Update app.py

app.py

@@ -36,47 +36,112 @@ CONFIG = {
     }
 }
 
-# Add missing chat_css definition
+DESCRIPTION = '''
+<div>
+<h1 style="text-align: center;">TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools</h1>
+</div>
+'''
+
+INTRO = """
+Precision therapeutics require multimodal adaptive models that provide personalized treatment recommendations. 
+We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge 
+retrieval across a toolbox of 211 expert-curated tools to navigate complex drug interactions, 
+contraindications, and patient-specific treatment strategies, delivering evidence-grounded therapeutic decisions.
+"""
+
+LICENSE = """
+We welcome your feedback and suggestions to enhance your experience with TxAgent, and if you're interested 
+in collaboration, please email Marinka Zitnik and Shanghua Gao.
+
+### Medical Advice Disclaimer
+DISCLAIMER: THIS WEBSITE DOES NOT PROVIDE MEDICAL ADVICE
+The information, including but not limited to, text, graphics, images and other material contained on this 
+website are for informational purposes only. No material on this site is intended to be a substitute for 
+professional medical advice, diagnosis or treatment.
+"""
+
+PLACEHOLDER = """
+<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">TxAgent</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Tips before using TxAgent:</p>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.55;">Please click clear🗑️ (top-right) to remove previous context before submitting a new question.</p>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.55;">Click retry🔄 (below message) to get multiple versions of the answer.</p>
+</div>
+"""
+
+css = """
+h1 {
+  text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: white;
+  background: #1565c0;
+  border-radius: 100vh;
+}
+.small-button button {
+    font-size: 12px !important;
+    padding: 4px 8px !important;
+    height: 6px !important;
+    width: 4px !important;
+}
+.gradio-accordion {
+    margin-top: 0px !important;
+    margin-bottom: 0px !important;
+}
+"""
+
 chat_css = """
 .gr-button { font-size: 20px !important; }
 .gr-button svg { width: 32px !important; height: 32px !important; }
 """
 
 def safe_load_embeddings(filepath: str) -> any:
+    """Safely load embeddings with proper weights_only handling"""
     try:
+        # First try with weights_only=True
         return torch.load(filepath, weights_only=True)
     except Exception as e:
         logger.warning(f"Secure load failed, trying with weights_only=False: {str(e)}")
         try:
-            import numpy.core.multiarray
-            torch.serialization.add_safe_globals({"_reconstruct": numpy.core.multiarray._reconstruct})
+            # Fallback to unsafe load if needed
             return torch.load(filepath, weights_only=False)
         except Exception as e:
-            logger.error(f"Failed to load embeddings even with safe_globals: {str(e)}")
+            logger.error(f"Failed to load embeddings: {str(e)}")
             return None
 
 def patch_embedding_loading():
+    """Monkey-patch the embedding loading functionality"""
     try:
         from txagent.toolrag import ToolRAGModel
-
+        
+        original_load = ToolRAGModel.load_tool_desc_embedding
+        
         def patched_load(self, tooluniverse):
             try:
                 if not os.path.exists(CONFIG["embedding_filename"]):
                     logger.error(f"Embedding file not found: {CONFIG['embedding_filename']}")
                     return False
-
+                
                 self.tool_desc_embedding = safe_load_embeddings(CONFIG["embedding_filename"])
-                if self.tool_desc_embedding is None:
-                    logger.error("Embedding is None, aborting.")
+                
+                # Updated tool loading approach
+                if hasattr(tooluniverse, 'get_all_tools'):
+                    tools = tooluniverse.get_all_tools()
+                elif hasattr(tooluniverse, 'tools'):
+                    tools = tooluniverse.tools
+                else:
+                    logger.error("No method found to access tools from ToolUniverse")
                     return False
-
-                tools = list(tooluniverse.get_all_tools()) if hasattr(tooluniverse, 'get_all_tools') else []
+                
                 current_count = len(tools)
                 embedding_count = len(self.tool_desc_embedding)
-
+                
                 if current_count != embedding_count:
                     logger.warning(f"Tool count mismatch (tools: {current_count}, embeddings: {embedding_count})")
-
+                    
                     if current_count < embedding_count:
                         self.tool_desc_embedding = self.tool_desc_embedding[:current_count]
                         logger.info(f"Truncated embeddings to match {current_count} tools")
@@ -85,51 +150,81 @@ def patch_embedding_loading():
                         padding = [last_embedding] * (current_count - embedding_count)
                         self.tool_desc_embedding = torch.cat([self.tool_desc_embedding] + padding)
                         logger.info(f"Padded embeddings to match {current_count} tools")
-
+                
                 return True
-
+                
             except Exception as e:
                 logger.error(f"Failed to load embeddings: {str(e)}")
                 return False
-
+        
         ToolRAGModel.load_tool_desc_embedding = patched_load
         logger.info("Successfully patched embedding loading")
-
+        
     except Exception as e:
         logger.error(f"Failed to patch embedding loading: {str(e)}")
         raise
 
