ea4all-gradio-agents-mcp-hackathon-kickoff

ea4all/__main__.py

@@ -1,10 +1,10 @@
-from ea4all import app_ea4all_agent
+from ea4all import ea4all_mcp as e4m
 import os
 
 def main() -> None:
     #Launch UI
     try:
-        app_ea4all_agent.ea4all_mcp.Launch(
+        e4m.ea4all_mcp.launch(
             server_name=os.getenv("GRADIO_SERVER_NAME","0.0.0.0"),
             server_port=os.getenv("GRADIO_SERVER_PORT",None),
             debug=os.getenv("GRADIO_DEBUG",True),

ea4all/app_ea4all_agent.py

@@ -1,655 +0,0 @@
-#Added agentic-workflow-collaboration-agents
-#Multimodal ChatInterface - not working
-#Added new QA Tab
-#Added new agent Well-Architected
-#Added Supervisor Agent workflow
-#ISSUE with VQA fixed
-#LLMChain refactored
-#Updated with changes as result of ea4all_agent Gradio Space deployment issues 
-#VQA Safeguardings - JPEG, PNG images only
-#Deployed version to Live
-#Library import refactoring, ea4all-architecture, empty message
-#Bring your own IT Landscape data: discontinued 
-#Added upload your Business Requirement
-#Load user's APM - disabled 2024-06-22
-#TEST E2E Togaf Agentic system 2024-06-24
-#MIGRATION TO HF Open Source using TGI and Meta-Llama-3-8B-Instruct 2024-06-25
-#ADDED GENERATE_ARCHITECTURE_RUNWAY diagram: graphviz 2024-07-03
-#REFERENCE ARCHITECTURE DYNAMIC TABS 2024-07-05
-#ADDED Business Query grader 2024-07-07
-#RCA Togaf Supervisor: increase reliability 2024-07-08 - ISSUE FIXED BY NOW
-#EA4ALL-agentic-system-container updated 2024-07-10
-###APM Agentic system: 2024-07-25 - Safety check added
-##Sub-graph node stream 204-07-26
-# Stream arbitrary nested content: https://langchain-ai.github.io/langgraph/how-tos/streaming-content/
-## Prompt refinement task_router, user_question_routing, prompt_category 2024-07-27
-## WebSearch Hallucination issue - recursion looping - solution: routing to route_question 2024-07-28
-## Safety_check greetings, msgs, APM Sample Dataset 2024-09-29
-# VQA issue - image not recognised 2024-07-30
-# Constants IMAGES (Architecture, Overview) 2024-07-31
-# About, QnA Examples moved to mock files 2024-08-01 - deployed to build
-## 2024-08-03: VQA Streaming, Diagrams' EDGE nodes changed to END - one task at a time: 2024-08-03
-## VQA Llama-3.2-11B-Vision-Instruct 2024-10-25
-#RELEASE 2024-11-15
-## CHANGES 2024-11-22
-# MIGRATION to Gradio 5
-# Chatbot UI migrated to gr.Chatbot 
-# run_qna_agentic_system, run_vqa_agentic_system updated: ChatMessage, chat_memory, UI events
-# chat_memory VQA missing image - fixed - needs improvement
-## RELEASE 2024-11-23
-#pip freeze > requirements.txt to keep libraries synched local and HF Spaces
-#gr.Image issue: caused by __main__ root_path=str(Path.cwd())
-## RELEASE 2024-12-09
-#Confluence Integration
-#Llama-3.2-11B-Vision-Instruct max_token issue <=4096 stills
-#Safety-check refinement
-#TOGAF Vision streaming
-## Release update 2024-12-11
-#EA4ALL Podcast
-#2025-02-03 RELEASE V1
-##RETROFIT & INTEGRATION w/ EA4ALL-dev-studio-structure
-#2025-02-09 
-##UAT EA4ALL-LGS-RETRIEVER-REFACTORED
-#2025-03-10
-##AI-Assistant-UI-Message-Stream refactor
-#2025-12-04
-## Add EA4ALL-PMO-Demand-Management CrewAI Agents
-#2025-05-06
-## Add MCP Server
-#2025-05-17
-## Added PMO_MOCK_QNA examples, 
-## get_relevant_questions() - moved to utils, constants moved to configuration
-#2025-05-19
-## EA4ALL Diagram refactored, vqa_max_tokens updated
-from langchain.callbacks.tracers import LangChainTracer
-from langchain.callbacks.tracers.langchain import wait_for_all_tracers
-from langchain_community.document_loaders import ConfluenceLoader
-from langchain_core.messages import HumanMessage
-from langsmith import Client
-
-from ea4all.src.shared.configuration import BaseConfiguration, APM_MOCK_QNA, PMO_MOCK_QNA
-from ea4all.src.shared import vectorstore
-from ea4all.src.ea4all_gra.configuration import AgentConfiguration as gra
-from ea4all.src.ea4all_indexer.configuration import IndexConfiguration
-import ea4all.src.ea4all_apm.graph as e4a
-import ea4all.src.ea4all_vqa.graph as e4v
-import ea4all.src.ea4all_gra.graph as e4t
-import ea4all.src.shared.utils as e4u
-from ea4all.src.ea4all_indexer.graph import indexer_graph
-from ea4all.src.graph import super_graph
-#from ea4all.src.pmo_crew.crew_runner import run_pmo_crew
-
-import uuid
-import os
-import pandas as pd
-
-import gradio as gr
-from gradio import ChatMessage
-import time
-from PIL import Image
-
-#Set LangSmith project
-tracer = LangChainTracer(project_name=os.getenv('LANGCHAIN_PROJECT'))
-
-config = {
-    "run_name": os.getenv('LANGCHAIN_RUNNAME'), 
-    "tags": [os.getenv('EA4ALL_ENV')],
-    "callbacks":[tracer],
-    "recursion_limit": 25,
-    "configurable": {
-        "thread_id": uuid.uuid4(),
-    },
-    "stream_mode": "messages"
-}
-
-#Blocks w/ ChatInterface, BYOD, About
-with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as ea4all_mcp:
-
-    agentic_pmo_desc="""
-        Hi, 
-        Provide project resource estimation for architecture work based on business requirements, skillset, 
-        architects allocation, and any other relevant information to enable successful project solution delivery."""
-    
-    agentic_qna_desc="""
-        Hi,
-        improve effieciency, knowledge sharing, and get valuable insights from your IT landscape using natural language.
-        As an Enterprise Architect Agentic System I can answer questions related to Enterprise Architecture, Technology, plus the following IT Landscape sample dataset: """
-
-    agentic_vqa_desc="""
-    Hi, talk to your Architecture Diagram using natural language. Gain rapid knowledge and insights translating image to meaningful description.
-    **Disclaimer**:
-    - This feature should NOT BE USED to process inappropriate content, but ONLY FOR Architecture Diagrams
-    """
-
-    agentic_togaf_desc="""
-        Hi,
-        in a click of button create a reference architecture that serves as a blueprint for designing and implementing IT solutions.
-        Standardise, increase efficiency and productivity to architecture solution development.
-        Generate context-specific reference and minimal viable architectures to support business and IT strategy and digital transformation.
-        Streamline the architecture operating model, taking the best of agentic workflows and architects working together.
-    """
-    
-        #ea4all-about
-    
-    def ea4all_about():
-        readme = e4u.load_mock_content(e4u.CFG.EA4ALL_ABOUT)
-        return readme
-   
-    #Load demo business requirements
-    def init_dbr():
-        # Open the file in read mode ('r')
-        with open(e4u._join_paths(BaseConfiguration.ea4all_store, gra.dbr_mock), 'r') as file:
-            # Read the contents of the file
-            contents = file.read()
-        return contents
-
-    def init_df():
-        return vectorstore.apm_dataframe_loader(e4u._join_paths(BaseConfiguration.ea4all_store, IndexConfiguration.apm_catalogue))
-    
-    #load core-architecture image
-    #fix the issue with gr.Image(path) inside a docker containder
-    def get_image(_image):
-        #from PIL import Image
-        # Load an image
-        image = e4u._join_paths(BaseConfiguration.ea4all_images,_image)
-        print(f"Full path: {image}")
-
-        return image
-
-    #ea4all-qna-agent-conversational-with-memory
-    async def run_qna_agentic_system(prompt, chat_memory, request:gr.Request):
-        
-        format_response = ""
-
-        if not prompt:
-            format_response = "Hi, how are you today? To start our conversation, please chat your message!"
-            chat_memory.append(ChatMessage(role="assistant", content=format_response))
-            yield chat_memory
-
-        if not chat_memory:
-            chat_memory.append(ChatMessage(role="user", content=prompt))
-            yield chat_memory
-
-        if prompt:
-            #capture user ip
-            ea4all_user = e4u.get_user_identification(request)
-
-            ##Initialise APM Graph
-            #apm_graph = e4a.apm_graph
-            #inputs = {"question": prompt, "chat_memory":chat_memory}
-            inputs = {"messages": [{"role": "user", "content": prompt}]}
-            
-            #add prompt to memory
-            chat_memory.append(ChatMessage(role="user", content=prompt))
-
-            partial_message = ""
-            async for event in super_graph.astream_events(input=inputs, config=config, version="v2"):
-            #async for event in super_graph.astream(input=inputs, config=config, subgraphs=True):
-             #   chat_memory.append(ChatMessage(role="assistant", content=str(event)))
-             #   yield chat_memory
-
-                kind = event["event"]
-                tags = event.get("tags", [])
-                name =  event['name']
-                
-                #chat_memory.append(ChatMessage(role="assistant", content=f"Running: {name}"))
-                #yield chat_memory
-
-                if name == "safety_check":
-                    #if kind == "on_chain_start":
-                    #    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}`"))
-                    #    yield chat_memory
-                    if kind == "on_chain_stream":
-                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}`: {event['data']['chunk']['safety_status'][0]}"))  
-                        if event['data']['chunk']['safety_status'][0] == 'no': 
-                            chat_memory.append(ChatMessage(role="assistant", content=f"Safety-status: {event['data']['chunk']['safety_status'][1]}"))
-                        yield chat_memory
-                if kind == "on_chain_end" and name == "route_question":
-                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}:` {event['data']['output']['source']}"))
-                    yield chat_memory
-                if kind == "on_chain_start" and name == "retrieve":
-                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}` RAG\n\n"))
-                    yield chat_memory
-                if kind == "on_chain_start" and name in ("generate_web_search", "websearch", "stream_generation"):
-                    chat_memory.append(ChatMessage(role="assistant", content= f"\n\n- `{name}`\n\n"))
-                    yield chat_memory
-                if kind == "on_chain_stream" and name == "stream_generation":
-                    data = event["data"]
-                    # Accumulate the chunk of data
-                    partial_message += data['chunk']
-                    chat_memory[-1].content = partial_message
-                    time.sleep(0.05)
-                    yield chat_memory
-                if name == "grade_generation_v_documents_and_question":
-                    if kind == "on_chain_start": 
-                        chat_memory.append(ChatMessage(role="assistant", content=f"\n\n- `{name}`: "))
-                        yield chat_memory
-                    if kind == "on_chain_end":
-                        chat_memory.append(ChatMessage(role="assistant", content=f"`{event['data']['input'].source}:` {event['data']['output']}"))
-                        yield chat_memory
-                if "stream_hallucination" in tags and kind == "on_chain_start":
-                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{tags[-1]}`"))
-                    yield chat_memory
-                if "stream_grade_answer" in tags and kind == "on_chain_start":
-                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{tags[-1]}`"))
-                    yield chat_memory
-                if name == "supervisor":
-                    if kind == "on_chain_start":
-                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}` "))
-                        yield chat_memory
-                    if kind == "on_chain_stream":
-                        chat_memory.append(ChatMessage(role="assistant", content=f"{event['data']['chunk']}"))
-                        yield chat_memory
-
-            os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
-            wait_for_all_tracers()
-
-    #Trigger Solution Architecture Diagram QnA
-    async def run_vqa_agentic_system(message, chat_memory, request:gr.Request):
-        #capture user ip
-        ea4all_user = e4u.get_user_identification(request)
-
-        """Handle file uploads and validate their types."""
-        allowed_file_types = ('JPEG', 'PNG')
-
-        print("---CALLING VISUAL QUESTION ANSWERING AGENTIC SYSTEM---")
-        print(f"Prompt: {message}")
-
-        if message['files'] == []:
-            chat_memory.append(ChatMessage(role="assistant", content="Please upload an Architecture PNG, JPEG diagram to start!"))
-            yield chat_memory
-        else:
-            diagram = message['files'][-1] ##chat_memory[-1]['content'][-1]        
-            msg = message['text'] ##chat_memory[-2]['content']
-            print(f"---DIAGRAM: {diagram}---")
-            try:
-                if msg == "":
-                    msg = "Please describe this diagram."
-
-                with Image.open(diagram) as diagram_:
-                    if diagram_.format not in allowed_file_types:
-                        chat_memory.append(ChatMessage(role="assistant", content="Invalid file type. Allowed file types are JPEG and PNG."))
-                        yield chat_memory
-                    else:
-                        #'vqa_image = e4u.get_raw_image(diagram) #MOVED into Graph
-                        vqa_image = diagram
-
-                        #Setup Quality Assurance Agentic System
-                        #graph = e4v.ea4all_graph(config['configurable']['vqa_model'])
-
-                        #Setup enter graph
-                        diagram_graph = e4v.diagram_graph
-
-                        partial_message = ""
-                        chat_memory.append(ChatMessage(role="assistant", content="Hi, I am working on your question..."))
-                        async for event in diagram_graph.astream_events(
-                            {"question":msg, "image": vqa_image}, config, version="v2"
-                        ):
-                            if (
-                                    event["event"] == "on_chat_model_stream"
-                                    and "vqa_stream" in event['tags']
-                                    #and event["metadata"].get("langgraph_node") == "tools"
-                                ):
-                                    partial_message += event["data"]["chunk"].content
-                                    chat_memory[-1].content = partial_message
-                                    time.sleep(e4u.CFG.STREAM_SLEEP)
-                                    yield chat_memory #, message to update prompt
-                            elif not partial_message:
-                                yield chat_memory #, message
-                        
-                        os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
-                        wait_for_all_tracers()
-
-            except Exception as e:
-                yield (e.args[-1])
-
-    #Run Togaf Agentic System
-    async def run_reference_architecture_agentic_system(business_query, request:gr.Request):
-
-        if len(business_query) < 50:
-            agent_response = "Please provide a valid Business Requirement content to start!"
-            yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
-        else: 
-            plain_text = e4u.markdown_to_plain_text(business_query)
-            agent_response = "Generating Architecture Blueprint ---TOGAF VISION TARGET--- \n\nI am working on your request..."
