commit

app.py

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+from src.langgraphagenticai.main import load_langgraph_agenticai_app
+
+
+if __name__=="__main__":
+    load_langgraph_agenticai_app()

readme.md

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+##### agentic ai project

requirements.txt

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+
+#langchain
+# langgraph
+# langchain_community
+# langchain_core
+# langchain_groq
+# langchain_openai
+# faiss-cpu
+# streamlit

src/langgraphagenticai/LLMS/groqllm.py

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+import os
+import streamlit as st
+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_model']
+            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 Occurred with Exception : {e}")
+        return llm

src/langgraphagenticai/graph/graph_builder.py

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+from langgraph.graph import StateGraph, START,END, MessagesState
+from langgraph.prebuilt import tools_condition,ToolNode
+from langchain_core.prompts import ChatPromptTemplate
+from src.langgraphagenticai.state.state import State
+from src.langgraphagenticai.nodes.basic_chatbot_node import BasicChatbotNode
+
+
+
+
+class GraphBuilder:
+
+    def __init__(self,model):
+        self.llm=model
+        self.graph_builder=StateGraph(State)
+
+    def basic_chatbot_build_graph(self):
+        """
+        Builds a basic chatbot graph using LangGraph.
+        This method initializes a chatbot node using the `BasicChatbotNode` class 
+        and integrates it into the graph. The chatbot node is set as both the 
+        entry and exit point of the graph.
+        """
+        self.basic_chatbot_node=BasicChatbotNode(self.llm)
+        self.graph_builder.add_node("chatbot",self.basic_chatbot_node.process)
+        self.graph_builder.add_edge(START,"chatbot")
+        self.graph_builder.add_edge("chatbot",END)
+
+    def setup_graph(self, usecase: str):
+        """
+        Sets up the graph for the selected use case.
+        """
+        if usecase == "Basic Chatbot":
+            self.basic_chatbot_build_graph()
+        return self.graph_builder.compile()
+
+
+
+    
+

src/langgraphagenticai/main.py

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+import streamlit as st
+import json
+from src.langgraphagenticai.ui.streamlitui.loadui import LoadStreamlitUI
+from src.langgraphagenticai.LLMS.groqllm import GroqLLM
+from src.langgraphagenticai.graph.graph_builder import GraphBuilder
+from src.langgraphagenticai.ui.streamlitui.display_result import DisplayResultStreamlit
+
+# MAIN Function START
+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.
+    """
+   
+    # Load UI
+    ui = LoadStreamlitUI()
+    user_input = ui.load_streamlit_ui()
+
+    if not user_input:
+        st.error("Error: Failed to load user input from the UI.")
+        return
+
+    # 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:")
+
+    if user_message:
+            try:
+                # Configure LLM
+                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
+
+                # Initialize and set up the graph based on use case
+                usecase = user_input.get('selected_usecase')
+                if not usecase:
+                    st.error("Error: No use case selected.")
+                    return
+                
+
+                ### Graph Builder
+                graph_builder=GraphBuilder(model)
+                try:
+                    graph = graph_builder.setup_graph(usecase)
+                    DisplayResultStreamlit(usecase,graph,user_message).display_result_on_ui()
+                except Exception as e:
+                    st.error(f"Error: Graph setup failed - {e}")
+                    return
+                
+
+            except Exception as e:
+                 raise ValueError(f"Error Occurred with Exception : {e}")
+            
+
+        
+
+   
+
+    

src/langgraphagenticai/nodes/basic_chatbot_node.py

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+from src.langgraphagenticai.state.state import State
+
+class BasicChatbotNode:
+    """
+    Basic chatbot logic implementation.
+    """
+    def __init__(self,model):
+        self.llm = model
+
+    def process(self, state: State) -> dict:
+        """
+        Processes the input state and generates a chatbot response.
+        """
+        return {"messages":self.llm.invoke(state['messages'])}

src/langgraphagenticai/state/state.py

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+from typing import Annotated, Literal, Optional
+from typing_extensions import TypedDict
+from langgraph.graph.message import add_messages
+from typing import TypedDict, Annotated, List
+from langchain_core.messages import HumanMessage, AIMessage
+
+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

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+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

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+import streamlit as st
+import os
+from datetime import date
+
+from langchain_core.messages import AIMessage,HumanMessage
+from src.langgraphagenticai.ui.uiconfigfile import Config
+
+
+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 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
+        
+        
+
+        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

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+[DEFAULT]
+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
+

src/langgraphagenticai/ui/uiconfigfile.py

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+from configparser import ConfigParser
+
+class Config:
+    def __init__(self,config_file="./src/langgraphagenticai/ui/uiconfigfile.ini"):
+        self.config=ConfigParser()
+        self.config.read(config_file)
+
+    def get_llm_options(self):
+        return self.config["DEFAULT"].get("LLM_OPTIONS").split(", ")
+    
+    def get_usecase_options(self):
+        return self.config["DEFAULT"].get("USECASE_OPTIONS").split(", ")
+
+    def get_groq_model_options(self):
+        return self.config["DEFAULT"].get("GROQ_MODEL_OPTIONS").split(", ")
+
+    def get_page_title(self):
+        return self.config["DEFAULT"].get("PAGE_TITLE")
+