ydy0615
2fdc996af9
Added a new `.coveragerc` file configuring coverage thresholds and exclusions. Included `pytest.ini` to enable coverage reporting for multiple backend modules (`main`, `llm`, `prompt`, `geoip`, `tts_asr`) with a 90 % fail‑under requirement and detailed HTML output. Implemented a suite of unit tests: * `test_geoip.py` – validates geo‑location lookup logic. * `test_llm_extended.py` – tests LLm response extraction and Ollama interactions. * `test_main_endpoints.py` – covers API endpoints for completions, OCR, and TTS. * `test_prompt_extended.py` – verifies language sanitization, timestamp generation, and prompt building. * `test_tts_asr_coverage.py` – checks device detection, cache clearing, and model loading under various environment configurations. * `test_tts_asr_extended.py` – further tests TTS/ASR device selection and time‑outs. Updated `backend/requirements.txt` to use newer, compatible packages, removed obsolete testing dependencies, and added `qwen-tts`. Modified `backend/tts_asr.py` to work with the new `Qwen3TTSModel`, simplified imports, and adjusted device mapping logic. Additionally, frontend changes added a new `TreeNodeItem` component, updated Markdown rendering, added TTS instruction fields, and reworked context menu handling. No breaking changes were introduced.
212 lines
6.4 KiB
Python
212 lines
6.4 KiB
Python
import asyncio
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import importlib
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import sys
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from pathlib import Path
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import pytest
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BACKEND_DIR = Path(__file__).resolve().parents[1]
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if str(BACKEND_DIR) not in sys.path:
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sys.path.insert(0, str(BACKEND_DIR))
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try:
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llm = importlib.import_module("llm")
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except ModuleNotFoundError:
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pytest.skip("llm module dependencies are not available", allow_module_level=True)
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def test_extract_message_with_object_message_content_and_thinking():
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class Msg:
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def __init__(self, content, thinking):
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self.content = content
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self.thinking = thinking
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class Resp:
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def __init__(self, message):
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self.message = message
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resp = Resp(Msg("hello world", "thinking about it"))
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content, thinking = llm._extract_message(resp)
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assert content == "hello world"
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assert thinking == "thinking about it"
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def test_extract_message_with_object_message_empty_content():
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class Msg:
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def __init__(self, content, thinking):
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self.content = content
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self.thinking = thinking
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class Resp:
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def __init__(self, message):
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self.message = message
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resp = Resp(Msg("", None))
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_with_dict_message():
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resp = {"message": {"content": "ok", "thinking": "calc"}}
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content, thinking = llm._extract_message(resp)
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assert content == "ok"
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assert thinking == "calc"
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def test_extract_message_dict_no_message_key():
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resp = {"not_message": {"content": "irrelevant"}}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_dict_message_content_none_and_thinking_none():
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resp = {"message": {"content": None, "thinking": None}}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_dict_message_thinking_none():
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resp = {"message": {"content": "val", "thinking": None}}
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content, thinking = llm._extract_message(resp)
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assert content == "val"
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assert thinking == ""
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def test_extract_message_empty_dict():
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resp = {}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_call_ollama_no_system_message(monkeypatch):
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captured = {}
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async def fake_chat(**kwargs):
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captured["messages"] = kwargs.get("messages", [])
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return {"message": {"content": "ok", "thinking": ""}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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result = asyncio.run(
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llm.call_ollama("user prompt body", system_prompt=None, tag="no-system", temperature=0.1)
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)
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assert result["content"] == "ok"
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assert len(captured["messages"]) == 1
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assert captured["messages"][0]["role"] == "user"
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assert captured["messages"][0]["content"] == "user prompt body"
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def test_call_ollama_whitespace_system_message(monkeypatch):
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captured = {}
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async def fake_chat(**kwargs):
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captured["messages"] = kwargs.get("messages", [])
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return {"message": {"content": "ok", "thinking": ""}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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result = asyncio.run(
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llm.call_ollama("user prompt", system_prompt=" ", tag="whitespace-system", temperature=0.1)
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)
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assert result["content"] == "ok"
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assert len(captured["messages"]) == 1
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assert captured["messages"][0]["role"] == "user"
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def test_call_ollama_thinking_in_kwargs(monkeypatch):
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captured = {}
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async def fake_chat(**kwargs):
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captured.update(kwargs)
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return {"message": {"content": "ok", "thinking": "boom"}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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res = asyncio.run(
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llm.call_ollama("prompt", thinking="boom", tag="think-flag", temperature=0.7)
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)
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assert res["content"] == "ok" and res["think"] == "boom"
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assert captured.get("think") == "boom"
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def test_call_ollama_cancelled_reraises(monkeypatch):
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async def fake_chat(**kwargs):
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raise asyncio.CancelledError
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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with pytest.raises(asyncio.CancelledError):
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asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="cancel", temperature=0.7)
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)
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def test_call_ollama_chat_raises_rethrows(monkeypatch):
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async def fake_chat(**kwargs):
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raise ValueError("boom")
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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with pytest.raises(ValueError):
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asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="exception", temperature=0.7)
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)
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def test_call_ollama_returns_content_and_think_from_response(monkeypatch):
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async def fake_chat(**kwargs):
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return {"message": {"content": "final", "thinking": "process"}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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res = asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="return", temperature=0.7)
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)
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assert res["content"] == "final" and res["think"] == "process"
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def test_call_vlm_ocr_passes_image_and_prompt(monkeypatch):
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image_bytes = b"image-bytes"
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called = {}
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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async def fake_chat(**kwargs):
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called["kwargs"] = kwargs
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return {"message": {"content": "ocr result", "thinking": ""}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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result = asyncio.run(llm.call_vlm_ocr(image_bytes, language="auto"))
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messages = called["kwargs"].get("messages", [])
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assert messages[0]["role"] == "user"
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assert messages[0]["content"] == "OCR PROMPT"
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assert messages[0]["images"] == [image_bytes]
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assert result == "ocr result"
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def test_call_vlm_ocr_chat_raises_rethrows(monkeypatch):
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image_bytes = b"image-bytes"
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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async def fake_chat(**kwargs):
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raise RuntimeError("ocr fail")
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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with pytest.raises(RuntimeError):
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asyncio.run(llm.call_vlm_ocr(image_bytes))
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def test_call_vlm_ocr_returns_content_from_response(monkeypatch):
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image_bytes = b"img"
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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async def fake_chat(**kwargs):
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return {"message": {"content": "ocr text", "thinking": ""}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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content = asyncio.run(llm.call_vlm_ocr(image_bytes))
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assert content == "ocr text"
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