ea4all-gradio-agents-mcp-hackathon-tools-refactoring-vqa

ea4all/__main__.py

@@ -11,6 +11,8 @@ def main() -> None:
             ssr_mode=False,
             mcp_server=True,
             inbrowser=os.getenv("GRADIO_INBROWSER", "True").lower() in ("true", "1", "yes"),
+            auth=("ea4all", "ea4a@@"),
+            auth_message="Please login with your credentials. Under development, will be public soon.",
         )
     except Exception as e:
         print(f"Error loading: {e}")

ea4all/ea4all_mcp.py

@@ -49,19 +49,19 @@ config = RunnableConfig(
 )
 
 #ea4all-qna-agent-conversational-with-memory
-async def run_qna_agentic_system(question: str, chat_memory: list) -> AsyncGenerator[list, None]:
+async def run_qna_agentic_system(question: str) -> AsyncGenerator[list, None]:
     """
     description: 
         Handles conversational Q&A for the Application Landscape using an agentic system.
     Args:
         question (str): The user's question or message.
-        chat_memory (list): The conversation history.
         request (gr.Request): The Gradio request object for user identification.
     Returns:
         reponse: Response to user's architectural question.
     """
 
     format_response = ""
+    chat_memory = []
     if not question:
         format_response = "Hi, how are you today? To start our conversation, please chat your message!"
         chat_memory.append(ChatMessage(role="assistant", content=format_response))
@@ -161,14 +161,13 @@ async def run_qna_agentic_system(question: str, chat_memory: list) -> AsyncGener
         wait_for_all_tracers()
 
 #Trigger Solution Architecture Diagram QnA
-async def run_vqa_agentic_system(question: str, diagram: str, chat_memory: list, request: gr.Request) -> AsyncGenerator[list, None]:
+async def run_vqa_agentic_system(question: str, diagram: str, request: gr.Request) -> AsyncGenerator[list, None]:
     """
     description: 
         Handles Visual Question Answering (VQA) for uploaded architecture diagrams.
     Args:
         question (str): User's question about the Architecture Diagram.
         diagram (str): Path to the diagram file.
-        chat_memory: The conversation history.
     Returns:
         response: Response to user's question.
     """
@@ -187,6 +186,7 @@ async def run_vqa_agentic_system(question: str, diagram: str, chat_memory: list,
     print("---CALLING VISUAL QUESTION ANSWERING AGENTIC SYSTEM---")
     print(f"Prompt: {message}")
 
+    chat_memory = []
     if message['files'] == []:
         chat_memory.append(ChatMessage(role="assistant", content="Please upload an Architecture PNG, JPEG diagram to start!"))
         yield chat_memory
@@ -312,7 +312,7 @@ async def run_reference_architecture_agentic_system(business_query: str) -> Asyn
                 ]
             )
 
-async def run_pmo_agentic_system(question:str, chat_memory: list) -> AsyncGenerator[list, None]:
+async def run_pmo_agentic_system(question:str) -> AsyncGenerator[list, None]:
     """
     description: 
         Answers questions about Project Portfolio Management and Architect Demand Management.
@@ -324,6 +324,7 @@ async def run_pmo_agentic_system(question:str, chat_memory: list) -> AsyncGenera
     """
 
     format_response = ""
+    chat_memory = []
     if not question:
         format_response = "Hi, how are you today? To start our conversation, please chat your message!"
         chat_memory.append(ChatMessage(role="assistant", content=format_response))
@@ -376,7 +377,11 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
         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_chatbot = gr.Chatbot(
+                    label="EA4ALL your AI Demand Management Architect Companion", type="messages",
+                    max_height=160,
+                    layout="bubble",
+                )
                 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 question examples", open=False):
                     pmo_examples = gr.Dropdown(e4u.get_relevant_questions(PMO_MOCK_QNA), value=None,label="Questions", interactive=True)
@@ -387,7 +392,11 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
             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")
+                    ea4all_chatbot = gr.Chatbot(
+                        label="EA4ALL your AI Landscape Architect Companion", type="messages",
+                        max_height=160,
+                        layout="bubble",
+                    )
                     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 question examples", open=False):
                         qna_examples = gr.Dropdown(e4u.get_relevant_questions(APM_MOCK_QNA), value=None,label="Questions", interactive=True)
@@ -396,11 +405,25 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
                     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...")
+            ea4all_vqa = gr.Chatbot(
+                label="EA4ALL your AI Multimodal Architect Companion", type="messages",
+                max_height=160,
+                layout="bubble",
+            )
+            vqa_prompt = gr.Textbox(lines=1, show_label=False, max_lines=1, submit_btn=True, stop_btn=True,autofocus=True, placeholder="Type your message here and upload your diagram...")
+            vqa_image = gr.Image(
+                label="Architecture Diagram",
+                type="filepath",
+                format="jpeg, png",
+                interactive=True,
+                show_download_button=False,
+                show_share_button=False,
+                visible=True,
+            )
+            #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 question examples", open=False):
                 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)
+            gr.ClearButton([ea4all_vqa,vqa_prompt,vqa_image, 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"):
@@ -479,7 +502,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
     #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, api_name="landscape_answering_agent")
+    qna_prompt.submit(run_qna_agentic_system,[qna_prompt],ea4all_chatbot, api_name="landscape_answering_agent")
     #qna_prompt.submit(lambda: "", None, [qna_prompt])
     #ea4all_chatbot.like(fn=get_user_feedback)
     #qna_examples.input(lambda value: value, qna_examples, qna_prompt)
@@ -487,14 +510,15 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
     #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], api_name="togaf_blueprint_generation")
 