-            togaf_chain = e4t.togaf_graph
-            final_diagram = ""
-            vision_message = ""
-            try:
-                async for s in togaf_chain.astream_events(
-                    {
-                        "messages": [
-                            HumanMessage(
-                                content=plain_text
-                            )
-                        ],
-                        "business_query": business_query,
-                    },
-                    config=config,
-                    version="v2"
-                ):    
-                    kind = s["event"]
-                    tags = s.get("tags", [])
-                    name = s['name']
-
-                    if "gra_stream" in tags and name == "stream_vision_target":
-                        if kind == "on_chain_stream":
-                            data = s["data"]
-                            # Accumulate the chunk of data
-                            vision_message += data['chunk'].content
-                            time.sleep(e4u.CFG.STREAM_SLEEP)
-                            yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
-                    elif name == "save_diagram" and kind == 'on_chain_end': #MOVED INTO Togaf_Task3
-                        final_diagram = s['data']['output']['architecture_runway']
-                    elif ("assess_business_query" in tags or "assess_landscape" in tags) and kind == 'on_chain_start': ##'on_chat_model_stream':
-                        agent_response += f"\n\n`{tags[-1]}:{name}`"
-
-                        yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
-                    
-                if vision_message=="":
-                    agent_response = "I cannot generate the Architecture Vision. Please provide a valid Business Requirement content to start!"
-                    yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
-                elif "Error" not in final_diagram:
-                    yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),final_diagram, None, gr.Tabs(visible=True)])
-                else:
-                    yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, final_diagram, gr.Tabs(visible=True)])
-
-            except Exception as e:  
-                yield(
-                    [
-                        e.args[-1],
-                        gr.Tabs(visible=True), 
-                        gr.Tabs(selected="id_togaf"),
-                        None,
-                        None,
-                        gr.Tabs(visible=False)
-                    ]
-                )
-
-    async def run_pmo_agentic_system(prompt, chat_memory):
-        """
-        Answer a question about Project Portfolio Management and Architect Demand Management.
-
-        Args:
-            prompt (str): The propject portfolio user question
-            chat_memory (list): The tool message history
-
-        Returns:
-            str: A summary answering the user question
-        """
-        format_response = ""
-
-        if not prompt:
-            format_response = "Hi, how are you today? To start our conversation, please chat your message!"
-            chat_memory.append(ChatMessage(role="assistant", content=format_response))
-            yield chat_memory
-
-        if not chat_memory:
-            chat_memory.append(ChatMessage(role="user", content=prompt))
-            yield chat_memory
-
-        inputs = {
-            "question": prompt,
-            "verbose": True,  # optional flags
-        }
-        
-        #yield run_pmo_crew(inputs)
-
-    def ea4all_confluence():
-
-        #Confluence API Key
-        confluence_api_key = os.environ['CONFLUENCE_API_KEY']
-
-        loader = ConfluenceLoader(
-            url="https://learnitall.atlassian.net/wiki", username="learn-it-all@outlook.com", api_key=confluence_api_key,
-            space_key="~71202000cd55f36336455f8c07afa1860ba810", 
-            include_attachments=False, limit=10,
-            keep_markdown_format=True
-        )
-
-        documents = loader.load()
-
-        data = {
-                "title": [doc.metadata["title"] for doc in documents],
-                "source": [doc.metadata["source"] for doc in documents],
-                "page_content": [doc.page_content for doc in documents],
-            }
-
-        df = pd.DataFrame(data)
-
-        return df        
-
-    def filter_page(page_list, title):
-        x =  page_list[page_list["title"] == title]
-        return x.iloc[0]['page_content']
-
-    #EA4ALL-Agentic system menu
-    with gr.Tabs(selected="how_to") as tabs:
-        with gr.Tab(label="Architect Demand Management"):
-            with gr.Tab(label="Architect Project Planning", id="pmo_qna_1"):
-                    ea4all_pmo_description = gr.Markdown(value=agentic_pmo_desc)
-                    pmo_chatbot = gr.Chatbot(label="EA4ALL your AI Architect Companion", type="messages")
-                    pmo_prompt = gr.Textbox(lines=1, show_label=False, max_lines=1, submit_btn=True, stop_btn=True,autofocus=True, placeholder="Type your message here or select an example...")
-                    with gr.Accordion("Open for prompt examples", open=False):
-                        pmo_examples = gr.Dropdown(e4u.load_mock_content(PMO_MOCK_QNA), value=None,label="Questions", interactive=True)
-                    gr.ClearButton([pmo_chatbot,pmo_prompt], value="Clear", size="sm", visible=False)
-            with gr.Tab(label="Project Portfolio Sample Dataset", id="id_pmo_ds"):
-                    pmo_df = gr.Dataframe()
-        with gr.Tab(label="Application Landscape QnA"):
-            with gr.Tabs() as tabs_apm_qna:
-                with gr.Tab(label="Connect, Explore, Together", id="app_qna_1"):
-                    ea4all_agent_metadata = gr.Markdown(value=agentic_qna_desc)
-                    ea4all_chatbot = gr.Chatbot(label="EA4ALL your AI Architect Companion", type="messages")
-                    qna_prompt = gr.Textbox(lines=1, show_label=False, max_lines=1, submit_btn=True, stop_btn=True,autofocus=True, placeholder="Type your message here or select an example...")
-                    with gr.Accordion("Open for prompt examples", open=False):
-                        qna_examples = gr.Dropdown(e4u.load_mock_content(APM_MOCK_QNA), value=None,label="Questions", interactive=True)
-                    gr.ClearButton([ea4all_chatbot,qna_prompt], value="Clear", size="sm", visible=False)
-                with gr.Tab(label="Sample Dataset", id="id_apm_ds"):
-                    apm_df = gr.Dataframe()
-        with gr.Tab(label="Diagram Question and Answering"):
-            gr.Markdown(value=agentic_vqa_desc)
-            ea4all_vqa = gr.Chatbot(label="EA4ALL your AI Multimodal Architect Companion", type="messages")
-            vqa_prompt = gr.MultimodalTextbox(interactive=True, show_label=False, submit_btn=True, stop_btn=True, autofocus=True, placeholder="Upload your diagram and type your message or select an example...")
