Etape 3 + refonte visuelle + corrections etape 2

backend/app/graph/nodes.py

@@ -1,4 +1,3 @@
-#nodes.py
 from app.agents.pm_agent import run_pm_agent
 from app.agents.dev_agent import run_dev_agent
 from app.agents.qa_agent import run_qa_agent
@@ -46,21 +45,59 @@ async def retrieval_node(state: WorkflowState, config: RunnableConfig):
     }
 
 async def dev_node(state: WorkflowState):
-    qa_logs = state.get("qa_result", {}).get("logs", "") if state.get("qa_result") else None
+    qa_logs = []
+    qa_result = state.get("qa_result")
+    
+    if qa_result:
+        global_summary = qa_result.get("global_summary")
+        technical_feedback = qa_result.get("technical_feedback", [])
+        
+        if global_summary:
+            qa_logs.append(f"Résumé Global : {global_summary}")
+        
+        if isinstance(technical_feedback, list):
+            qa_logs.extend(technical_feedback)
+        elif technical_feedback:
+            qa_logs.append(technical_feedback)
+
+    generated_code_state = state.get("generated_code") or {}
+    existing_repo_url = generated_code_state.get("repo_url")
+    existing_files = generated_code_state.get("files")
+    
+    generated_code = await run_dev_agent(
+        spec=state["spec"], 
+        qa_feedback=qa_logs if qa_logs else None,
+        repo_url=existing_repo_url,
+        files=existing_files
+    )
     
-    generated_code = await run_dev_agent(state["spec"], qa_feedback=qa_logs)
     return {
         "generated_code": generated_code,
         "status": "code_generated",
     }
 
 async def qa_node(state: WorkflowState):
-    qa_result = await run_qa_agent(state["generated_code"])
+    dev_data = state.get("generated_code", {})
+    project_title = dev_data.get("spec_title", "default_project")
     current_loops = state.get("loop_count", 0)
+
+    qa_eval = await run_qa_agent(
+        project_title=project_title,
+        dev_output=dev_data
+    )
     
-    is_success = True
-    
-    clean_qa_result = {"success": is_success, "raw": qa_result}
+    if hasattr(qa_eval, "model_dump"):
+        clean_qa_result = qa_eval.model_dump()
+    elif isinstance(qa_eval, dict):
+        clean_qa_result = qa_eval
+    else:
+        clean_qa_result = {
+            "is_complete_and_safe": getattr(qa_eval, "is_complete_and_safe", False),
+            "global_summary": getattr(qa_eval, "global_summary", "Erreur d'analyse"),
+            "technical_feedback": getattr(qa_eval, "technical_feedback", [])
+        }
+
+    is_success = clean_qa_result.get("is_complete_and_safe", False)
 
     return {
         "qa_result": clean_qa_result,