status import update_statusbar
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timer = Timer(None, 0)
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def process_task(model):
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timer.stop()
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import app.ui.handlers as handlers
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response = agent.run_sync(
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[BinaryImage(take_screenshot(), media_type="image/png")], model=model
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)
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output: AgentAnswer = response.output
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handlers.long_answer = "\n".join(output.long_answer or [])
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handlers.solution_thinking = output.solution_thinking
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display_text(output.long_answer)
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update_statusbar(output.answer_variant or "?")
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@quickHotKey(
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virtualKey=kVK_ANSI_A,
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modifierMask=mask(cmdKey, controlKey, optionKey, shiftKey),
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)
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def low_handler() -> None:
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update_statusbar("...")
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timer.set_callback(lambda _: process_task(model_low))
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timer.start()
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@quickHotKey(
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virtualKey=kVK_ANSI_H,
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modifierMask=mask(cmdKey, controlKey, optionKey, shiftKey),
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)
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def high_handler() -> None:
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update_statusbar("...")
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timer.set_callback(lambda _: process_task(model_high))
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timer.start()
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1
src/app/modules/__init__.py
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1
src/app/modules/__init__.py
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pass
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src/app/modules/ai/__init__.py
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src/app/modules/ai/__init__.py
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from .agent import agent, model_high, model_low
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from .types import AgentAnswer
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__all__ = ["agent", "AgentAnswer", "model_high", "model_low"]
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60
src/app/modules/ai/agent.py
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src/app/modules/ai/agent.py
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from google.genai.types import ThinkingLevel
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from pydantic_ai import Agent
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from pydantic_ai.models.google import (
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GoogleModel,
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GoogleModelSettings,
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ThinkingConfigDict,
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)
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from pydantic_ai.providers.google import GoogleProvider
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from app.modules.env import env
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from .types import AgentAnswer
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provider = GoogleProvider(api_key=env.google.api_key.get_secret_value())
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model_high = GoogleModel(
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env.google.model,
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provider=provider,
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settings=GoogleModelSettings(
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google_thinking_config=ThinkingConfigDict(thinking_level=ThinkingLevel.HIGH)
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),
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)
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model_low = GoogleModel(
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env.google.model,
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provider=provider,
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settings=GoogleModelSettings(
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google_thinking_config=ThinkingConfigDict(thinking_level=ThinkingLevel.LOW)
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),
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)
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agent = Agent(
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model_high,
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output_type=AgentAnswer,
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instructions="""\
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You are a highly advanced, stealthy autonomous assistant specialized in solving academic tests, coding challenges,
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and logic puzzles directly from visual inputs.
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**Your Goal:**
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Analyze the provided screenshot, extract the question and context, and provide the correct solution
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immediately in the specified format.
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**Process:**
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1. **Visual Analysis:** Perform OCR to read the text. Identify code blocks, diagrams, or multiple-choice options.
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2. **Reasoning:** In the `solution_thinking` field, briefly analyze the problem. Verify edge cases.
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If it's a coding problem, plan the syntax. If it's a test, rule out wrong answers.
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3. **Decision:**
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* **Case A: Multiple Choice.** If the question has options (A, B, C, D or 1, 2, 3, 4),
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identify the SINGLE correct option. Place ONLY the letter or number (e.g., "A", "2", "C") in `answer_variant`.
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Leave `long_answer` null. ONLY if the question implies multiple correct answers, write them separated by commas.
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E.g., "A,C".
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* **Case B: Open Answer / Code.** If no options exist, write the solution. Place it in `long_answer`.
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* **Case C: Complex/Mixed.** If an explanation is absolutely required alongside a letter choice,
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you may fill both, but prioritize `answer_variant`.
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**Constraints for `long_answer`:**
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* It must be a list of strings.
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* **CRITICAL:** Each string must be **under 100 characters**.
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* Split long code lines or sentences intelligently (e.g., at variable assignments or punctuation).
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* Do not use markdown code blocks (```) inside the strings, just raw text/code.
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* Do not add conversational filler like "Here is the code:". Just the solution.
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""",
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)
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src/app/modules/ai/types.py
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src/app/modules/ai/types.py
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from pydantic import BaseModel, Field
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class AgentAnswer(BaseModel):
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solution_thinking: str = Field(
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...,
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description="Brief chain-of-thought reasoning to ensure accuracy. Invisible to the user initially.",
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)
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long_answer: list[str] | None = Field(
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None,
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description="The solution text or code split into lines. Each line MUST be < 100 chars. "
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"Used for open-ended questions.",
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)
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answer_variant: str | None = Field(
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None,
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description="The specific option letter (A, B, C, D) or number if the screenshot contains a "
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"multiple-choice question.",
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)
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src/app/modules/env.py
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src/app/modules/env.py
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from pydantic import SecretStr
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class GoogleSettings(BaseSettings):
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api_key: SecretStr
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model: str = "gemini-3-pro-preview"
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class Settings(BaseSettings):
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google: GoogleSettings
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model_config = SettingsConfigDict(
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case_sensitive=False,
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env_file=".env",
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env_nested_delimiter="__",
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extra="ignore",
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)
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env = Settings() # noqa
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src/app/modules/screenshot.py
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src/app/modules/screenshot.py
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from io import BytesIO
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import pyscreenshot
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from PIL.Image import Image
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def take_screenshot() -> bytes:
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im: Image = pyscreenshot.grab(childprocess=False)
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io = BytesIO()
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im.save(io, format="PNG")
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return io.getvalue()
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1
src/app/ui/__init__.py
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src/app/ui/__init__.py
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pass
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src/app/ui/handlers.py
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src/app/ui/handlers.py
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import pyperclip
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solution_thinking = ""
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long_answer = ""
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def copy_answer(_):
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pyperclip.copy(long_answer)
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|
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|
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def copy_solution(_):
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pyperclip.copy(solution_thinking)
|
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src/app/ui/items.py
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src/app/ui/items.py
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from rumps import MenuItem
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|
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from app.app import app
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from .handlers import copy_answer, copy_solution
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default_items = [
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MenuItem("Copy answer", key="c", callback=copy_answer),
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MenuItem("Copy solution", key="C", callback=copy_solution),
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]
|
||||
|
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|
||||
def display_text(strings: list[str]):
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||||
for item in app.menu.items(): # noqa
|
||||
del app.menu[item[0]]
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||||
app.menu = [
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||||
MenuItem(i) for i in default_items + (strings or ["No detailed answer"])
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||||
]
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src/app/ui/status.py
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src/app/ui/status.py
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from app.app import app
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||||
|
||||
|
||||
def update_statusbar(letter: str):
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||||
app.title = letter
|
||||
728
uv.lock
generated
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728
uv.lock
generated
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@@ -0,0 +1,728 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
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||||
wheels = [
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
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]
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|
||||
[[package]]
|
||||
name = "cachetools"
|
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source = { registry = "https://pypi.org/simple" }
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]
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||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.11.12"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" }
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||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.4.4"
|
||||
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||||
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Reference in New Issue
Block a user