-            with gr.Accordion("Open for prompt examples", open=False):
-                vqa_examples = gr.Dropdown(e4u.get_vaq_examples(), value=None,label="Diagram and Questions", interactive=True)
-            gr.ClearButton([ea4all_vqa,vqa_prompt,vqa_examples], value="Clear", size="sm", visible=True)
-        with gr.Tab(label="Reference Architecture", id="id_refarch"):
-            with gr.Tabs(selected="id_dbr") as tabs_reference_architecture:
-                with gr.Tab(label='Business Requirement', id="id_dbr"):
-                    gr.Markdown(value=agentic_togaf_desc)
-                    dbr_text=gr.TextArea(value=init_dbr, lines=14, interactive=True)
-                    with gr.Row():
-                        dbr_file=gr.File(
-                            value=e4u._join_paths(BaseConfiguration.ea4all_store, gra.dbr_mock),
-                            label="Business Requirement", 
-                            height=35, 
-                            show_label=False, 
-                            file_count="single", 
-                            file_types=['text'], 
-                            interactive=True,
-                            type='binary'
-                        )
-                        dbr_run=gr.Button(scale=None,value="Run Reference Architecture")
-                        dbr_cls=gr.ClearButton([dbr_file,dbr_text])
-                with gr.Tab(label='Confluence Integration', id="id_confluence"):
-                    confluence_list = gr.Dropdown(value=None, label="Confluence Pages", interactive=True)
-                    confluence_df = gr.DataFrame(visible=False, headers=["title", "source", "page_content"])  
-                    @gr.render(inputs=[confluence_list,confluence_df])
-                    def show_page(page, df):
-                        if page:
-                            with gr.Row():
-                                with gr.Column():
-                                    dbr_confluence =gr.Button(scale=None,value="Run Reference Architecture")
-                                with gr.Column():
-                                    btn=gr.Button("Clear")
-                            with gr.Row(variant='default', show_progress=True):
-                                page_content = gr.Markdown(filter_page(df,page), line_breaks=True)
-                                btn.click(lambda: gr.Dropdown(value=None), None, confluence_list)
-                                dbr_confluence.click(run_reference_architecture_agentic_system,show_progress='full', inputs=[page_content],outputs=[togaf_vision,tabs_togaf,tabs_reference_architecture, architecture_runway, diagram_header, tab_diagram])
-                with gr.Tab(label='Reference Architecture',visible=False, id="id_togaf") as tabs_togaf:
-                    togaf_vision=gr.Markdown(value='### Reference Architecture: Vision and Target')
-                with gr.Tab(label="Target Architecture Runway",visible=False, id="id_runway") as tab_diagram:
-                    diagram_header=gr.Markdown(visible=True)
-                    architecture_runway=gr.Image(label="Target Architecture Runway",interactive=False,visible=True, scale=10)
-        with gr.Tab(label="Overview", id="how_to"):
-            gr.Markdown(
-                """
-                    # Title
-        
-                    **Explore, Share, Together:** harness the value of `Enterprise Architecture in the era of Generative AI` to positively impact individuals and organisations.\n
-
-                    ## Overview
-                """
-            ),
-            gr.Image(
-                get_image(e4u.CFG.EA4ALL_ARCHITECTURE),
-                show_download_button=False,
-                container=False,
-                show_share_button=False,
-                )
-            gr.Markdown(
-                """
-                    ## Journey
-
-                    Audio overview summarising the key learnings, challenges, so what, stats from day-1 to last sprint. (**Powered by Google NoteBookLM**)
-
-                """
-            )
-            podcast = gr.Audio(
-                type="filepath",
-                value=os.path.join(BaseConfiguration.ea4all_store,e4u.CFG.EA4ALL_PODCAST), 
-                label="EA4ALL Journey Podcast", 
-                show_download_button=False,
-                autoplay=False,
-                container=True,
-                interactive=False,
-            )
-            gr.Markdown(ea4all_about)
-
-    #get LLM response user's feedback
-    def get_user_feedback(evt: gr.SelectData, request:gr.Request):
-        ##{evt.index} {evt.value} {evt._data['liked']}
-        try:     
-            uuid_str = os.environ["EA4ALL_" + e4u.get_user_identification(request).replace(".","_")]
-            gr.Info("Thanks for your feedback - run_id: " + uuid_str)
-            run_id = uuid.UUID(uuid_str)
-            client = Client()
-            client.create_feedback(
-                run_id,
-                key="feedback-key",
-                score= 1.0 if evt._data['liked'] == True else 0,
-                comment=str(evt.value)
-            )
-        except Exception as e:
-            gr.Warning(f"Couldn't capture a feedback: {e}")
-
-    #Set initial state of apm, llm and capture user-ip
-    async def ea4all_agent_init(request:gr.Request):
-
-        #capture user IP address
-        #ea4all_user = e4u.get_user_identification(request)
-        gr.Info("Thank you for connecting! I'd love to hear your feedback! Thumbs up or Thumbs down. LinkedIn comment.")
-
-        # Set initial landscape vectorstore
-
-        await indexer_graph.ainvoke(input={"docs":[]}, config=config)
-
-        #set chatbot description w/ user apm columns
-        df = vectorstore.apm_dataframe_loader(e4u._join_paths(BaseConfiguration.ea4all_store, IndexConfiguration.apm_catalogue))
-        columns_string = ', '.join(df.columns)
-        apm_columns = agentic_qna_desc + columns_string
-
-        prompt=ChatMessage(role='assistant', content='Hi, I am your Architect Copilot! How can I help you today?')
-
-        page_list = ea4all_confluence()
-
-        #Load gradio.dataframe with Portfolio sample dataset
-        pmo_df = pd.read_csv("ea4all/ea4all_store/ea4all-portfolio-management.csv")
-
-        return (
-            apm_columns, 
-            [prompt], 
-            [prompt],
-            [prompt],
-            gr.Dropdown(choices=page_list['title'].values.tolist()), 
-            gr.DataFrame(value=page_list), 
-            gr.DataFrame(value=df),
-            gr.DataFrame(value=pmo_df),
-        )
-
-    #authentication
-    def ea4all_login(username, password):
-        return (username==password)
-
-    #TABS & Reference Architecture look-and-feel control
-    def off_dbrtext():
-        return gr.TextArea(visible=False), gr.Tab(visible=False), gr.Tab(visible=False)
-
-    def on_dbrtext(file):
-        if file:
-            return gr.TextArea(visible=True)
-        return gr.TextArea(visible=False)
-    
-    #Upload & clear business requirement
-    def load_dbr(file):
-        return file.decode()
-    
-    def unload_dbr():
-        return gr.TextArea(visible=False)
-
-    def on_dbrtext(file):
-        if file:
-            return gr.TextArea(visible=True)
-        return gr.TextArea(visible=False)
-    
-    #Upload & clear business requirement
-    def load_dbr(file):
-        return file.decode()
-    
-    def unload_dbr():
-        return gr.TextArea(visible=False)
-
-    #Podcast upload progress
-    podcast.change(show_progress='full')
-   
-    #Togaf upload file
-    dbr_file.clear(unload_dbr,outputs=dbr_text)
-    dbr_file.change(on_dbrtext,inputs=dbr_file,outputs=dbr_text)
-    dbr_file.upload(load_dbr,inputs=dbr_file, outputs=dbr_text)
-    dbr_cls.click(off_dbrtext,outputs=[dbr_text, tabs_togaf, tab_diagram])
-
-    #Refactored ea4all_chatbot / vqa_chatbot (ChatInterface -> Chatbot)
-    qna_prompt.submit(run_qna_agentic_system,[qna_prompt,ea4all_chatbot],ea4all_chatbot)
-    qna_prompt.submit(lambda: "", None, [qna_prompt])
-    ea4all_chatbot.like(fn=get_user_feedback)
-    qna_examples.input(lambda value: value, qna_examples, qna_prompt)
-
-    #Execute Reference Architecture
-    dbr_run.click(run_reference_architecture_agentic_system,show_progress='full',inputs=[dbr_text],outputs=[togaf_vision,tabs_togaf,tabs_reference_architecture, architecture_runway, diagram_header, tab_diagram])
-
-    #vqa_chatbot (ChatInterface -> Chatbot)
-    def add_message(message, history):
-        if message["text"] is not None:
-            history.append({"role": "user", "content": message["text"]})
-        
-        if len(message['files']) > 0:
-            history.append({"role": "user", "content": {"path": message['files'][-1]}})
-        
