Ed_
d1ce0eaaeb
- Add cost tracking with new cost_tracker.py module - Enhance Track Proposal modal with editable titles and goals - Add Conductor Setup summary and New Track creation form to MMA Dashboard - Implement Task DAG editing (add/delete tickets) and track-scoped discussion - Add visual polish: color-coded statuses, tinted progress bars, and node indicators - Support live worker streaming from AI providers to GUI panels - Fix numerous integration test regressions and stabilize headless service
105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
import pytest
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import asyncio
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from unittest.mock import patch, MagicMock
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from gui_2 import App
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import events
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@pytest.fixture
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def app_instance():
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with (
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patch('gui_2.load_config', return_value={'ai': {}, 'projects': {}}),
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patch('gui_2.save_config'),
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patch('gui_2.project_manager'),
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patch('gui_2.session_logger'),
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patch('gui_2.immapp.run'),
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patch.object(App, '_load_active_project'),
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patch.object(App, '_fetch_models'),
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patch.object(App, '_load_fonts'),
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patch.object(App, '_post_init')
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):
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app = App()
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yield app
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@pytest.mark.asyncio
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async def test_mma_stream_event_routing(app_instance: App):
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"""Verifies that 'mma_stream' events from AsyncEventQueue reach mma_streams."""
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# 1. Mock received chunks from a Tier 3 worker
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stream_id = "Tier 3 (Worker): T-001"
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chunks = ["Thinking... ", "I will ", "list files."]
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for chunk in chunks:
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# Simulate receiving an 'mma_stream' event in the background asyncio worker
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payload = {"stream_id": stream_id, "text": chunk}
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# We manually trigger the logic inside _process_event_queue for this test
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# to avoid dealing with the background thread's lifecycle.
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with app_instance._pending_gui_tasks_lock:
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app_instance._pending_gui_tasks.append({
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"action": "mma_stream_append",
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"payload": payload
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})
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# 2. Simulate GUI frame processing
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app_instance._process_pending_gui_tasks()
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# 3. Verify final state
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expected_text = "".join(chunks)
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assert app_instance.mma_streams.get(stream_id) == expected_text
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@pytest.mark.asyncio
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async def test_mma_stream_multiple_workers(app_instance: App):
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"""Verifies that streaming works for multiple concurrent workers."""
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s1 = "Tier 3 (Worker): T-001"
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s2 = "Tier 3 (Worker): T-002"
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# Interleaved chunks
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events_to_simulate = [
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(s1, "T1 start. "),
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(s2, "T2 start. "),
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(s1, "T1 middle. "),
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(s2, "T2 middle. "),
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(s1, "T1 end."),
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(s2, "T2 end.")
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]
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for sid, txt in events_to_simulate:
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with app_instance._pending_gui_tasks_lock:
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app_instance._pending_gui_tasks.append({
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"action": "mma_stream_append",
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"payload": {"stream_id": sid, "text": txt}
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})
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app_instance._process_pending_gui_tasks()
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assert app_instance.mma_streams[s1] == "T1 start. T1 middle. T1 end."
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assert app_instance.mma_streams[s2] == "T2 start. T2 middle. T2 end."
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def test_handle_ai_response_resets_stream(app_instance: App):
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"""Verifies that the final handle_ai_response (status=done) replaces/finalizes the stream."""
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stream_id = "Tier 3 (Worker): T-001"
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# Part 1: Some streaming progress
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with app_instance._pending_gui_tasks_lock:
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app_instance._pending_gui_tasks.append({
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"action": "mma_stream_append",
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"payload": {"stream_id": stream_id, "text": "Partially streamed..."}
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})
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app_instance._process_pending_gui_tasks()
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assert app_instance.mma_streams[stream_id] == "Partially streamed..."
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# Part 2: Final response arrives (full text)
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with app_instance._pending_gui_tasks_lock:
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app_instance._pending_gui_tasks.append({
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"action": "handle_ai_response",
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"payload": {
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"stream_id": stream_id,
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"text": "Final complete response.",
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"status": "done"
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}
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})
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app_instance._process_pending_gui_tasks()
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# In our current implementation, handle_ai_response OVERWRITES.
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# This is good because it ensures we have the exact final text from the model
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# (sometimes streaming chunks don't perfectly match final text if there are
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# tool calls or specific SDK behaviors).
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assert app_instance.mma_streams[stream_id] == "Final complete response."
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