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

.python-version

@@ -0,0 +1 @@
+3.12

ea4all/ea4all_mcp.py

@@ -67,6 +67,7 @@ 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 langchain_core.runnables import RunnableConfig
 
 from ea4all.src.shared.configuration import BaseConfiguration, APM_MOCK_QNA, PMO_MOCK_QNA
 from ea4all.src.shared import vectorstore
@@ -82,25 +83,35 @@ from ea4all.src.graph import super_graph
 
 import gradio as gr
 from gradio import ChatMessage
+import os
+import uuid
+import time
+from PIL import Image
 
 from utils.utils import (
     get_user_feedback, add_message, unload_dbr, on_dbrtext, load_dbr, off_dbrtext,
-    ea4all_agent_init, get_image, filter_page, ea4all_about, init_dbr, Client, pd
+    ea4all_agent_init, get_image, filter_page, ea4all_about, init_dbr
 )
 
+TITLE =  """
+    # Title
+        
+    **Explore, Share, Together:** harness the value of `Enterprise Architecture in the era of Generative AI` to positively impact individuals and organisations.\n
+
+    ## Overview
+    """
+
 #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"
-}
+config = RunnableConfig(
+        run_name = os.getenv('LANGCHAIN_RUNNAME', "ea4all-mcp"), 
+        tags = [os.getenv('EA4ALL_ENV', "MCP")],
+        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:
@@ -173,12 +184,18 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                     #    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]}"))
+                        chunk = event['data'].get('chunk')
+                        if chunk and 'safety_status' in chunk and len(chunk['safety_status']) > 0:
+                            chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}`: {chunk['safety_status'][0]}"))
+                            if chunk['safety_status'][0] == 'no' and len(chunk['safety_status']) > 1:
+                                chat_memory.append(ChatMessage(role="assistant", content=f"Safety-status: {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']}"))
+                    output = event['data'].get('output')
+                    if output and 'source' in output:
+                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}:` {output['source']}"))
+                    else:
+                        chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}:` (no source available)"))
                     yield chat_memory
                 if kind == "on_chain_start" and name == "retrieve":
                     chat_memory.append(ChatMessage(role="assistant", content=f"- `{name}` RAG\n\n"))
@@ -189,7 +206,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                 if kind == "on_chain_stream" and name == "stream_generation":
                     data = event["data"]
                     # Accumulate the chunk of data
-                    partial_message += data['chunk']
+                    partial_message += data.get('chunk', '')
                     chat_memory[-1].content = partial_message
                     time.sleep(0.05)
                     yield chat_memory
@@ -198,7 +215,12 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                         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']}"))
+                        input_data = event['data'].get('input')
+                        if input_data and hasattr(input_data, 'source'):
+                            output_value = event['data'].get('output', '')
+                            chat_memory.append(ChatMessage(role="assistant", content=f"`{input_data.source}:` {output_value}"))
+                        else:
+                            chat_memory.append(ChatMessage(role="assistant", content=f"`{event['data'].get('output', '')}`"))
                         yield chat_memory
                 if "stream_hallucination" in tags and kind == "on_chain_start":
                     chat_memory.append(ChatMessage(role="assistant", content=f"- `{tags[-1]}`"))
@@ -211,10 +233,14 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                         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
+                        chunk = event['data'].get('chunk')
+                        if chunk is not None:
+                            chat_memory.append(ChatMessage(role="assistant", content=f"{chunk}"))
+                            yield chat_memory
 
-            os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
+                # Set environment variable only when 'event' is defined
+                os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
+            
             wait_for_all_tracers()
 
     #Trigger Solution Architecture Diagram QnA
@@ -260,17 +286,20 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                         ):
                             if (
                                     event["event"] == "on_chat_model_stream"
-                                    and "vqa_stream" in event['tags']
+                                    and "vqa_stream" in event.get('tags', [])
                                     #and event["metadata"].get("langgraph_node") == "tools"
                                 ):
-                                    partial_message += event["data"]["chunk"].content
-                                    chat_memory[-1].content = partial_message
+                                    chunk = event["data"].get("chunk")
+                                    if chunk is not None and hasattr(chunk, "content"):
+                                        partial_message += 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'])
+                            os.environ["EA4ALL_" + ea4all_user.replace(".", "_")] = str(event['run_id'])
+                        
                         wait_for_all_tracers()
 
             except Exception as e:
@@ -309,11 +338,14 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                         if kind == "on_chain_stream":
                             data = s["data"]
                             # Accumulate the chunk of data
-                            vision_message += data['chunk'].content
+                            chunk = data.get('chunk')
+                            if chunk is not None and hasattr(chunk, "content"):
+                                vision_message += 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']
+                        output = s['data'].get('output', {})
+                        final_diagram = output.get('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}`"
 
@@ -376,7 +408,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                     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)
+                        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()
@@ -387,7 +419,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                     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)
+                        qna_examples = gr.Dropdown(e4u.get_relevant_questions(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()
@@ -396,7 +428,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
             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)
+                vqa_examples = gr.Dropdown(e4u.get_vqa_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:
@@ -437,15 +469,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                     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.Markdown(value=TITLE)
             gr.Image(
                 get_image(e4u.CFG.EA4ALL_ARCHITECTURE),
                 show_download_button=False,

ea4all/src/shared/utils.py

@@ -233,7 +233,7 @@ def get_history_gradio(history, chat_history=[]):
     return history
 
 #retrieve relevant questions based on user interaction
-def get_vaq_examples():
+def get_vqa_examples() -> list:
     examples=[
         {"text": "Describe this image.", "files": ["ea4all/images/multi-app-architecture.png"]},
         {"text": "Assess any risk and vulnerabilities in the current solution.", "files": ["ea4all/images/ea4all_architecture.png"]},
@@ -314,13 +314,13 @@ def get_raw_image(image_path):
 
     return raw_image
 
-def load_mock_content(file_path):
+def load_mock_content(file_path) -> str:
     try:
         with open(_join_paths(ea4all_config.ea4all_store,file_path), "r") as file:
             content = file.read()
             return content
     except ValueError as e:
-        return e
+        return str(e)
     
 def print_json_to_md(data, indent=0, column=None):
     try:
@@ -476,3 +476,11 @@ def extract_detailed_business_requirements(llm, topic: type[BaseModel], name:str
 # Post-processing
 def format_docs(docs):
     return "\n".join(doc.page_content for doc in docs)
+
+#load mock data
+def get_relevant_questions(source: str) -> list:
+    relevant_questions = []
+    mock = load_mock_content(source)
+    for line in mock.splitlines(): relevant_questions += [line]
+
+    return relevant_questions

pyproject.toml

@@ -0,0 +1,7 @@
+[project]
+name = "ea4all-gradio-agent-mcp-hackathon"
+version = "0.1.0"
+description = "EA4ALL Agentic System MCP Server"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = []