Update app.py

app.py

@@ -1,4 +1,6 @@
 import random
+import datetime
+import sys
 import os
 import torch
 import logging
@@ -15,15 +17,16 @@ 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"
 
-# Configuration - Update paths as needed
+# Configuration
 CONFIG = {
     "model_name": "mims-harvard/TxAgent-T1-Llama-3.1-8B",
     "rag_model_name": "mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
-    "embedding_filename": "path_to_your_embeddings.pt",  # Update this path
+    "embedding_filename": "ToolRAG-T1-GTE-Qwen2-1.5Btool_embedding_47dc56b3e3ddeb31af4f19defdd538d984de1500368852a0fab80bc2e826c944.pt",
     "tool_files": {
         "opentarget": str(files('tooluniverse.data').joinpath('opentarget_tools.json')),
         "fda_drug_label": str(files('tooluniverse.data').joinpath('fda_drug_labeling_tools.json')),
@@ -33,62 +36,166 @@ CONFIG = {
     }
 }
 
-def safe_load_embeddings(filepath: str):
-    """Handle embedding loading with fallbacks"""
+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:
-        # Try with weights_only=True first
+        # First try with weights_only=True
         return torch.load(filepath, weights_only=True)
     except Exception as e:
-        logger.warning(f"Secure load failed, trying without weights_only: {str(e)}")
+        logger.warning(f"Secure load failed, trying with weights_only=False: {str(e)}")
         try:
+            # Fallback to unsafe load if needed
             return torch.load(filepath, weights_only=False)
         except Exception as e:
             logger.error(f"Failed to load embeddings: {str(e)}")
             return None
 
-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 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"])
+                
+                # 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
+                
+                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 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...")
+        logger.info("Generating tool list using ToolUniverse...")
         try:
             tu = ToolUniverse()
-            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")
+            if hasattr(tu, 'get_all_tools'):
+                tools = tu.get_all_tools()
+            elif hasattr(tu, 'tools'):
+                tools = tu.tools
             else:
-                logger.error("No tools could be loaded")
+                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"Tool file preparation failed: {str(e)}")
+            logger.error(f"Failed to prepare tool files: {str(e)}")
 
 def create_agent():
-    """Create and initialize the TxAgent with robust error handling"""
+    """Create and initialize the TxAgent"""
+    # Apply the embedding patch before creating the agent
+    patch_embedding_loading()
     prepare_tool_files()
-    
+
+    # Initialize the agent
     try:
         agent = TxAgent(
-            model_name=CONFIG["model_name"],
-            rag_model_name=CONFIG["rag_model_name"],
+            CONFIG["model_name"],
+            CONFIG["rag_model_name"],
             tool_files_dict=CONFIG["tool_files"],
             force_finish=True,
             enable_checker=True,
@@ -99,82 +206,227 @@ def create_agent():
         agent.init_model()
         return agent
     except Exception as e:
-        logger.error(f"Agent creation failed: {str(e)}")
+        logger.error(f"Failed to create agent: {str(e)}")
         raise
 
-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 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,
+        enable_summary=enable_summary,
+        init_rag_num=init_rag_num,
+        step_rag_num=step_rag_num,
+        skip_last_k=skip_last_k,
+        summary_mode=summary_mode,
+        summary_skip_last_k=summary_skip_last_k,
+        summary_context_length=summary_context_length,
+        force_finish=force_finish,
+        seed=seed,
+    )
+    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)
+    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):
-    """Create the Gradio interface with proper message handling"""
-    with gr.Blocks() as demo:
-        chatbot = gr.Chatbot(
-            height=800,
-            label='TxAgent',
-            show_copy_button=True,
-            type="messages"  # Use the modern message format
-        )
+    """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)
         
-        msg = gr.Textbox(label="Input", placeholder="Type your question...")
-        clear = gr.ClearButton([msg, chatbot])
+        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)
         
-        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)}")]
+        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]
+        )
 
-        msg.submit(respond, [msg, chatbot], [chatbot])
-        clear.click(lambda: [], None, [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
 
 def main():
-    """Main application entry point"""
+    """Main function to run the application"""
     try:
         agent = create_agent()
         demo = create_demo(agent)
-        demo.launch(server_name="0.0.0.0", server_port=7860)
+        demo.launch(share=True)
     except Exception as e:
         logger.error(f"Application failed to start: {str(e)}")
         raise