update app.py to use refactored files

app.py

@@ -6,8 +6,8 @@ from dotenv import load_dotenv
 from fastapi import FastAPI, Request, HTTPException, Header, status
 from pydantic import BaseModel, Field
 
-from langgraph_pr_review_bot import PRReviewState,code_retriever_node, code_reviewer_node, feedback_formatter_node, post_code_review_node, update_review_body_based_on_human_input_node
-from langgraph_pr_review_bot import google_api_key,git_hub_token
+from src.langgraph_logic.state import PRReviewState
+from src.langgraph_logic.graph import create_graph
 from langgraph.graph import StateGraph, END
 import sqlite3 # Import sqlite3 for direct connection if needed for older versions
 from langgraph.checkpoint.sqlite import SqliteSaver
@@ -39,30 +39,7 @@ print(f"Using require_human_approval key: {require_human_approval_from_env}")
 
 
 # --- Build the Graph ---
-workflow = StateGraph(PRReviewState)
-
-# Add your core nodes
-workflow.add_node("code_retriever_node", code_retriever_node)
-workflow.add_node("code_reviewer_node", code_reviewer_node)
-workflow.add_node("feedback_formatter_node", feedback_formatter_node)
-
-# --- New HIL-related nodes ---
-workflow.add_node("post_code_review_node", post_code_review_node) # Post as pending
-workflow.add_node("update_review_body_based_on_human_input_node", update_review_body_based_on_human_input_node)
-
-
-
-# Define entry point
-workflow.set_entry_point("code_retriever_node")
-
-# Define edges
-workflow.add_edge("code_retriever_node", "code_reviewer_node")
-workflow.add_edge("code_reviewer_node", "feedback_formatter_node")
-
-# After parsing, always post a pending review
-workflow.add_edge("feedback_formatter_node", "post_code_review_node")
-workflow.add_edge("post_code_review_node", "update_review_body_based_on_human_input_node")
-workflow.add_edge("update_review_body_based_on_human_input_node", END)
+graph_builder = create_graph()
 
 # Define the path for the SQLite database file
 #SQLITE_DB_PATH = "langgraph_checkpoints.sqlite"
@@ -89,7 +66,7 @@ except Exception as e:
     # If an error occurs here, global_memory_saver will remain None
 
 if global_memory_saver:
-    global_graph = workflow.compile(
+    global_graph = graph_builder.compile(
         checkpointer=global_memory_saver,
         interrupt_before=["update_review_body_based_on_human_input_node"]
     )