Mise en forme

quick_deploy_agent.py

@@ -384,3 +384,43 @@ class Resolve(Tool):
         final = ranked[0]; alts = ranked[1:1+max(0,int(topn))]
         exp = f"Choix {final['code']} (score {final['score_final']:.2f}) – votes={final['votes']} – raisons: {', '.join(sorted(set(final['evidences'])))}"
         return {"final": final, "alternatives": alts, "explanation": exp}
+
+
+
+def build_agent(model_id: str | None = None) -> CodeAgent:
+    model_id = model_id or "Qwen/Qwen2.5-Coder-7B-Instruct"  # léger pour tester
+    agent = CodeAgent(
+        tools=[ValidateEANTool(), OFFByEAN(), RegexCOICOP(), OFFtoCOICOP(), SemSim(), Resolve()],
+        model=InferenceClientModel(model_id=model_id),
+        add_base_tools=False,
+        max_steps=6,
+        verbosity_level=2,
+    )
+    return agent
+
+def parse_result(res):
+    if isinstance(res, dict): return res
+    try: return ast.literal_eval(res)
+    except Exception: return {"raw": res}
+
+if __name__ == "__main__":
+    # Remplace par les vraies données si possible - uniquement du test
+    ean = "3256221112345"  # EAN fictif (peut ne pas exister sur OFF)
+    label = "Camembert au lait cru AOP 250g - ALDI"
+
+    agent = build_agent()
+    task = f"""
+    Classe ce produit en COICOP:
+    EAN: {ean}
+    Libellé: {label}
+    Pipeline:
+    1) validate_ean(ean)
+    2) openfoodfacts_product_by_ean(ean)  # si OFF ne trouve pas, on s'appuie sur regex + embeddings
+    3) map_off_to_coicop(product_name, categories_tags, ingredients_text)
+    4) coicop_regex_rules(text=libellé)
+    5) coicop_semantic_similarity(text=libellé, topk=5)
+    6) resolve_coicop_candidates([...], topn=3)
+    Attend un JSON final.
+    """
+    out = agent.run(task)
+    print(parse_result(out))