Update app.py
Browse files
app.py
CHANGED
@@ -5,23 +5,15 @@ from multiprocessing import freeze_support
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import importlib
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import inspect
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# === Fix path to include src/txagent
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
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# === Import and reload to ensure correct file
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import txagent.txagent
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importlib.reload(txagent.txagent)
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from txagent.txagent import TxAgent
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print(">>> TxAgent loaded from:", inspect.getfile(TxAgent))
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print(">>> TxAgent has run_gradio_chat:", hasattr(TxAgent, "run_gradio_chat"))
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# === Environment
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current_dir = os.path.abspath(os.path.dirname(__file__))
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os.environ["MKL_THREADING_LAYER"] = "GNU"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# === Configs
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model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
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rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
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new_tool_files = {
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}
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question_examples = [
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["Given a patient with WHIM syndrome on
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["What treatment options exist for HER2+ breast cancer resistant to trastuzumab?"]
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]
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# === UI
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def create_ui(agent):
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with gr.Blocks() as demo:
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gr.Markdown("<h1 style='text-align: center;'>TxAgent: Therapeutic Reasoning</h1>")
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gr.Markdown("Ask
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temperature = gr.Slider(0, 1, value=0.3, label="Temperature")
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max_new_tokens = gr.Slider(128, 4096, value=1024, label="Max New Tokens")
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@@ -54,56 +48,65 @@ def create_ui(agent):
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multi_agent = gr.Checkbox(label="Enable Multi-agent Reasoning", value=False)
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conversation_state = gr.State([])
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chatbot = gr.
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message_input = gr.Textbox(placeholder="Ask your biomedical question...", show_label=False)
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send_button = gr.Button("Send", variant="primary")
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# === Chat handler
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def handle_chat(message, history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
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print("⚠️ Error in chat handler:", e)
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yield history + [{"role": "assistant", "content": f"An error occurred: {str(e)}"}]
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# === Actions
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send_button.click(
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fn=handle_chat,
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inputs=[message_input,
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outputs=
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)
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message_input.submit(
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fn=handle_chat,
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inputs=[message_input,
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outputs=
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)
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gr.Examples(examples=question_examples, inputs=message_input)
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gr.Markdown("**DISCLAIMER**:
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return demo
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# === Entry point
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if __name__ == "__main__":
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freeze_support()
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try:
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@@ -115,16 +118,16 @@ if __name__ == "__main__":
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enable_checker=True,
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step_rag_num=10,
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seed=100,
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additional_default_tools=[]
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)
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agent.init_model()
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if not hasattr(agent, "run_gradio_chat"):
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raise AttributeError("❌ TxAgent
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demo = create_ui(agent)
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demo.launch(show_error=True)
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except Exception as e:
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print(f"❌
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raise
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import importlib
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import inspect
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
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import txagent.txagent
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importlib.reload(txagent.txagent)
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from txagent.txagent import TxAgent
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current_dir = os.path.abspath(os.path.dirname(__file__))
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os.environ["MKL_THREADING_LAYER"] = "GNU"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
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rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
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new_tool_files = {
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}
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question_examples = [
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["Given a patient with WHIM syndrome on antibiotics, is Xolremdi + fluconazole advisable?"],
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["What treatment options exist for HER2+ breast cancer resistant to trastuzumab?"]
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]
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def extract_sections(content):
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"""
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Example extractor splitting into sections. You should improve it to parse actual keys.
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"""
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return {
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"Summary": content[:1000], # simulate
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"Clinical Studies": content[1000:2500],
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"Drug Interactions": "See CYP3A4 interactions...",
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"Pharmacokinetics": "- Absorption: Oral\n- Half-life: ~24h\n- Metabolized by CYP3A4"
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}
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def create_ui(agent):
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with gr.Blocks() as demo:
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gr.Markdown("<h1 style='text-align: center;'>TxAgent: Therapeutic Reasoning</h1>")
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gr.Markdown("Ask therapeutic or biomedical questions. Results are categorized for readability.")
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temperature = gr.Slider(0, 1, value=0.3, label="Temperature")
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max_new_tokens = gr.Slider(128, 4096, value=1024, label="Max New Tokens")
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multi_agent = gr.Checkbox(label="Enable Multi-agent Reasoning", value=False)
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conversation_state = gr.State([])
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chatbot = gr.Tabs()
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summary_box = gr.Markdown(label="Summary")
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studies_box = gr.Markdown(label="Clinical Studies")
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interactions_box = gr.Markdown(label="Drug Interactions")
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kinetics_box = gr.Markdown(label="Pharmacokinetics")
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with chatbot:
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with gr.TabItem("Summary"):
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summary_display = summary_box
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with gr.TabItem("Clinical Studies"):
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studies_display = studies_box
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with gr.TabItem("Drug Interactions"):
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interactions_display = interactions_box
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with gr.TabItem("Pharmacokinetics"):
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kinetics_display = kinetics_box
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message_input = gr.Textbox(placeholder="Ask your biomedical question...", show_label=False)
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send_button = gr.Button("Send", variant="primary")
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def handle_chat(message, history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
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generator = agent.run_gradio_chat(
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message=message,
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history=history,
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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max_token=max_tokens,
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call_agent=multi_agent,
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conversation=conversation,
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max_round=max_round
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)
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final_output = ""
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for update in generator:
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for m in update:
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role = m["role"] if isinstance(m, dict) else getattr(m, "role", "assistant")
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content = m["content"] if isinstance(m, dict) else getattr(m, "content", "")
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if role == "assistant":
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final_output += content + "\n"
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sections = extract_sections(final_output)
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return sections["Summary"], sections["Clinical Studies"], sections["Drug Interactions"], sections["Pharmacokinetics"]
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send_button.click(
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fn=handle_chat,
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inputs=[message_input, [], temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round],
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outputs=[summary_box, studies_box, interactions_box, kinetics_box]
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)
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message_input.submit(
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fn=handle_chat,
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inputs=[message_input, [], temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round],
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outputs=[summary_box, studies_box, interactions_box, kinetics_box]
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)
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gr.Examples(examples=question_examples, inputs=message_input)
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gr.Markdown("**DISCLAIMER**: For research only. Not medical advice.")
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return demo
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if __name__ == "__main__":
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freeze_support()
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try:
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enable_checker=True,
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step_rag_num=10,
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seed=100,
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additional_default_tools=[]
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)
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agent.init_model()
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if not hasattr(agent, "run_gradio_chat"):
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raise AttributeError("❌ TxAgent missing `run_gradio_chat`")
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demo = create_ui(agent)
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demo.launch(show_error=True)
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except Exception as e:
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print(f"❌ App failed to start: {e}")
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raise
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