partially sdlc

src/langgraphagenticai/graph/graph_builder.py

@@ -3,6 +3,7 @@ from langgraph.prebuilt import tools_condition,ToolNode
 from langchain_core.prompts import ChatPromptTemplate
 import datetime
 #module import
+from src.langgraphagenticai.node.sdlc_node import SDLCNode
 from src.langgraphagenticai.node.ai_news_node import AINewsNode
 from src.langgraphagenticai.node import travel_planner_node
 from src.langgraphagenticai.node.customer_support_chatbot import Customer_Support_Bot
@@ -10,7 +11,7 @@ from src.langgraphagenticai.tools.customtool import book_appointment, cancel_app
 from src.langgraphagenticai.tools.search_tool import create_tool_node, get_tools
 from src.langgraphagenticai.node.chatbot_with_tool_node import ChatbotWithToolNode
 from src.langgraphagenticai.node.basic_chatbot_node import BasicChatbotNode
-from src.langgraphagenticai.state.state import State
+from src.langgraphagenticai.state.state import State , SDLCState
 from src.langgraphagenticai.node.travel_planner_node import TravelPlannerNode
 
 class GraphBuilder:
@@ -20,6 +21,7 @@ class GraphBuilder:
     def __init__(self,model):
         self.llm = model
         self.graph_builder = StateGraph(State)
+        self.sdlc_graph_builder = StateGraph(SDLCState)
         
     def basic_chatbot_build_graph(self):
         """
@@ -148,6 +150,22 @@ class GraphBuilder:
         self.graph_builder.add_edge("fetch_news", "summarize_news")
         self.graph_builder.add_edge("summarize_news", "save_result")
         self.graph_builder.add_edge("save_result", END)
+        
+        
+    def sdlc_workflow_build_graph(self):
+        
+        sdlc_wf_node = SDLCNode(self.llm)
+        
+        self.graph_builder = self.sdlc_graph_builder
+
+        self.graph_builder.add_node("generate_user_stories", sdlc_wf_node.generate_user_stories)
+        self.graph_builder.add_node("generate_code", sdlc_wf_node.generate_code)
+
+        self.graph_builder.set_entry_point("generate_user_stories")
+        self.graph_builder.add_edge("generate_user_stories", "generate_code")
+        self.graph_builder.add_edge("generate_code", END)
+        
+
 
     def setup_graph(self, usecase: str):
         """
@@ -165,6 +183,8 @@ class GraphBuilder:
             self.customer_support_build_graph()
         elif usecase =="AI News":
             self.ai_news_build_graph()
+        elif usecase =="SDLC Workflow":
+            self.sdlc_workflow_build_graph()
         else:
             raise ValueError("Invalid use case selected.")
         return self.graph_builder.compile()

src/langgraphagenticai/main.py

@@ -7,6 +7,7 @@ import streamlit as st
 import json
 
 
+
 # MAIN Function START
 def load_langgraph_agenticai_app():
     """
@@ -28,6 +29,8 @@ def load_langgraph_agenticai_app():
         # Text input for user message
         if st.session_state.IsFetchButtonClicked:
             user_message = st.session_state.timeframe 
+        elif st.session_state.IsSDLC :
+            user_message = st.session_state.state
         else :
             user_message = st.chat_input("Enter your message:")
         if user_message:

src/langgraphagenticai/node/sdlc_node.py

@@ -0,0 +1,38 @@
+import streamlit as st
+
+class SDLCNode:
+    def __init__(self, llm):
+        self.llm = llm
+
+    def generate_user_stories(self, state: dict) -> dict:
+        prompt = f"""
+        Generate comprehensive user stories based on the following requirements:
+        {state['requirements']}
+        
+        {f"PO Feedback to incorporate: {state['po_feedback']}" if state.get('po_feedback') else ""}
+        
+        Format the user stories in Markdown with clear acceptance criteria.
+        """
+        
+        response = self.llm.invoke(prompt)
+        
+        st.session_state.state["user_stories"] = response.content
+        st.session_state.state["current_step"] = "po_approval"
+        return {"user_stories": response.content, "current_step": "po_approval"}
+
+    def po_approval(self, state: dict) -> dict:
+        return {"current_step": "po_approval"}
+
+    def generate_code(self, state: dict) -> dict:
+        prompt = f"""
+        Generate production-quality code based on these user stories:
+        {state['user_stories']}
+        
+        {f"Review Feedback to incorporate: {state['review_feedback']}" if state.get('review_feedback') else ""}
+        
+        Include proper error handling, comments, and follow best practices.
+        """
+        
+        response = self.llm.invoke(prompt)
+        st.session_state.state["generated_code"] = response.content 
+        return {"generated_code": response.content, "current_step": "code_review"}

src/langgraphagenticai/state/state.py

@@ -1,4 +1,4 @@
-from typing import Annotated
+from typing import Annotated, Literal, Optional
 from typing_extensions import TypedDict
 from langgraph.graph.message import add_messages
 from typing import TypedDict, Annotated, List
@@ -17,4 +17,20 @@ class PlannerState(TypedDict):
     interests: List[str]
     itinerary: str
     start_date: str 
-    end_date: str    
+    end_date: str    
+    
+class SDLCState(TypedDict):
+    current_step: Literal[
+        "requirements",
+        "generate_user_stories",
+        "po_approval",
+        "generate_code",
+        "code_review",
+        "completed"
+    ]
+    requirements: Optional[str]
+    user_stories: Optional[str]
+    po_feedback: Optional[str]
+    generated_code: Optional[str]
+    review_feedback: Optional[str]
+    decision: Optional[Literal["approved", "feedback"]]

src/langgraphagenticai/tools/customer_support_tools.py

@@ -4,7 +4,8 @@ import json
 
 from src.langgraphagenticai.vectorstores.vectore_store import FlowerShopVectorStore
 
-vector_store = FlowerShopVectorStore()
+
+
 INVENTORY_FILE_PATH = './data/inventory.json'
 
 customers_database = [
@@ -101,7 +102,7 @@ def query_knowledge_base(query: str) -> List[Dict[str, str]]:
     Return:
         List[Dict[str, str]]: Potentially relevant question and answer pairs from the knowledge base
     """
-    return vector_store.query_faqs(query=query)
+    return FlowerShopVectorStore().vector_store.query_faqs(query=query)
 
 
 
@@ -120,7 +121,7 @@ def search_for_product_reccommendations(description: str):
     Return:
         List[Dict[str, str]]: Potentially relevant products
     """
-    return vector_store.query_inventories(query=description)
+    return FlowerShopVectorStore().vector_store.query_inventories(query=description)
 
 @tool
 def retrieve_existing_customer_orders(customer_id: str) -> List[Dict]:

src/langgraphagenticai/ui/streamlitui/display_result.py

@@ -87,7 +87,6 @@ class DisplayResultStreamlit:
                 st.write(customers_database)
                 st.title('data protection checks')
                 st.write(data_protection_checks) 
-        
         elif usecase == "AI News":
             frequency = self.user_message
             with st.spinner("Fetching and summarizing news... ⏳"):
@@ -114,11 +113,22 @@ class DisplayResultStreamlit:
                         mime="text/markdown"
                     )
                 st.success(f"✅ Summary saved to {AI_NEWS_PATH}")
-                
-            # display graph
-            if graph:
-                st.write('state graph - workflow')
-                st.image(graph.get_graph(xray=True).draw_mermaid_png())
+         
+        elif usecase == "SDLC Workflow":
+            initial_state = self.user_message
+            # Invoke the workflow node for generating user stories
+            result = graph.invoke(initial_state)
+            if 'user_stories' in result:
+                with st.expander('generated userstories'):
+                    st.markdown(result["user_stories"])
+            if 'generated_code' in result:
+                with st.expander('generated code'):
+                    st.markdown(result['generated_code'])
+                   
+        # display graph
+        if graph:
+            st.write('state graph - workflow')
+            st.image(graph.get_graph(xray=True).draw_mermaid_png())
             
     def _display_travel_planner_results(self):
         # Extract travel parameters from message

src/langgraphagenticai/ui/streamlitui/loadui.py

@@ -10,13 +10,36 @@ class LoadStreamlitUI:
     def __init__(self):
         self.config =  Config() # config
         self.user_controls = {}
+        
+    def initialize_session(self):
+        return {
+            "current_step": "requirements",
+            "requirements": "",
+            "user_stories": "",
+            "po_feedback": "",
+            "generated_code": "",
+            "review_feedback": "",
+            "decision": None
+        }
 
+    def render_requirements(self):
+        st.markdown("## 📝 Requirements Submission")
+        st.session_state.state["requirements"] = st.text_area(
+            "Enter your requirements:",
+            height=200,
+            key="req_input"
+        )
+        if st.button("Submit Requirements", key="submit_req"):
+            st.session_state.state["current_step"] = "generate_user_stories"
+            st.session_state.IsSDLC = True
+            
 
     def load_streamlit_ui(self):
         st.set_page_config(page_title= "🤖 " + self.config.get_page_title(), layout="wide")
         st.header("🤖 " + self.config.get_page_title())
         st.session_state.timeframe = ''
         st.session_state.IsFetchButtonClicked = False
+        st.session_state.IsSDLC = False
         
         
 
@@ -117,4 +140,15 @@ class LoadStreamlitUI:
             else :
                 st.session_state.IsFetchButtonClicked = False
                     
+        
+        # Added for SDLC Workflow
+        elif self.user_controls['selected_usecase']=="SDLC Workflow":
+            st.subheader(" SDLC Workflow ")
+            
+            
+            if "state" not in st.session_state:
+                st.session_state.state = self.initialize_session()
+            self.render_requirements()
+            
+        
         return self.user_controls

src/langgraphagenticai/ui/uiconfigfile.ini

@@ -1,6 +1,6 @@
 [DEFAULT]
 PAGE_TITLE = LangGraph: Build Stateful Agentic AI graph
 LLM_OPTIONS = Groq
-USECASE_OPTIONS = Basic Chatbot, Chatbot with Tool, Travel Planner, AI News, Appointment Receptionist, Customer Support
+USECASE_OPTIONS = Basic Chatbot, Chatbot with Tool, Travel Planner, AI News, SDLC Workflow, Appointment Receptionist, Customer Support
 GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i