 def prepare_tool_files():
+    """Ensure tool files exist and are populated"""
     os.makedirs(os.path.join(current_dir, 'data'), exist_ok=True)
     if not os.path.exists(CONFIG["tool_files"]["new_tool"]):
         logger.info("Generating tool list using ToolUniverse...")
-        tu = ToolUniverse()
-        tools = list(tu.get_all_tools()) if hasattr(tu, 'get_all_tools') else []
-        with open(CONFIG["tool_files"]["new_tool"], "w") as f:
-            json.dump(tools, f, indent=2)
-        logger.info(f"Saved {len(tools)} tools to {CONFIG['tool_files']['new_tool']}")
+        try:
+            tu = ToolUniverse()
+            if hasattr(tu, 'get_all_tools'):
+                tools = tu.get_all_tools()
+            elif hasattr(tu, 'tools'):
+                tools = tu.tools
+            else:
+                tools = []
+                logger.error("Could not access tools from ToolUniverse")
+            
+            with open(CONFIG["tool_files"]["new_tool"], "w") as f:
+                json.dump(tools, f, indent=2)
+            logger.info(f"Saved {len(tools)} tools to {CONFIG['tool_files']['new_tool']}")
+        except Exception as e:
+            logger.error(f"Failed to prepare tool files: {str(e)}")
 
 def create_agent():
+    """Create and initialize the TxAgent"""
+    # Apply the embedding patch before creating the agent
     patch_embedding_loading()
     prepare_tool_files()
 
-    agent = TxAgent(
-        CONFIG["model_name"],
-        CONFIG["rag_model_name"],
-        tool_files_dict=CONFIG["tool_files"],
-        force_finish=True,
-        enable_checker=True,
-        step_rag_num=10,
-        seed=100,
-        additional_default_tools=['DirectResponse', 'RequireClarification']
-    )
-    agent.init_model()
-    return agent
+    # Initialize the agent
+    try:
+        agent = TxAgent(
+            CONFIG["model_name"],
+            CONFIG["rag_model_name"],
+            tool_files_dict=CONFIG["tool_files"],
+            force_finish=True,
+            enable_checker=True,
+            step_rag_num=10,
+            seed=100,
+            additional_default_tools=['DirectResponse', 'RequireClarification']
+        )
+        agent.init_model()
+        return agent
+    except Exception as e:
+        logger.error(f"Failed to create agent: {str(e)}")
+        raise
+
+def handle_chat_response(history, message, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
+    """Convert generator output to Gradio-compatible format"""
+    full_response = ""
+    for chunk in message:
+        if isinstance(chunk, dict):
+            full_response += chunk.get("content", "")
+        else:
+            full_response += str(chunk)
+    history.append((None, full_response))
+    return history
 
 def update_model_parameters(agent, enable_finish, enable_rag, enable_summary,
                           init_rag_num, step_rag_num, skip_last_k,
                           summary_mode, summary_skip_last_k, summary_context_length, 
                           force_finish, seed):
+    """Update model parameters"""
     updated_params = agent.update_parameters(
         enable_finish=enable_finish,
         enable_rag=enable_rag,
@@ -146,59 +241,195 @@ def update_model_parameters(agent, enable_finish, enable_rag, enable_summary,
     return updated_params
 
 def update_seed(agent):
+    """Update random seed"""
     seed = random.randint(0, 10000)
     updated_params = agent.update_parameters(seed=seed)
     return updated_params
 
 def handle_retry(agent, history, retry_data: gr.RetryData, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
+    """Handle retry functionality"""
     print("Updated seed:", update_seed(agent))
     new_history = history[:retry_data.index]
     previous_prompt = history[retry_data.index]['content']
     print("previous_prompt", previous_prompt)
-    yield from agent.run_gradio_chat(new_history + [{"role": "user", "content": previous_prompt}], 
+    response = agent.run_gradio_chat(new_history + [{"role": "user", "content": previous_prompt}], 
                                    temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
+    yield from handle_chat_response(new_history, response, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
 
