Update app.py

app.py

@@ -1,6 +1,4 @@
 import random
-import datetime
-import sys
 import os
 import torch
 import logging
@@ -17,7 +15,6 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
-# Determine the directory where the current file is located
 current_dir = os.path.dirname(os.path.abspath(__file__))
 os.environ["MKL_THREADING_LAYER"] = "GNU"
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -36,98 +33,94 @@ CONFIG = {
     }
 }
 
-chat_css = """
-.gr-button { font-size: 20px !important; }
-.gr-button svg { width: 32px !important; height: 32px !important; }
+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.
 """
 
-def safe_load_embeddings(filepath: str) -> any:
+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>
+"""
+
+def safe_load_embeddings(filepath: str):
+    """Handle embedding loading with fallbacks"""
     try:
         return torch.load(filepath, weights_only=True)
     except Exception as e:
-        logger.warning(f"Secure load failed, trying with weights_only=False: {str(e)}")
+        logger.warning(f"Secure load failed, trying without weights_only: {str(e)}")
         try:
             return torch.load(filepath, weights_only=False)
         except Exception as e:
             logger.error(f"Failed to load embeddings: {str(e)}")
             return None
 
-def patch_embedding_loading():
-    try:
-        from txagent.toolrag import ToolRAGModel
-
-        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 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
-
-                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")
-                    else:
-                        last_embedding = self.tool_desc_embedding[-1]
-                        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 get_tools_from_universe(tooluniverse):
+    """Flexible tool extraction from ToolUniverse"""
+    if hasattr(tooluniverse, 'get_all_tools'):
+        return tooluniverse.get_all_tools()
+    elif hasattr(tooluniverse, 'tools'):
+        return tooluniverse.tools
+    elif hasattr(tooluniverse, 'list_tools'):
+        return tooluniverse.list_tools()
+    else:
+        logger.error("Could not find any tool access method in ToolUniverse")
+        # Try to load from files directly as fallback
+        tools = []
+        for tool_file in CONFIG["tool_files"].values():
+            if os.path.exists(tool_file):
+                with open(tool_file, 'r') as f:
+                    tools.extend(json.load(f))
+        return tools if tools else None
 
 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...")
+        logger.info("Generating tool list...")
         try:
             tu = ToolUniverse()
-            if hasattr(tu, 'get_all_tools'):
-                tools = tu.get_all_tools()
-            elif hasattr(tu, 'tools'):
-                tools = tu.tools
+            tools = get_tools_from_universe(tu)
+            if tools:
+                with open(CONFIG["tool_files"]["new_tool"], "w") as f:
+                    json.dump(tools, f, indent=2)
+                logger.info(f"Saved {len(tools)} 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']}")
+                logger.error("No tools could be loaded")
         except Exception as e:
-            logger.error(f"Failed to prepare tool files: {str(e)}")
+            logger.error(f"Tool file preparation failed: {str(e)}")
 
 def create_agent():
-    patch_embedding_loading()
+    """Create and initialize the TxAgent with robust error handling"""
     prepare_tool_files()
-
+    
     try:
         agent = TxAgent(
-            CONFIG["model_name"],
-            CONFIG["rag_model_name"],
+            model_name=CONFIG["model_name"],
+            rag_model_name=CONFIG["rag_model_name"],
             tool_files_dict=CONFIG["tool_files"],
             force_finish=True,
             enable_checker=True,
@@ -138,51 +131,97 @@ def create_agent():
         agent.init_model()
         return agent
     except Exception as e:
-        logger.error(f"Failed to create agent: {str(e)}")
+        logger.error(f"Agent creation failed: {str(e)}")
         raise
 
