# ui/ui_core.py
import gradio as gr
import json
question_examples = [
["Given a patient with WHIM syndrome on prophylactic antibiotics, is it advisable to co-administer Xolremdi with fluconazole?"],
["What treatment options exist for HER2+ breast cancer resistant to trastuzumab?"]
]
def extract_tool_name_and_clean_content(msg):
tool_name = "Tool Result"
content = msg.get("content") if isinstance(msg, dict) else getattr(msg, "content", "")
try:
parsed = json.loads(content)
if isinstance(parsed, dict):
tool_name = parsed.get("tool_name", tool_name)
content = parsed.get("content", content)
except Exception:
pass
if isinstance(content, (dict, list)):
content = json.dumps(content, indent=2)
return f"Tool: {tool_name}", content
def format_collapsible(content, title="Answer"):
return (
f""
f"{title}
"
f"{content}
"
)
def create_ui(agent):
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("
💊 TxAgent: Therapeutic Reasoning
")
chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
message_input = gr.Textbox(placeholder="Ask a biomedical question...", show_label=False)
send_button = gr.Button("Send", variant="primary")
conversation_state = gr.State([])
def handle_chat(message, history, conversation):
generator = agent.run_gradio_chat(
message=message,
history=history,
temperature=0.3,
max_new_tokens=1024,
max_token=8192,
call_agent=False,
conversation=conversation,
max_round=30
)
for update in generator:
formatted = []
for m in update:
role = m.get("role") if isinstance(m, dict) else getattr(m, "role", "assistant")
if role == "assistant":
title, clean = extract_tool_name_and_clean_content(m)
content = format_collapsible(clean, title)
else:
content = m.get("content") if isinstance(m, dict) else getattr(m, "content", "")
formatted.append({"role": role, "content": content})
yield formatted
send_button.click(fn=handle_chat, inputs=[message_input, chatbot, conversation_state], outputs=chatbot)
message_input.submit(fn=handle_chat, inputs=[message_input, chatbot, conversation_state], outputs=chatbot)
gr.Examples(examples=question_examples, inputs=message_input)
gr.Markdown("DISCLAIMER: This demo is for research purposes only and does not provide medical advice.")
return demo