Refacto du code coté Python, java et coté angular afin de mieux séparer les responsabilité et d'avoir moins de répétitivité dans le code.

Mise en place de tests unitaires coté Python et Angular
Mise en place de la couverture de test directement dans le workflow : le programme ne build pas si jamais un test échoue
Passage en v0.16.2 en conséquence

brain/tests/test_adapt_campaign.py

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+"""Tests du use case de conseils d'adaptation (app.application.adapt_campaign)."""
+from __future__ import annotations
+
+import pytest
+
+from app.application.adapt_campaign import AdaptCampaignUseCase
+from app.domain.models import ChatMessage, ExtractedDocument, ExtractedPage
+from app.domain.ports import PdfExtractionError
+
+
+class FakeExtractor:
+    def __init__(self, doc: ExtractedDocument) -> None:
+        self._doc = doc
+
+    def extract(self, pdf_bytes: bytes) -> ExtractedDocument:
+        return self._doc
+
+
+class FakeChatLLM:
+    def __init__(self, tokens: list[str]) -> None:
+        self._tokens = tokens
+        self.system_prompt: str | None = None
+        self.messages: list[ChatMessage] | None = None
+
+    async def stream_chat(self, messages, *, system_prompt=None, temperature=None):
+        self.messages = messages
+        self.system_prompt = system_prompt
+        for t in self._tokens:
+            yield t
+
+
+def _doc(text: str) -> ExtractedDocument:
+    return ExtractedDocument(pages=[ExtractedPage(index=0, text=text, used_ocr=False)])
+
+
+async def test_stream_yields_tokens_and_builds_context():
+    llm = FakeChatLLM(["con", "seil"])
+    uc = AdaptCampaignUseCase(llm, FakeExtractor(_doc("contenu du pdf")))
+    out = [t async for t in uc.stream(b"x", "mon brief de campagne",
+                                      [ChatMessage(role="user", content="aide")])]
+    assert out == ["con", "seil"]
+    assert "mon brief de campagne" in llm.system_prompt
+    assert "contenu du pdf" in llm.system_prompt
+
+
+async def test_stream_empty_pdf_text_raises():
+    uc = AdaptCampaignUseCase(FakeChatLLM([]), FakeExtractor(_doc("   ")))
+    with pytest.raises(PdfExtractionError):
+        [t async for t in uc.stream(b"x", "brief", [])]
+
+
+async def test_stream_injects_default_request_when_no_messages():
+    llm = FakeChatLLM(["ok"])
+    uc = AdaptCampaignUseCase(llm, FakeExtractor(_doc("texte du pdf")))
+    _ = [t async for t in uc.stream(b"x", "", [])]
+    assert llm.messages[0].role == "user"
+    assert "campagne" in llm.messages[0].content.lower()
+
+
+def test_fit_pdf_short_text_not_truncated():
+    uc = AdaptCampaignUseCase(None, None, max_input_tokens=10000)
+    text, truncated = uc._fit_pdf_to_budget("court texte", "brief")
+    assert truncated is False
+    assert text == "court texte"
+
+
+def test_fit_pdf_long_text_is_truncated():
+    uc = AdaptCampaignUseCase(None, None, max_input_tokens=2100)
+    long_text = "mot " * 5000
+    text, truncated = uc._fit_pdf_to_budget(long_text, "")
+    assert truncated is True
+    assert len(text) < len(long_text)