ea4all-gradio-agents-mcp-hackathon-ui-retrofit

ea4all/__main__.py

@@ -10,6 +10,7 @@ def main() -> None:
             debug=os.getenv("GRADIO_DEBUG", "True").lower() in ("true", "1", "yes"),
             ssr_mode=False,
             mcp_server=True,
+            inbrowser=os.getenv("GRADIO_INBROWSER", "True").lower() in ("true", "1", "yes"),
         )
     except Exception as e:
         print(f"Error loading: {e}")

ea4all/ea4all_mcp.py

@@ -1,68 +1,7 @@
-#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
 #2025-06-04
 ## Gradio Agents MCP Hackathon: retrofit to expose EA4ALL Agentic System Agents only
+## Greetings message not working
+## UI exposing too much tools, need to be refactored
 from langchain.callbacks.tracers import LangChainTracer
 from langchain.callbacks.tracers.langchain import wait_for_all_tracers
 from langchain_core.messages import HumanMessage
@@ -399,14 +338,14 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
     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.get_relevant_questions(PMO_MOCK_QNA), value=None,label="Questions", interactive=True)
-                    gr.ClearButton([pmo_chatbot,pmo_prompt], value="Clear", size="sm", visible=False)
+                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.get_relevant_questions(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()
+                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/utils/utils.py

@@ -120,9 +120,9 @@ async def ea4all_agent_init(request:gr.Request):
 
     return (
         apm_columns, 
-        [prompt], 
-        [prompt],
-        [prompt],
+        [{"role": "system", "content": "You are a helpful assistant."}],
+        [{"role": "system", "content": "You are a helpful assistant."}],
+        [{"role": "system", "content": "You are a helpful assistant."}],
         gr.Dropdown(choices=page_list['title'].values.tolist()), 
         gr.DataFrame(value=page_list), 
         gr.DataFrame(value=df),