manual push

Dockerfile

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+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY app.py .
+EXPOSE 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os, zipfile, urllib.request
+from fastapi import FastAPI
+from pydantic import BaseModel
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings
+from langchain_chroma import Chroma
+from langchain_core.prompts import ChatPromptTemplate
+
+# Dossier inscriptible dans le conteneur du Space
+DOCS_DIR = "/tmp/documents/"
+
+def telecharger_documents():
+    # --- code de téléchargement du notebook, adapté au conteneur ---
+    # ============================================================
+    # TELECHARGEMENT DU DOCUMENT
+    # ============================================================
+    # Telechargement direct (Python pur — compatible Windows/Mac/Colab)
+
+    import os, urllib.request
+
+    os.makedirs(DOCS_DIR, exist_ok=True)
+    dest = os.path.join(DOCS_DIR, "CONVENTION_SYNTEC.pdf")
+
+    if os.path.exists(dest):
+        print(f"Le fichier {dest} existe deja — telechargement ignore.")
+    else:
+        print("Telechargement de CONVENTION_SYNTEC.pdf...")
+        urllib.request.urlretrieve("https://github.com/archiducarmel/SupDeVinci_M1_MachineLearning_DeepLearning/releases/download/datas/CONVENTION_SYNTEC.pdf", dest)
+        print("OK.")
+
+    # Verification
+    size_kb = os.path.getsize(dest) / 1024
+    print(f"\\n✅ {dest} ({size_kb:.0f} Ko) pret dans ./documents/")
+
+telecharger_documents()
+
+# ----- Pipeline RAG (identique au notebook) -----
+all_docs = []
+for f in sorted(os.listdir(DOCS_DIR)):
+    if f.endswith('.pdf'):
+        all_docs.extend(PyPDFLoader(os.path.join(DOCS_DIR, f)).load())
+chunks = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100).split_documents(all_docs)
+
+# La clé NVIDIA est lue dans la variable d'environnement NVIDIA_API_KEY (secret du Space)
+embeddings = NVIDIAEmbeddings(model="nvidia/llama-nemotron-embed-1b-v2", truncate="NONE")
+vector_store = Chroma.from_documents(chunks, embeddings)
+retriever = vector_store.as_retriever(search_kwargs={"k": 3})
+llm = ChatNVIDIA(model="openai/gpt-oss-120b", temperature=0.2, max_completion_tokens=2048)
+
+prompt = ChatPromptTemplate.from_template(
+    "Tu es un assistant RH expert de la convention collective Syntec. "
+    "Réponds à la QUESTION en t'appuyant UNIQUEMENT sur le CONTEXTE ci-dessous.\\n"
+    "Si l'information n'y figure pas, réponds exactement : « Je ne sais pas ».\\n"
+    "Sois concis et cite la source (document et numéro de page).\\n\\n"
+    "CONTEXTE :\\n{context}\\n\\nQUESTION : {input}")
+
+def extract_answer(response):
+    text = (response.content or "").strip()
+    if not text:
+        text = (response.additional_kwargs.get("reasoning_content", "") or "").strip()
+    return text
+
+def format_docs(docs):
+    return "\\n\\n".join(f"[{d.metadata.get('source', '?').split('/')[-1]} — page {d.metadata.get('page')}] {d.page_content}" for d in docs)
+
+generation = prompt | llm | extract_answer
+
+def rag_answer(question):
+    docs = retriever.invoke(question)
+    answer = generation.invoke({"context": format_docs(docs), "input": question})
+    return {"answer": answer, "context": docs}
+
+app = FastAPI(title="Assistant RH — Convention Syntec — API RAG")
+
+class QuestionIn(BaseModel):
+    question: str
+
+@app.get("/")
+def health():
+    return {"status": "ok"}
+
+@app.post("/ask")
+def ask(payload: QuestionIn):
+    result = rag_answer(payload.question)
+    sources = [{"document": d.metadata.get("source", "?").split("/")[-1], "page": d.metadata.get("page")} for d in result["context"]]
+    return {"answer": result["answer"], "sources": sources}

requierment.txt

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+fastapi
+uvicorn[standard]
+langchain
+langchain-community
+langchain-chroma
+langchain-nvidia-ai-endpoints
+langchain-text-splitters
+pypdf