update

app.py

@@ -0,0 +1,5 @@
+from src.langgraphagenticai.main import load_langgraph_agenticai_app
+
+
+if __name__=="__main__":
+    load_langgraph_agenticai_app()

src/langgraphagenticai/LLMS/groqllm.py

@@ -0,0 +1,21 @@
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+
+class GroqLLM:
+    def __init__(self,user_controls_input):
+        self.user_controls_input = user_controls_input
+
+    def get_llm_model(self):
+        try:
+            groq_api_key = self.user_controls_input["GROQ_API_KEY"]
+            selected_groq_model = self.user_controls_input["selected_groq_models"]
+            if groq_api_key=='' and os.environ["GROQ_API_KEY"]=='':
+                st.error("Please Enter the Groq API key")
+            llm = ChatGroq(api_key=groq_api_key,model=selected_groq_model)
+        except Exception as e:
+            raise ValueError(f"Error occured with Exception: {e}")
+        
+        return llm
+    
+    

src/langgraphagenticai/graph/graph_builder.py

@@ -0,0 +1,15 @@
+from langgraph.graph import StateGraph,START,END,MessagesState
+from langgraph.prebuilt import tools_condition,ToolNode
+from langchain_core.prompts import ChatPromptTemplate
+import datetime
+from src.langgraphagenticai.state.state import State
+
+class GraphBuilder:
+
+    def __init__(self,model):
+        self.llm = model
+        self.graph_builder = StateGraph(State)
+
+    def basic_chatbot_build_graph(self):
+        
+

src/langgraphagenticai/main.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import json
+from src.langgraphagenticai.ui.streamlitui.loadui import LoadStreamlitUI
+from src.langgraphagenticai.LLMS.groqllm import GroqLLM
+
+def load_langgraph_agenticai_app():
+    """
+    Loads and runs the LangGraph AgenticAI application with Streamlit UI.
+    This function initializes the UI, handles user input, configures the LLM model,
+    sets up the graph based on the selected use case, and displays the output while 
+    implementing exception handling for robustness.
+    """
+
+    ui = LoadStreamlitUI()
+    user_input = ui.load_streamlit_ui()
+
+    if not user_input:
+        st.error("Error:Failed to load user input from UI")
+
+    #Text input for user message
+    if st.session_state.IsFetchButtonClicked:
+        user_message = st.session_state.timeframe
+    else:
+        user_message = st.chat_input("Enter your message:")
+
+    # Initializing the LLM
+    if user_message:
+        try:
+            obj_llm_config = GroqLLM(user_controls_input=user_input)
+            model = obj_llm_config.get_llm_model()
+
+            if not model:
+                st.error("Error: LLM model could not be initialized.")
+                return 
+            
+            usecase = user_input.get("selected_usecase")
+            if not usecase:
+                st.error("Error: Usecase not selected.")
+                return
+            
+        except Exception as e:
+            raise ValueError(f"Error occured with exception : {e}")
+        
+    # Graph Builder
+    
+

src/langgraphagenticai/nodes/basic_chatbot_node.py

@@ -0,0 +1,17 @@
+from src.langgraphagenticai.state.state import State
+
+class BasicChatbotNode:
+    """ 
+    Basic chatbot logic implementation.
+    """
+    def __init__(self,model):
+        self.llm = model
+
+    def process(self,state):
+        """ 
+        Processes the input state and generate the chatbot response
+        """
+        return {"messages":[self.llm.invoke(state["messages"])]}
+
+
+    

src/langgraphagenticai/state/state.py

@@ -0,0 +1,10 @@
+from typing import Annotated,Literal,Optional
+from typing_extensions import TypedDict
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AIMessage,HumanMessage
+
+class State(TypedDict):
+    """ 
+    Represents the structure of the state used in the graph
+    """
+    messages = Annotated[list,add_messages]

src/langgraphagenticai/ui/streamlitui/display_result.py

@@ -0,0 +1,24 @@
+import streamlit as st
+from langchain_core.messages import HumanMessage,AIMessage
+import json
+
+
+class DisplayResultStreamlit:
+    def __init__(self,usecase,graph,user_message):
+        self.usecase= usecase
+        self.graph = graph
+        self.user_message = user_message
+
+    def display_result_on_ui(self):
+        usecase= self.usecase
+        graph = self.graph
+        user_message = self.user_message
+        if usecase =="Basic Chatbot":
+                for event in graph.stream({'messages':("user",user_message)}):
+                    print(event.values())
+                    for value in event.values():
+                        print(value['messages'])
+                        with st.chat_message("user"):
+                            st.write(user_message)
+                        with st.chat_message("assistant"):
+                            st.write(value["messages"].content)

src/langgraphagenticai/ui/streamlitui/loadui.py

@@ -9,6 +9,55 @@ class LoadStreamlitUI:
         self.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 load_streamlit_ui(self):
-        st.set_page_config()
+        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
+        
+        
+
+        with st.sidebar:
+            # Get options from config
+            llm_options = self.config.get_llm_options()
+            usecase_options = self.config.get_usecase_options()
+
+            # LLM selection
+            self.user_controls["selected_llm"] = st.selectbox("Select LLM", llm_options)
+
+            if self.user_controls["selected_llm"] == 'Groq':
+                # Model selection
+                model_options = self.config.get_groq_model_options()
+                self.user_controls["selected_groq_model"] = st.selectbox("Select Model", model_options)
+                # API key input
+                self.user_controls["GROQ_API_KEY"] = st.session_state["GROQ_API_KEY"] = st.text_input("API Key",
+                                                                                                      type="password")
+                # Validate API key
+                if not self.user_controls["GROQ_API_KEY"]:
+                    st.warning("⚠️ Please enter your GROQ API key to proceed. Don't have? refer : https://console.groq.com/keys ")
+                   
+            
+            # Use case selection
+            self.user_controls["selected_usecase"] = st.selectbox("Select Usecases", usecase_options)
+            
+            if "state" not in st.session_state:
+                st.session_state.state = self.initialize_session()
+
+            
+            
+        
+        return self.user_controls
 

src/langgraphagenticai/ui/uiconfigfile.ini

@@ -2,4 +2,4 @@
 PAGE_TITLE = LangGraph: Build stateful Agentic AI Graph 
 LLM_OPTIONS = Groq
 USECASE_OPTIONS = Basic chatbot
-GROQ_MODEL_OPTIONS = mixtral-8x7b-32768,llama3-8b-8192,llama3-70b-8192,gemma-7b-i
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i

src/langgraphagenticai/ui/uiconfigfile.py

@@ -5,7 +5,7 @@ class config:
         self.config = ConfigParser()
         self.config.read(config_file)
 
-    def get_llm_option(self):
+    def get_llm_options(self):
         return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
     
     def get_usecase_options(self):