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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY ./app ./app
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

app/controllers/lm_controller.py

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+from fastapi import APIRouter, HTTPException
+from models.lm_generator import AiAgentLMGenerator
+from models.docx_generator import DocxGenerator
+from pydantic import BaseModel
+
+router = APIRouter()
+
+class LMRequest(BaseModel):
+    name: str
+    job: str
+    job_description: str
+    email: str
+    phone: str
+    competences: list[str]
+    github_url: str = None
+    linkedin_url: str = None
+
+class DocxRequest(BaseModel):
+    lm_content: str
+
+@router.post("/generatelmcontent")
+def generate_lm_content(request: LMRequest):
+    try:
+        generator = AiAgentLMGenerator()
+        user_data = request.dict()
+        content = generator.generate(user_data)
+        return {"lm_content": content}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/generateDocx")
+def generate_docx(request: DocxRequest):
+    try:
+        docx_gen = DocxGenerator()
+        docx_path = docx_gen.generate_docx(request.lm_content)
+        return {"docx_path": docx_path}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app/main.py

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+from fastapi import FastAPI
+from controllers.lm_controller import router as lm_router
+
+app = FastAPI()
+
+app.include_router(lm_router)

app/models/docx_generator.py

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+from docx import Document
+from docx.shared import Pt
+from docx.enum.text import WD_ALIGN_PARAGRAPH
+import os
+
+class DocxGenerator:
+    def generate_docx(self, lm_content: str) -> str:
+        doc = Document()
+
+        # Ajouter le contenu de la lettre
+        lines = lm_content.split("\n")
+        for i, line in enumerate(lines):
+            p = doc.add_paragraph()
+            run = p.add_run(line)
+            if i == 0:
+                run.bold = True
+
+        output_path = "generated_lm.docx"
+        doc.save(output_path)
+        with open(output_path, "rb") as f:
+            blob = f.read()
+        return blob

app/models/lm_generator.py

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+from langchain_google_genai import ChatGoogleGenerativeAI
+
+class AiAgentLMGenerator:
+    def __init__(self):
+        self.llm = ChatGoogleGenerativeAI(
+            google_api_key="AIzaSyAmpT2kjqFcz7HZyiFeh6dBOu-zx_MRxIA",
+            model="models/gemini-2.5-flash"
+        )
+
+    def generate(self, user_data: dict) -> str:
+        name = user_data.get("name", "Candidat")
+        job = user_data.get("job", "poste")
+        prompt = (
+            f"Rédige une lettre de motivation professionnelle pour postuler au poste de {job}. "
+            f"Le candidat s'appelle {name}. La lettre doit être formelle et convaincante."
+            f"Veuillez saisir l'objet et le contenu de la lettre.\n"
+            f"Pas d'autre reponse que la lettre."
+        )
+        response = self.llm.invoke(prompt)
+        return response.content if hasattr(response, 'content') else str(response)

app/models/pdf_generator.py

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+from fpdf import FPDF
+import os
+
+class PdfGenerator:
+    def generate_pdf(self, lm_content: str) -> str:
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", size=12)
+        for line in lm_content.split("\n"):
+            pdf.cell(200, 10, txt=line, ln=1)
+        output_path = "generated_lm.pdf"
+        pdf.output(output_path)
+        return os.path.abspath(output_path)

requirements.txt

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+fastapi
+uvicorn
+python-docx
+langchain
+langchain-google-genai
+pydantic