 PASSWORD = "mypassword"
 
 def check_password(input_password):
+    """Check password for protected settings"""
     if input_password == PASSWORD:
         return gr.update(visible=True), ""
     else:
         return gr.update(visible=False), "Incorrect password, try again!"
 
 def create_demo(agent):
-    chatbot = gr.Chatbot(type="messages")
-    with gr.Blocks(css=chat_css) as demo:
-        with gr.Row():
-            gr.Markdown("""
-            # TxAgent Interface
-            Ask biomedical questions and get reasoning-based answers.
-            """)
-
-        user_input = gr.Textbox(label="Your question")
-        temperature = gr.Slider(0, 1, value=0.3, step=0.1, label="Temperature")
-        max_new_tokens = gr.Slider(128, 4096, value=1024, step=1, label="Max New Tokens")
-        max_tokens = gr.Slider(128, 81920, value=81920, step=1, label="Max Tokens")
-        max_round = gr.Slider(1, 30, value=30, step=1, label="Max Rounds")
-        multi_agent = gr.Checkbox(label="Multi-Agent Mode")
-
-        submit = gr.Button("Run TxAgent")
+    """Create the Gradio interface"""
+    default_temperature = 0.3
+    default_max_new_tokens = 1024
+    default_max_tokens = 81920
+    default_max_round = 30
+    
+    question_examples = [
+        ['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of moderate hepatic impairment?'],
+        ['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of severe hepatic impairment?'],
+        ['A 30-year-old patient is taking Prozac to treat their depression. They were recently diagnosed with WHIM syndrome and require a treatment for that condition as well. Is Xolremdi suitable for this patient, considering contraindications?'],
+    ]
+
+    chatbot = gr.Chatbot(height=800, placeholder=PLACEHOLDER,
+                        label='TxAgent', show_copy_button=True)
+    
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown(DESCRIPTION)
+        gr.Markdown(INTRO)
+        
+        temperature_state = gr.State(value=default_temperature)
+        max_new_tokens_state = gr.State(value=default_max_new_tokens)
+        max_tokens_state = gr.State(value=default_max_tokens)
+        max_round_state = gr.State(value=default_max_round)
+        
+        chatbot.retry(
+            lambda *args: handle_retry(agent, *args), 
+            inputs=[chatbot, chatbot, temperature_state, max_new_tokens_state,
+                   max_tokens_state, gr.Checkbox(value=False, render=False), 
+                   gr.State([]), max_round_state]
+        )
 
-        def run_agent(message, history, temperature, max_new_tokens, max_tokens, multi_agent, max_round):
-            return agent.run_gradio_chat(history + [{"role": "user", "content": message}],
-                                         temperature, max_new_tokens, max_tokens,
-                                         multi_agent, [], max_round)
-
-        submit.click(run_agent, inputs=[user_input, chatbot, temperature, max_new_tokens, max_tokens, multi_agent, max_round], outputs=chatbot)
+        with gr.Row():
+            with gr.Column(scale=4):
+                msg = gr.Textbox(label="Input", placeholder="Type your question here...")
+            with gr.Column(scale=1):
+                submit_btn = gr.Button("Submit", variant="primary")
 
+        with gr.Row():
+            clear_btn = gr.ClearButton([msg, chatbot])
+
+        def respond(message, chat_history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
+            response = agent.run_gradio_chat(
+                chat_history + [{"role": "user", "content": message}],
+                temperature,
+                max_new_tokens,
+                max_tokens,
+                multi_agent,
+                conversation,
+                max_round
+            )
+            return handle_chat_response(chat_history, response, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
+
+        submit_btn.click(
+            respond,
+            inputs=[msg, chatbot, temperature_state, max_new_tokens_state, 
+                   max_tokens_state, gr.Checkbox(value=False, render=False),
+                   gr.State([]), max_round_state],
+            outputs=[chatbot]
+        )
+        msg.submit(
+            respond,
+            inputs=[msg, chatbot, temperature_state, max_new_tokens_state, 
+                   max_tokens_state, gr.Checkbox(value=False, render=False),
+                   gr.State([]), max_round_state],
+            outputs=[chatbot]
+        )
+
+        with gr.Accordion("Settings", open=False):
+            temperature_slider = gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=default_temperature,
+                label="Temperature"
+            )
+            max_new_tokens_slider = gr.Slider(
+                minimum=128,
+                maximum=4096,
+                step=1,
+                value=default_max_new_tokens,
+                label="Max new tokens"
+            )
+            max_tokens_slider = gr.Slider(
+                minimum=128,
+                maximum=32000,
+                step=1,
+                value=default_max_tokens,
+                label="Max tokens"
+            )
+            max_round_slider = gr.Slider(
+                minimum=0,
+                maximum=50,
+                step=1,
+                value=default_max_round,
+                label="Max round")
+
+            temperature_slider.change(
+                lambda x: x, inputs=temperature_slider, outputs=temperature_state)
+            max_new_tokens_slider.change(
+                lambda x: x, inputs=max_new_tokens_slider, outputs=max_new_tokens_state)
+            max_tokens_slider.change(
+                lambda x: x, inputs=max_tokens_slider, outputs=max_tokens_state)
+            max_round_slider.change(
+                lambda x: x, inputs=max_round_slider, outputs=max_round_state)
+
+            password_input = gr.Textbox(
+                label="Enter Password for More Settings", type="password")
+            incorrect_message = gr.Textbox(visible=False, interactive=False)
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False, visible=False) as protected_accordion:
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        with gr.Accordion("Model Settings", open=False):
+                            model_name_input = gr.Textbox(
+                                label="Enter model path", value=CONFIG["model_name"])
+                            load_model_btn = gr.Button(value="Load Model")
+                            load_model_btn.click(
+                                agent.load_models, 
+                                inputs=model_name_input, 
+                                outputs=gr.Textbox(label="Status"))
+                    with gr.Column(scale=1):
+                        with gr.Accordion("Functional Parameters", open=False):
+                            enable_finish = gr.Checkbox(label="Enable Finish", value=True)
+                            enable_rag = gr.Checkbox(label="Enable RAG", value=True)
+                            enable_summary = gr.Checkbox(label="Enable Summary", value=False)
+                            init_rag_num = gr.Number(label="Initial RAG Num", value=0)
+                            step_rag_num = gr.Number(label="Step RAG Num", value=10)
+                            skip_last_k = gr.Number(label="Skip Last K", value=0)
+                            summary_mode = gr.Textbox(label="Summary Mode", value='step')
+                            summary_skip_last_k = gr.Number(label="Summary Skip Last K", value=0)
+                            summary_context_length = gr.Number(label="Summary Context Length", value=None)
+                            force_finish = gr.Checkbox(label="Force FinalAnswer", value=True)
+                            seed = gr.Number(label="Seed", value=100)
+                            submit_btn = gr.Button("Update Parameters")
+                            updated_parameters_output = gr.JSON()
+                            submit_btn.click(
+                                lambda *args: update_model_parameters(agent, *args),
+                                inputs=[enable_finish, enable_rag, enable_summary, 
+                                      init_rag_num, step_rag_num, skip_last_k,
+                                      summary_mode, summary_skip_last_k, 
+                                      summary_context_length, force_finish, seed],
+                                outputs=updated_parameters_output
+                            )
+            
+            submit_button = gr.Button("Submit")
+            submit_button.click(
+                check_password,
+                inputs=password_input,
+                outputs=[protected_accordion, incorrect_message]
+            )
+        
+        gr.Markdown(LICENSE)
+    
     return demo
 
-# Define main and launch
-
 def main():
-    agent = create_agent()
-    demo = create_demo(agent)
-    demo.launch(share=True)
+    """Main function to run the application"""
+    try:
+        agent = create_agent()
+        demo = create_demo(agent)
+        demo.launch(share=True)
+    except Exception as e:
+        logger.error(f"Application failed to start: {str(e)}")
+        raise
 
 if __name__ == "__main__":
-    main()
+    main()