-    chat_msg = vqa_prompt.submit(UIUtils.add_message, [vqa_prompt, ea4all_vqa], [vqa_prompt, ea4all_vqa], show_api=False)
-    bot_msg = chat_msg.then(run_vqa_agentic_system, [vqa_prompt, ea4all_vqa], ea4all_vqa, api_name="diagram_answering_agent")
+    #chat_msg = vqa_prompt.submit(UIUtils.add_message, [vqa_prompt, vqa_image], [vqa_prompt, ea4all_vqa], show_api=False)
+    #bot_msg = chat_msg.then(run_vqa_agentic_system, [vqa_prompt, vqa_image], ea4all_vqa, api_name="diagram_answering_agent")
+    vqa_prompt.submit(run_vqa_agentic_system,[vqa_prompt, vqa_image], ea4all_vqa, api_name="diagram_answering_agent")
     
     #ea4all_vqa.like(fn=get_user_feedback)
-    #vqa_examples.input(lambda value: value, vqa_examples, vqa_prompt)
+    vqa_examples.input(lambda value: [value['text'], value['files'][-1]], vqa_examples, outputs=[vqa_prompt, vqa_image])
 
     #Invoke CrewAI PMO Agentic System
-    pmo_prompt.submit(run_pmo_agentic_system,[pmo_prompt,pmo_chatbot],pmo_chatbot, api_name="architect_demand_agent")
+    pmo_prompt.submit(run_pmo_agentic_system,[pmo_prompt],pmo_chatbot, api_name="architect_demand_agent")
     pmo_prompt.submit(lambda: "", None, [pmo_prompt], show_api=False)
     #pmo_examples.input(lambda value: value, pmo_examples, pmo_prompt)
 

ea4all/utils/utils.py

@@ -28,7 +28,7 @@ class UIUtils:
 
     #vqa_chatbot (ChatInterface -> Chatbot)
     @staticmethod
-    def add_message(message, history, show_api=False):
+    def add_message(message, history):
         if message["text"] is not None:
             history.append({"role": "user", "content": message["text"]})
         
@@ -42,11 +42,11 @@ class UIUtils:
 
     #Upload & clear business requirement
     @staticmethod
-    def load_dbr(file, show_api=False):
+    def load_dbr(file):
         return file.decode()
 
 #Load demo business requirements
-def init_dbr(show_api=False):
+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
@@ -58,7 +58,7 @@ def init_df(show_api=False):
 
 #load core-architecture image
 #fix the issue with gr.Image(path) inside a docker containder
-def get_image(_image, show_api=False):
+def get_image(_image):
     #from PIL import Image
     # Load an image
     image = e4u._join_paths(BaseConfiguration.ea4all_images,_image)
@@ -90,12 +90,12 @@ def ea4all_confluence(show_api=False):
 
     return df        
 
-def filter_page(page_list, title, show_api=False):
+def filter_page(page_list, title):
     x =  page_list[page_list["title"] == title]
     return x.iloc[0]['page_content']
 
 #get LLM response user's feedback
-def get_user_feedback(evt: gr.SelectData, request:gr.Request, show_api=False):
+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(".","_")]
@@ -112,7 +112,7 @@ def get_user_feedback(evt: gr.SelectData, request:gr.Request, show_api=False):
         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, show_api=False):
+async def ea4all_agent_init(request:gr.Request):
 
     agentic_qna_desc="""Hi,
         improve effieciency, knowledge sharing, and get valuable insights from your IT landscape using natural language.
@@ -150,17 +150,17 @@ async def ea4all_agent_init(request:gr.Request, show_api=False):
     )
 
 #authentication
-def ea4all_login(username, password, show_api=False):
+def ea4all_login(username, password):
     return (username==password)
 
 #TABS & Reference Architecture look-and-feel control
-def off_dbrtext(show_api=False):
+def off_dbrtext():
     return gr.TextArea(visible=False), gr.Tab(visible=False), gr.Tab(visible=False)
 
-def on_dbrtext(file,show_api=False):
+def on_dbrtext(file):
     if file:
         return gr.TextArea(visible=True)
     return gr.TextArea(visible=False)
 
-def unload_dbr(show_api=False):
+def unload_dbr():
     return gr.TextArea(visible=False)