-        return (
-            gr.MultimodalTextbox(value=message, interactive=True, placeholder="Upload a diagram and type your message..."),
-            history
-        )
-
-    chat_msg = vqa_prompt.submit(add_message, [vqa_prompt, ea4all_vqa], [vqa_prompt, ea4all_vqa])
-    bot_msg = chat_msg.then(run_vqa_agentic_system, [vqa_prompt, ea4all_vqa], ea4all_vqa, api_name="bot_response")
-    
-    ea4all_vqa.like(fn=get_user_feedback)
-    vqa_examples.input(lambda value: value, vqa_examples, vqa_prompt)
-
-    #Invoke CrewAI PMO Agentic System
-    pmo_prompt.submit(run_pmo_agentic_system,[pmo_prompt,pmo_chatbot],pmo_chatbot)
-    pmo_prompt.submit(lambda: "", None, [pmo_prompt])
-    pmo_examples.input(lambda value: value, pmo_examples, pmo_prompt)
-
-    #Set initial state of apm and llm
-    ea4all_mcp.load(ea4all_agent_init, outputs=[ea4all_agent_metadata,ea4all_chatbot, ea4all_vqa, pmo_chatbot, confluence_list, confluence_df, apm_df, pmo_df])

ea4all/ea4all_mcp.py

@@ -1,28 +1,655 @@
+#Added agentic-workflow-collaboration-agents
+#Multimodal ChatInterface - not working
+#Added new QA Tab
+#Added new agent Well-Architected
+#Added Supervisor Agent workflow
+#ISSUE with VQA fixed
+#LLMChain refactored
+#Updated with changes as result of ea4all_agent Gradio Space deployment issues 
+#VQA Safeguardings - JPEG, PNG images only
+#Deployed version to Live
+#Library import refactoring, ea4all-architecture, empty message
+#Bring your own IT Landscape data: discontinued 
+#Added upload your Business Requirement
+#Load user's APM - disabled 2024-06-22
+#TEST E2E Togaf Agentic system 2024-06-24
+#MIGRATION TO HF Open Source using TGI and Meta-Llama-3-8B-Instruct 2024-06-25
+#ADDED GENERATE_ARCHITECTURE_RUNWAY diagram: graphviz 2024-07-03
+#REFERENCE ARCHITECTURE DYNAMIC TABS 2024-07-05
+#ADDED Business Query grader 2024-07-07
+#RCA Togaf Supervisor: increase reliability 2024-07-08 - ISSUE FIXED BY NOW
+#EA4ALL-agentic-system-container updated 2024-07-10
+###APM Agentic system: 2024-07-25 - Safety check added
+##Sub-graph node stream 204-07-26
+# Stream arbitrary nested content: https://langchain-ai.github.io/langgraph/how-tos/streaming-content/
+## Prompt refinement task_router, user_question_routing, prompt_category 2024-07-27
+## WebSearch Hallucination issue - recursion looping - solution: routing to route_question 2024-07-28
+## Safety_check greetings, msgs, APM Sample Dataset 2024-09-29
+# VQA issue - image not recognised 2024-07-30
+# Constants IMAGES (Architecture, Overview) 2024-07-31
+# About, QnA Examples moved to mock files 2024-08-01 - deployed to build
+## 2024-08-03: VQA Streaming, Diagrams' EDGE nodes changed to END - one task at a time: 2024-08-03
+## VQA Llama-3.2-11B-Vision-Instruct 2024-10-25
+#RELEASE 2024-11-15
+## CHANGES 2024-11-22
+# MIGRATION to Gradio 5
+# Chatbot UI migrated to gr.Chatbot 
+# run_qna_agentic_system, run_vqa_agentic_system updated: ChatMessage, chat_memory, UI events
+# chat_memory VQA missing image - fixed - needs improvement
+## RELEASE 2024-11-23
+#pip freeze > requirements.txt to keep libraries synched local and HF Spaces
+#gr.Image issue: caused by __main__ root_path=str(Path.cwd())
+## RELEASE 2024-12-09
+#Confluence Integration
+#Llama-3.2-11B-Vision-Instruct max_token issue <=4096 stills
+#Safety-check refinement
+#TOGAF Vision streaming
+## Release update 2024-12-11
+#EA4ALL Podcast
+#2025-02-03 RELEASE V1
+##RETROFIT & INTEGRATION w/ EA4ALL-dev-studio-structure
+#2025-02-09 
+##UAT EA4ALL-LGS-RETRIEVER-REFACTORED
+#2025-03-10
+##AI-Assistant-UI-Message-Stream refactor
+#2025-12-04
+## Add EA4ALL-PMO-Demand-Management CrewAI Agents
+#2025-05-06
+## Add MCP Server
+#2025-05-17
+## Added PMO_MOCK_QNA examples, 
+## get_relevant_questions() - moved to utils, constants moved to configuration
+#2025-05-19
+## EA4ALL Diagram refactored, vqa_max_tokens updated
+from langchain.callbacks.tracers import LangChainTracer
+from langchain.callbacks.tracers.langchain import wait_for_all_tracers
+from langchain_community.document_loaders import ConfluenceLoader
+from langchain_core.messages import HumanMessage
+from langsmith import Client
+
+from ea4all.src.shared.configuration import BaseConfiguration, APM_MOCK_QNA, PMO_MOCK_QNA
+from ea4all.src.shared import vectorstore
+from ea4all.src.ea4all_gra.configuration import AgentConfiguration as gra
+from ea4all.src.ea4all_indexer.configuration import IndexConfiguration
+import ea4all.src.ea4all_apm.graph as e4a
+import ea4all.src.ea4all_vqa.graph as e4v
+import ea4all.src.ea4all_gra.graph as e4t
+import ea4all.src.shared.utils as e4u
+from ea4all.src.ea4all_indexer.graph import indexer_graph
+from ea4all.src.graph import super_graph
+#from ea4all.src.pmo_crew.crew_runner import run_pmo_crew
+
+import uuid
+import os
+import pandas as pd
+
 import gradio as gr
+from gradio import ChatMessage
+import time
+from PIL import Image
 
-def letter_counter(word, letter):
-    """
-    Count the number of occurrences of a letter in a word or text.
+#Set LangSmith project
+tracer = LangChainTracer(project_name=os.getenv('LANGCHAIN_PROJECT'))
+
+config = {
+    "run_name": os.getenv('LANGCHAIN_RUNNAME'), 
+    "tags": [os.getenv('EA4ALL_ENV')],
+    "callbacks":[tracer],
+    "recursion_limit": 25,
+    "configurable": {
+        "thread_id": uuid.uuid4(),
+    },
+    "stream_mode": "messages"
+}
 
-    Args:
-        word (str): The input text to search through
-        letter (str): The letter to search for
+#Blocks w/ ChatInterface, BYOD, About
+with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as ea4all_mcp:
 
-    Returns:
-        str: A message indicating how many times the letter appears
+    agentic_pmo_desc="""
+        Hi, 
+        Provide project resource estimation for architecture work based on business requirements, skillset, 
+        architects allocation, and any other relevant information to enable successful project solution delivery."""
+    
+    agentic_qna_desc="""
+        Hi,
+        improve effieciency, knowledge sharing, and get valuable insights from your IT landscape using natural language.
+        As an Enterprise Architect Agentic System I can answer questions related to Enterprise Architecture, Technology, plus the following IT Landscape sample dataset: """
+
+    agentic_vqa_desc="""
+    Hi, talk to your Architecture Diagram using natural language. Gain rapid knowledge and insights translating image to meaningful description.
+    **Disclaimer**:
+    - This feature should NOT BE USED to process inappropriate content, but ONLY FOR Architecture Diagrams
+    """
+
+    agentic_togaf_desc="""
+        Hi,
+        in a click of button create a reference architecture that serves as a blueprint for designing and implementing IT solutions.
+        Standardise, increase efficiency and productivity to architecture solution development.
+        Generate context-specific reference and minimal viable architectures to support business and IT strategy and digital transformation.
+        Streamline the architecture operating model, taking the best of agentic workflows and architects working together.
     """
-    word = word.lower()
-    letter = letter.lower()
-    count = word.count(letter)
-    return count
-
-demo = gr.Interface(
-    fn=letter_counter,
-    inputs=["textbox", "textbox"],
-    outputs="number",
-    title="Letter Counter",
-    description="Enter text and a letter to count how many times the letter appears in the text."
-)
-
-if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    
+        #ea4all-about
+    
+    def ea4all_about():
+        readme = e4u.load_mock_content(e4u.CFG.EA4ALL_ABOUT)
+        return readme
+   
+    #Load demo business requirements
+    def init_dbr():
+        # Open the file in read mode ('r')
+        with open(e4u._join_paths(BaseConfiguration.ea4all_store, gra.dbr_mock), 'r') as file:
+            # Read the contents of the file
+            contents = file.read()
+        return contents
+
+    def init_df():
+        return vectorstore.apm_dataframe_loader(e4u._join_paths(BaseConfiguration.ea4all_store, IndexConfiguration.apm_catalogue))
+    
+    #load core-architecture image
+    #fix the issue with gr.Image(path) inside a docker containder
+    def get_image(_image):
+        #from PIL import Image
+        # Load an image
+        image = e4u._join_paths(BaseConfiguration.ea4all_images,_image)
+        print(f"Full path: {image}")
+
+        return image
+
+    #ea4all-qna-agent-conversational-with-memory
+    async def run_qna_agentic_system(prompt, chat_memory, request:gr.Request):
+        
+        format_response = ""
+
+        if not prompt:
+            format_response = "Hi, how are you today? To start our conversation, please chat your message!"
