Update app.py

app.py

@@ -4,42 +4,60 @@ import gradio as gr
 from multiprocessing import freeze_support
 import importlib
 import inspect
+import json
 
+# Fix path to include src
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src"))
+
+# Reload TxAgent from txagent.py
 import txagent.txagent
 importlib.reload(txagent.txagent)
 from txagent.txagent import TxAgent
 
+# Debug info
+print(">>> TxAgent loaded from:", inspect.getfile(TxAgent))
+print(">>> TxAgent has run_gradio_chat:", hasattr(TxAgent, "run_gradio_chat"))
+
+# Env vars
 current_dir = os.path.abspath(os.path.dirname(__file__))
 os.environ["MKL_THREADING_LAYER"] = "GNU"
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+# Model config
 model_name = "mims-harvard/TxAgent-T1-Llama-3.1-8B"
 rag_model_name = "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B"
 new_tool_files = {
     "new_tool": os.path.join(current_dir, "data", "new_tool.json")
 }
 
+# Sample questions
 question_examples = [
-    ["Given a patient with WHIM syndrome on antibiotics, is Xolremdi + fluconazole advisable?"],
+    ["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_sections(content):
-    """
-    Example extractor splitting into sections. You should improve it to parse actual keys.
-    """
-    return {
-        "Summary": content[:1000],  # simulate
-        "Clinical Studies": content[1000:2500],
-        "Drug Interactions": "See CYP3A4 interactions...",
-        "Pharmacokinetics": "- Absorption: Oral\n- Half-life: ~24h\n- Metabolized by CYP3A4"
-    }
-
+# Helper: format assistant responses in collapsible panels
+def format_collapsible(content):
+    if isinstance(content, (dict, list)):
+        try:
+            formatted = json.dumps(content, indent=2)
+        except Exception:
+            formatted = str(content)
+    else:
+        formatted = str(content)
+
+    return (
+        "<details style='border: 1px solid #ccc; padding: 8px; margin-top: 8px;'>"
+        "<summary style='font-weight: bold;'>Answer</summary>"
+        f"<pre style='white-space: pre-wrap;'>{formatted}</pre>"
+        "</details>"
+    )
+
+# === UI setup
 def create_ui(agent):
     with gr.Blocks() as demo:
         gr.Markdown("<h1 style='text-align: center;'>TxAgent: Therapeutic Reasoning</h1>")
-        gr.Markdown("Ask therapeutic or biomedical questions. Results are categorized for readability.")
+        gr.Markdown("Ask biomedical or therapeutic questions. Powered by step-by-step reasoning and tools.")
 
         temperature = gr.Slider(0, 1, value=0.3, label="Temperature")
         max_new_tokens = gr.Slider(128, 4096, value=1024, label="Max New Tokens")
@@ -48,25 +66,11 @@ def create_ui(agent):
         multi_agent = gr.Checkbox(label="Enable Multi-agent Reasoning", value=False)
         conversation_state = gr.State([])
 
-        chatbot = gr.Tabs()
-        summary_box = gr.Markdown(label="Summary")
-        studies_box = gr.Markdown(label="Clinical Studies")
-        interactions_box = gr.Markdown(label="Drug Interactions")
-        kinetics_box = gr.Markdown(label="Pharmacokinetics")
-
-        with chatbot:
-            with gr.TabItem("Summary"):
-                summary_display = summary_box
-            with gr.TabItem("Clinical Studies"):
-                studies_display = studies_box
-            with gr.TabItem("Drug Interactions"):
-                interactions_display = interactions_box
-            with gr.TabItem("Pharmacokinetics"):
-                kinetics_display = kinetics_box
-
+        chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
         message_input = gr.Textbox(placeholder="Ask your biomedical question...", show_label=False)
         send_button = gr.Button("Send", variant="primary")
 
+        # Main handler
         def handle_chat(message, history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
             generator = agent.run_gradio_chat(
                 message=message,
@@ -79,36 +83,32 @@ def create_ui(agent):
                 max_round=max_round
             )
 
-            final_output = ""
             for update in generator:
+                formatted = []
                 for m in update:
                     role = m["role"] if isinstance(m, dict) else getattr(m, "role", "assistant")
                     content = m["content"] if isinstance(m, dict) else getattr(m, "content", "")
-                    if role == "assistant":
-                        final_output += content + "\n"
 
-            sections = extract_sections(final_output)
-            return sections["Summary"], sections["Clinical Studies"], sections["Drug Interactions"], sections["Pharmacokinetics"]
+                    if role == "assistant":
+                        content = format_collapsible(content)
 
-        send_button.click(
-            fn=handle_chat,
-            inputs=[message_input, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round],
-            outputs=[summary_box, studies_box, interactions_box, kinetics_box]
-        )
+                    formatted.append({"role": role, "content": content})
+                yield formatted
 
-        message_input.submit(
-            fn=handle_chat,
-            inputs=[message_input, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round],
-            outputs=[summary_box, studies_box, interactions_box, kinetics_box]
-        )
+        # Button and Enter triggers
+        inputs = [message_input, chatbot, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round]
+        send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
+        message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)
 
         gr.Examples(examples=question_examples, inputs=message_input)
-        gr.Markdown("**DISCLAIMER**: For research only. Not medical advice.")
+        gr.Markdown("**DISCLAIMER**: This demo is for research purposes only and does not provide medical advice.")
 
     return demo
 
+# === Entry point
 if __name__ == "__main__":
     freeze_support()
+
     try:
         agent = TxAgent(
             model_name=model_name,
@@ -118,12 +118,12 @@ if __name__ == "__main__":
             enable_checker=True,
             step_rag_num=10,
             seed=100,
-            additional_default_tools=[]
+            additional_default_tools=[]  # Avoid loading unimplemented tools
         )
         agent.init_model()
 
         if not hasattr(agent, "run_gradio_chat"):
-            raise AttributeError("❌ TxAgent missing `run_gradio_chat`")
+            raise AttributeError("TxAgent missing run_gradio_chat")
 
         demo = create_ui(agent)
         demo.launch(show_error=True)