-def respond(message, history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
-    updated_history = history + [{"role": "user", "content": message}]
-    response_generator = agent.run_gradio_chat(
-        updated_history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round
-    )
-
-    collected = ""
-    for chunk in response_generator:
-        if isinstance(chunk, dict):
-            collected += chunk.get("content", "")
-        else:
-            collected += str(chunk)
-
-    return history + [{"role": "user", "content": message}, {"role": "assistant", "content": collected}]
+def format_response(history, message):
+    """Properly format responses for Gradio Chatbot"""
+    if isinstance(message, (str, dict)):
+        return history + [[None, str(message)]]
+    elif hasattr(message, '__iter__'):
+        full_response = ""
+        for chunk in message:
+            if isinstance(chunk, dict):
+                full_response += chunk.get("content", "")
+            else:
+                full_response += str(chunk)
+        return history + [[None, full_response]]
+    return history + [[None, str(message)]]
 
 def create_demo(agent):
-    with gr.Blocks(css=chat_css) as demo:
-        chatbot = gr.Chatbot(label="TxAgent", type="messages")
-        with gr.Row():
-            msg = gr.Textbox(label="Your question")
-        with gr.Row():
-            temp = gr.Slider(0, 1, value=0.3, label="Temperature")
-            max_new_tokens = gr.Slider(128, 4096, value=1024, label="Max New Tokens")
-            max_tokens = gr.Slider(128, 81920, value=81920, label="Max Total Tokens")
-            max_rounds = gr.Slider(1, 30, value=30, label="Max Rounds")
-            multi_agent = gr.Checkbox(label="Multi-Agent Mode")
-        with gr.Row():
-            submit = gr.Button("Ask TxAgent")
-
-        submit.click(
-            respond,
-            inputs=[msg, chatbot, temp, max_new_tokens, max_tokens, multi_agent, gr.State([]), max_rounds],
-            outputs=[chatbot]
+    """Create the Gradio interface with proper message handling"""
+    with gr.Blocks() as demo:
+        gr.Markdown(DESCRIPTION)
+        gr.Markdown(INTRO)
+        
+        chatbot = gr.Chatbot(
+            height=800,
+            label='TxAgent',
+            show_copy_button=True,
+            bubble_full_width=False
         )
+        
+        msg = gr.Textbox(label="Input", placeholder="Type your question...")
+        clear = gr.ClearButton([msg, chatbot])
+        
+        def respond(message, chat_history):
+            try:
+                # Convert Gradio history to agent format
+                agent_history = []
+                for user_msg, bot_msg in chat_history:
+                    if user_msg:
+                        agent_history.append({"role": "user", "content": user_msg})
+                    if bot_msg:
+                        agent_history.append({"role": "assistant", "content": bot_msg})
+                
+                # Get response from agent
+                response = agent.run_gradio_chat(
+                    agent_history + [{"role": "user", "content": message}],
+                    temperature=0.3,
+                    max_new_tokens=1024,
+                    max_tokens=81920,
+                    multi_agent=False,
+                    conversation=[],
+                    max_round=30
+                )
+                
+                # Format the response properly
+                full_response = ""
+                for chunk in response:
+                    if isinstance(chunk, dict):
+                        full_response += chunk.get("content", "")
+                    else:
+                        full_response += str(chunk)
+                
+                return chat_history + [(message, full_response)]
+            
+            except Exception as e:
+                logger.error(f"Error in response handling: {str(e)}")
+                return chat_history + [(message, f"Error: {str(e)}")]
+
+        msg.submit(respond, [msg, chatbot], [chatbot])
+        clear.click(lambda: [], None, [chatbot])
+
+        # Add settings section
+        with gr.Accordion("Settings", open=False):
+            gr.Markdown("Adjust model parameters here")
+            
+            with gr.Row():
+                temperature = gr.Slider(0, 1, value=0.3, label="Temperature")
+                max_new_tokens = gr.Slider(128, 4096, value=1024, step=1, label="Max New Tokens")
+            
+            with gr.Row():
+                max_tokens = gr.Slider(128, 32000, value=81920, step=1, label="Max Tokens")
+                max_round = gr.Slider(1, 50, value=30, step=1, label="Max Round")
+
     return demo
 
 def main():
+    """Main application entry point"""
     try:
-        global agent
         agent = create_agent()
         demo = create_demo(agent)
-        demo.launch()
+        demo.launch(server_name="0.0.0.0", server_port=7860)
     except Exception as e:
         logger.error(f"Application failed to start: {str(e)}")
         raise