+            chat_memory.append(ChatMessage(role="assistant", content=format_response))
+            yield chat_memory
+
+        if not chat_memory:
+            chat_memory.append(ChatMessage(role="user", content=prompt))
+            yield chat_memory
+
+        if prompt:
+            #capture user ip
+            ea4all_user = e4u.get_user_identification(request)
+
+            ##Initialise APM Graph
+            #apm_graph = e4a.apm_graph
+            #inputs = {"question": prompt, "chat_memory":chat_memory}
+            inputs = {"messages": [{"role": "user", "content": prompt}]}
+            
+            #add prompt to memory
+            chat_memory.append(ChatMessage(role="user", content=prompt))
+
+            partial_message = ""
+            async for event in super_graph.astream_events(input=inputs, config=config, version="v2"):
+            #async for event in super_graph.astream(input=inputs, config=config, subgraphs=True):
+             #   chat_memory.append(ChatMessage(role="assistant", content=str(event)))
+             #   yield chat_memory
+
+                kind = event["event"]
+                tags = event.get("tags", [])
+                name =  event['name']
+                
+                #chat_memory.append(ChatMessage(role="assistant", content=f"Running: {name}"))
+                #yield chat_memory
+
+                if name == "safety_check":
+                    #if kind == "on_chain_start":
+                    #    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}`"))
+                    #    yield chat_memory
+                    if kind == "on_chain_stream":
+                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}`: {event['data']['chunk']['safety_status'][0]}"))  
+                        if event['data']['chunk']['safety_status'][0] == 'no': 
+                            chat_memory.append(ChatMessage(role="assistant", content=f"Safety-status: {event['data']['chunk']['safety_status'][1]}"))
+                        yield chat_memory
+                if kind == "on_chain_end" and name == "route_question":
+                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}:` {event['data']['output']['source']}"))
+                    yield chat_memory
+                if kind == "on_chain_start" and name == "retrieve":
+                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}` RAG\n\n"))
+                    yield chat_memory
+                if kind == "on_chain_start" and name in ("generate_web_search", "websearch", "stream_generation"):
+                    chat_memory.append(ChatMessage(role="assistant", content= f"\n\n- `{name}`\n\n"))
+                    yield chat_memory
+                if kind == "on_chain_stream" and name == "stream_generation":
+                    data = event["data"]
+                    # Accumulate the chunk of data
+                    partial_message += data['chunk']
+                    chat_memory[-1].content = partial_message
+                    time.sleep(0.05)
+                    yield chat_memory
+                if name == "grade_generation_v_documents_and_question":
+                    if kind == "on_chain_start": 
+                        chat_memory.append(ChatMessage(role="assistant", content=f"\n\n- `{name}`: "))
+                        yield chat_memory
+                    if kind == "on_chain_end":
+                        chat_memory.append(ChatMessage(role="assistant", content=f"`{event['data']['input'].source}:` {event['data']['output']}"))
+                        yield chat_memory
+                if "stream_hallucination" in tags and kind == "on_chain_start":
+                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{tags[-1]}`"))
+                    yield chat_memory
+                if "stream_grade_answer" in tags and kind == "on_chain_start":
+                    chat_memory.append(ChatMessage(role="assistant", content=f"- `{tags[-1]}`"))
+                    yield chat_memory
+                if name == "supervisor":
+                    if kind == "on_chain_start":
+                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}` "))
+                        yield chat_memory
+                    if kind == "on_chain_stream":
+                        chat_memory.append(ChatMessage(role="assistant", content=f"{event['data']['chunk']}"))
+                        yield chat_memory
+
+            os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
+            wait_for_all_tracers()
+
+    #Trigger Solution Architecture Diagram QnA
+    async def run_vqa_agentic_system(message, chat_memory, request:gr.Request):
+        #capture user ip
+        ea4all_user = e4u.get_user_identification(request)
+
+        """Handle file uploads and validate their types."""
+        allowed_file_types = ('JPEG', 'PNG')
+
+        print("---CALLING VISUAL QUESTION ANSWERING AGENTIC SYSTEM---")
+        print(f"Prompt: {message}")
+
+        if message['files'] == []:
+            chat_memory.append(ChatMessage(role="assistant", content="Please upload an Architecture PNG, JPEG diagram to start!"))
+            yield chat_memory
+        else:
+            diagram = message['files'][-1] ##chat_memory[-1]['content'][-1]        
+            msg = message['text'] ##chat_memory[-2]['content']
+            print(f"---DIAGRAM: {diagram}---")
+            try:
+                if msg == "":
+                    msg = "Please describe this diagram."
+
+                with Image.open(diagram) as diagram_:
+                    if diagram_.format not in allowed_file_types:
+                        chat_memory.append(ChatMessage(role="assistant", content="Invalid file type. Allowed file types are JPEG and PNG."))
+                        yield chat_memory
+                    else:
+                        #'vqa_image = e4u.get_raw_image(diagram) #MOVED into Graph
+                        vqa_image = diagram
+
+                        #Setup Quality Assurance Agentic System
+                        #graph = e4v.ea4all_graph(config['configurable']['vqa_model'])
+
+                        #Setup enter graph
+                        diagram_graph = e4v.diagram_graph
+
+                        partial_message = ""
+                        chat_memory.append(ChatMessage(role="assistant", content="Hi, I am working on your question..."))
+                        async for event in diagram_graph.astream_events(
+                            {"question":msg, "image": vqa_image}, config, version="v2"
+                        ):
+                            if (
+                                    event["event"] == "on_chat_model_stream"
+                                    and "vqa_stream" in event['tags']
+                                    #and event["metadata"].get("langgraph_node") == "tools"
+                                ):
+                                    partial_message += event["data"]["chunk"].content
+                                    chat_memory[-1].content = partial_message
+                                    time.sleep(e4u.CFG.STREAM_SLEEP)
+                                    yield chat_memory #, message to update prompt
+                            elif not partial_message:
+                                yield chat_memory #, message
+                        
+                        os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
+                        wait_for_all_tracers()
+
+            except Exception as e:
+                yield (e.args[-1])
+
+    #Run Togaf Agentic System
+    async def run_reference_architecture_agentic_system(business_query, request:gr.Request):
+
+        if len(business_query) < 50:
+            agent_response = "Please provide a valid Business Requirement content to start!"
+            yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
+        else: 
+            plain_text = e4u.markdown_to_plain_text(business_query)
+            agent_response = "Generating Architecture Blueprint ---TOGAF VISION TARGET--- \n\nI am working on your request..."
+            togaf_chain = e4t.togaf_graph
+            final_diagram = ""
+            vision_message = ""
+            try:
+                async for s in togaf_chain.astream_events(
+                    {
+                        "messages": [
+                            HumanMessage(
+                                content=plain_text
+                            )
+                        ],
+                        "business_query": business_query,
+                    },
+                    config=config,
+                    version="v2"
+                ):    
+                    kind = s["event"]
+                    tags = s.get("tags", [])
+                    name = s['name']
+
+                    if "gra_stream" in tags and name == "stream_vision_target":
+                        if kind == "on_chain_stream":
+                            data = s["data"]
+                            # Accumulate the chunk of data
+                            vision_message += data['chunk'].content
+                            time.sleep(e4u.CFG.STREAM_SLEEP)
+                            yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
+                    elif name == "save_diagram" and kind == 'on_chain_end': #MOVED INTO Togaf_Task3
+                        final_diagram = s['data']['output']['architecture_runway']
+                    elif ("assess_business_query" in tags or "assess_landscape" in tags) and kind == 'on_chain_start': ##'on_chat_model_stream':
+                        agent_response += f"\n\n`{tags[-1]}:{name}`"
+
+                        yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
+                    
+                if vision_message=="":
+                    agent_response = "I cannot generate the Architecture Vision. Please provide a valid Business Requirement content to start!"
+                    yield([agent_response,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, None, gr.Tabs(visible=False)])
+                elif "Error" not in final_diagram:
+                    yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),final_diagram, None, gr.Tabs(visible=True)])
+                else:
+                    yield([vision_message,gr.Tabs(visible=True), gr.Tabs(selected="id_togaf"),None, final_diagram, gr.Tabs(visible=True)])
+
+            except Exception as e:  
+                yield(
+                    [
+                        e.args[-1],
+                        gr.Tabs(visible=True), 
+                        gr.Tabs(selected="id_togaf"),
+                        None,
+                        None,
+                        gr.Tabs(visible=False)
+                    ]
+                )
+
+    async def run_pmo_agentic_system(prompt, chat_memory):
+        """
+        Answer a question about Project Portfolio Management and Architect Demand Management.
+
+        Args:
+            prompt (str): The propject portfolio user question
+            chat_memory (list): The tool message history
+
+        Returns:
+            str: A summary answering the user question
+        """
+        format_response = ""
+
+        if not prompt:
+            format_response = "Hi, how are you today? To start our conversation, please chat your message!"
+            chat_memory.append(ChatMessage(role="assistant", content=format_response))
+            yield chat_memory
+
+        if not chat_memory:
+            chat_memory.append(ChatMessage(role="user", content=prompt))
+            yield chat_memory
+
+        inputs = {
+            "question": prompt,
+            "verbose": True,  # optional flags
+        }
+        
+        #yield run_pmo_crew(inputs)
+
+    def ea4all_confluence():
+
+        #Confluence API Key
+        confluence_api_key = os.environ['CONFLUENCE_API_KEY']
+
+        loader = ConfluenceLoader(
+            url="https://learnitall.atlassian.net/wiki", username="learn-it-all@outlook.com", api_key=confluence_api_key,
+            space_key="~71202000cd55f36336455f8c07afa1860ba810", 
+            include_attachments=False, limit=10,
+            keep_markdown_format=True
+        )
+
+        documents = loader.load()
+
+        data = {
+                "title": [doc.metadata["title"] for doc in documents],
+                "source": [doc.metadata["source"] for doc in documents],
+                "page_content": [doc.page_content for doc in documents],
+            }
+
+        df = pd.DataFrame(data)
+
+        return df        
+
+    def filter_page(page_list, title):
+        x =  page_list[page_list["title"] == title]
+        return x.iloc[0]['page_content']
+
+    #EA4ALL-Agentic system menu
+    with gr.Tabs(selected="how_to") as tabs:
+        with gr.Tab(label="Architect Demand Management"):
+            with gr.Tab(label="Architect Project Planning", id="pmo_qna_1"):
+                    ea4all_pmo_description = gr.Markdown(value=agentic_pmo_desc)
+                    pmo_chatbot = gr.Chatbot(label="EA4ALL your AI Architect Companion", type="messages")
+                    pmo_prompt = gr.Textbox(lines=1, show_label=False, max_lines=1, submit_btn=True, stop_btn=True,autofocus=True, placeholder="Type your message here or select an example...")
+                    with gr.Accordion("Open for prompt examples", open=False):
+                        pmo_examples = gr.Dropdown(e4u.load_mock_content(PMO_MOCK_QNA), value=None,label="Questions", interactive=True)
+                    gr.ClearButton([pmo_chatbot,pmo_prompt], value="Clear", size="sm", visible=False)
+            with gr.Tab(label="Project Portfolio Sample Dataset", id="id_pmo_ds"):
+                    pmo_df = gr.Dataframe()
+        with gr.Tab(label="Application Landscape QnA"):
+            with gr.Tabs() as tabs_apm_qna:
+                with gr.Tab(label="Connect, Explore, Together", id="app_qna_1"):
+                    ea4all_agent_metadata = gr.Markdown(value=agentic_qna_desc)
+                    ea4all_chatbot = gr.Chatbot(label="EA4ALL your AI Architect Companion", type="messages")
+                    qna_prompt = gr.Textbox(lines=1, show_label=False, max_lines=1, submit_btn=True, stop_btn=True,autofocus=True, placeholder="Type your message here or select an example...")
+                    with gr.Accordion("Open for prompt examples", open=False):
+                        qna_examples = gr.Dropdown(e4u.load_mock_content(APM_MOCK_QNA), value=None,label="Questions", interactive=True)
+                    gr.ClearButton([ea4all_chatbot,qna_prompt], value="Clear", size="sm", visible=False)
+                with gr.Tab(label="Sample Dataset", id="id_apm_ds"):
+                    apm_df = gr.Dataframe()
+        with gr.Tab(label="Diagram Question and Answering"):
+            gr.Markdown(value=agentic_vqa_desc)
+            ea4all_vqa = gr.Chatbot(label="EA4ALL your AI Multimodal Architect Companion", type="messages")
+            vqa_prompt = gr.MultimodalTextbox(interactive=True, show_label=False, submit_btn=True, stop_btn=True, autofocus=True, placeholder="Upload your diagram and type your message or select an example...")
+            with gr.Accordion("Open for prompt examples", open=False):
+                vqa_examples = gr.Dropdown(e4u.get_vaq_examples(), value=None,label="Diagram and Questions", interactive=True)
+            gr.ClearButton([ea4all_vqa,vqa_prompt,vqa_examples], value="Clear", size="sm", visible=True)
+        with gr.Tab(label="Reference Architecture", id="id_refarch"):
+            with gr.Tabs(selected="id_dbr") as tabs_reference_architecture:
+                with gr.Tab(label='Business Requirement', id="id_dbr"):
+                    gr.Markdown(value=agentic_togaf_desc)
+                    dbr_text=gr.TextArea(value=init_dbr, lines=14, interactive=True)
+                    with gr.Row():
+                        dbr_file=gr.File(
+                            value=e4u._join_paths(BaseConfiguration.ea4all_store, gra.dbr_mock),
+                            label="Business Requirement", 
+                            height=35, 
+                            show_label=False, 
+                            file_count="single", 
+                            file_types=['text'], 
+                            interactive=True,
+                            type='binary'
+                        )
+                        dbr_run=gr.Button(scale=None,value="Run Reference Architecture")
+                        dbr_cls=gr.ClearButton([dbr_file,dbr_text])
+                with gr.Tab(label='Confluence Integration', id="id_confluence"):
+                    confluence_list = gr.Dropdown(value=None, label="Confluence Pages", interactive=True)
+                    confluence_df = gr.DataFrame(visible=False, headers=["title", "source", "page_content"])  
+                    @gr.render(inputs=[confluence_list,confluence_df])
+                    def show_page(page, df):
+                        if page:
+                            with gr.Row():
+                                with gr.Column():
+                                    dbr_confluence =gr.Button(scale=None,value="Run Reference Architecture")
+                                with gr.Column():
+                                    btn=gr.Button("Clear")
+                            with gr.Row(variant='default', show_progress=True):
+                                page_content = gr.Markdown(filter_page(df,page), line_breaks=True)
+                                btn.click(lambda: gr.Dropdown(value=None), None, confluence_list)
+                                dbr_confluence.click(run_reference_architecture_agentic_system,show_progress='full', inputs=[page_content],outputs=[togaf_vision,tabs_togaf,tabs_reference_architecture, architecture_runway, diagram_header, tab_diagram])
+                with gr.Tab(label='Reference Architecture',visible=False, id="id_togaf") as tabs_togaf:
+                    togaf_vision=gr.Markdown(value='### Reference Architecture: Vision and Target')
+                with gr.Tab(label="Target Architecture Runway",visible=False, id="id_runway") as tab_diagram:
+                    diagram_header=gr.Markdown(visible=True)
+                    architecture_runway=gr.Image(label="Target Architecture Runway",interactive=False,visible=True, scale=10)
+        with gr.Tab(label="Overview", id="how_to"):
+            gr.Markdown(
+                """
+                    # Title
+        
+                    **Explore, Share, Together:** harness the value of `Enterprise Architecture in the era of Generative AI` to positively impact individuals and organisations.\n
+
+                    ## Overview
+                """
+            ),
+            gr.Image(
+                get_image(e4u.CFG.EA4ALL_ARCHITECTURE),
+                show_download_button=False,
+                container=False,
+                show_share_button=False,
+                )
+            gr.Markdown(
+                """
+                    ## Journey
+
+                    Audio overview summarising the key learnings, challenges, so what, stats from day-1 to last sprint. (**Powered by Google NoteBookLM**)
+
+                """
+            )
+            podcast = gr.Audio(
+                type="filepath",
+                value=os.path.join(BaseConfiguration.ea4all_store,e4u.CFG.EA4ALL_PODCAST), 
+                label="EA4ALL Journey Podcast", 
+                show_download_button=False,
+                autoplay=False,
+                container=True,
+                interactive=False,
+            )
+            gr.Markdown(ea4all_about)
+
+    #get LLM response user's feedback
+    def get_user_feedback(evt: gr.SelectData, request:gr.Request):
+        ##{evt.index} {evt.value} {evt._data['liked']}
+        try:     
+            uuid_str = os.environ["EA4ALL_" + e4u.get_user_identification(request).replace(".","_")]
+            gr.Info("Thanks for your feedback - run_id: " + uuid_str)
+            run_id = uuid.UUID(uuid_str)
+            client = Client()
+            client.create_feedback(
+                run_id,
+                key="feedback-key",
+                score= 1.0 if evt._data['liked'] == True else 0,
+                comment=str(evt.value)
+            )
+        except Exception as e:
+            gr.Warning(f"Couldn't capture a feedback: {e}")
+
+    #Set initial state of apm, llm and capture user-ip
+    async def ea4all_agent_init(request:gr.Request):
+
+        #capture user IP address
+        #ea4all_user = e4u.get_user_identification(request)
+        gr.Info("Thank you for connecting! I'd love to hear your feedback! Thumbs up or Thumbs down. LinkedIn comment.")
+
+        # Set initial landscape vectorstore
+
+        await indexer_graph.ainvoke(input={"docs":[]}, config=config)
+
+        #set chatbot description w/ user apm columns
+        df = vectorstore.apm_dataframe_loader(e4u._join_paths(BaseConfiguration.ea4all_store, IndexConfiguration.apm_catalogue))
+        columns_string = ', '.join(df.columns)
+        apm_columns = agentic_qna_desc + columns_string
+
+        prompt=ChatMessage(role='assistant', content='Hi, I am your Architect Copilot! How can I help you today?')
+
+        page_list = ea4all_confluence()
+
+        #Load gradio.dataframe with Portfolio sample dataset
+        pmo_df = pd.read_csv("ea4all/ea4all_store/ea4all-portfolio-management.csv")
+
+        return (
+            apm_columns, 
+            [prompt], 
+            [prompt],
+            [prompt],
+            gr.Dropdown(choices=page_list['title'].values.tolist()), 
+            gr.DataFrame(value=page_list), 
+            gr.DataFrame(value=df),
+            gr.DataFrame(value=pmo_df),
+        )
+
+    #authentication
+    def ea4all_login(username, password):
+        return (username==password)
+
+    #TABS & Reference Architecture look-and-feel control
+    def off_dbrtext():
+        return gr.TextArea(visible=False), gr.Tab(visible=False), gr.Tab(visible=False)
+
+    def on_dbrtext(file):
+        if file:
+            return gr.TextArea(visible=True)
+        return gr.TextArea(visible=False)
+    
+    #Upload & clear business requirement
+    def load_dbr(file):
+        return file.decode()
+    
+    def unload_dbr():
+        return gr.TextArea(visible=False)
+
+    def on_dbrtext(file):
+        if file:
+            return gr.TextArea(visible=True)
+        return gr.TextArea(visible=False)
+    
+    #Upload & clear business requirement
+    def load_dbr(file):
+        return file.decode()
+    
+    def unload_dbr():
+        return gr.TextArea(visible=False)
+
+    #Podcast upload progress
+    podcast.change(show_progress='full')
+   
+    #Togaf upload file
+    dbr_file.clear(unload_dbr,outputs=dbr_text)
+    dbr_file.change(on_dbrtext,inputs=dbr_file,outputs=dbr_text)
+    dbr_file.upload(load_dbr,inputs=dbr_file, outputs=dbr_text)
+    dbr_cls.click(off_dbrtext,outputs=[dbr_text, tabs_togaf, tab_diagram])
+
+    #Refactored ea4all_chatbot / vqa_chatbot (ChatInterface -> Chatbot)
+    qna_prompt.submit(run_qna_agentic_system,[qna_prompt,ea4all_chatbot],ea4all_chatbot)
+    qna_prompt.submit(lambda: "", None, [qna_prompt])
+    ea4all_chatbot.like(fn=get_user_feedback)
+    qna_examples.input(lambda value: value, qna_examples, qna_prompt)
+
+    #Execute Reference Architecture
+    dbr_run.click(run_reference_architecture_agentic_system,show_progress='full',inputs=[dbr_text],outputs=[togaf_vision,tabs_togaf,tabs_reference_architecture, architecture_runway, diagram_header, tab_diagram])
+
+    #vqa_chatbot (ChatInterface -> Chatbot)
+    def add_message(message, history):
+        if message["text"] is not None:
+            history.append({"role": "user", "content": message["text"]})
+        
+        if len(message['files']) > 0:
+            history.append({"role": "user", "content": {"path": message['files'][-1]}})
+        
+        return (
+            gr.MultimodalTextbox(value=message, interactive=True, placeholder="Upload a diagram and type your message..."),
+            history
+        )
+
+    chat_msg = vqa_prompt.submit(add_message, [vqa_prompt, ea4all_vqa], [vqa_prompt, ea4all_vqa])
+    bot_msg = chat_msg.then(run_vqa_agentic_system, [vqa_prompt, ea4all_vqa], ea4all_vqa, api_name="bot_response")
+    
+    ea4all_vqa.like(fn=get_user_feedback)
+    vqa_examples.input(lambda value: value, vqa_examples, vqa_prompt)
+
+    #Invoke CrewAI PMO Agentic System
+    pmo_prompt.submit(run_pmo_agentic_system,[pmo_prompt,pmo_chatbot],pmo_chatbot)
+    pmo_prompt.submit(lambda: "", None, [pmo_prompt])
+    pmo_examples.input(lambda value: value, pmo_examples, pmo_prompt)
+
+    #Set initial state of apm and llm
+    ea4all_mcp.load(ea4all_agent_init, outputs=[ea4all_agent_metadata,ea4all_chatbot, ea4all_vqa, pmo_chatbot, confluence_list, confluence_df, apm_df, pmo_df])