Update app.py

app.py

@@ -1,126 +1,10 @@
 import gradio as gr
-import logging
 
-# Delay heavy imports until later to avoid multiprocessing conflicts
-tx_app = None  # Global agent instance
+def echo(msg, history):
+    return history + [(msg, f"Echo: {msg}")]
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+with gr.Blocks() as app:
+    chatbot = gr.Chatbot(label="EchoBot", type="messages")
+    msg = gr.Textbox(label="Type a message")
 
-# ========== Dummy Response (will be replaced by real agent later) ==========
-def respond(message, chat_history, temperature, max_new_tokens, max_tokens, multi_agent, conversation_state, max_round):
-    global tx_app
-    if tx_app is None:
-        return chat_history + [("", "⚠️ Model is still loading. Please wait a few seconds and try again.")]
-
-    try:
-        if not isinstance(message, str) or len(message.strip()) < 10:
-            return chat_history + [("", "Please enter a longer message.")]
-        
-        # Convert chat format if needed
-        if chat_history and isinstance(chat_history[0], dict):
-            chat_history = [(h["role"], h["content"]) for h in chat_history if "role" in h and "content" in h]
-
-        response = ""
-        for chunk in tx_app.run_gradio_chat(
-            message=message.strip(),
-            history=chat_history,
-            temperature=temperature,
-            max_new_tokens=max_new_tokens,
-            max_token=max_tokens,
-            call_agent=multi_agent,
-            conversation=conversation_state,
-            max_round=max_round,
-            seed=42,
-        ):
-            if isinstance(chunk, dict):
-                response += chunk.get("content", "")
-            elif isinstance(chunk, str):
-                response += chunk
-            else:
-                response += str(chunk)
-
-            yield chat_history + [("user", message), ("assistant", response)]
-
-    except Exception as e:
-        logger.error(f"Respond error: {e}")
-        yield chat_history + [("", f"⚠️ Error: {e}")]
-
-# ========== Gradio UI ==========
-with gr.Blocks(title="TxAgent Biomedical Assistant") as app:
-    gr.Markdown("# 🧠 TxAgent Biomedical Assistant")
-
-    chatbot = gr.Chatbot(label="Conversation", height=600, type="messages")
-
-    msg = gr.Textbox(
-        label="Your medical query",
-        placeholder="Enter your biomedical question...",
-        lines=3
-    )
-
-    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=10, label="Max Rounds")
-        multi_agent = gr.Checkbox(label="Multi-Agent Mode")
-
-    submit = gr.Button("Submit")
-    clear = gr.Button("Clear")
-    conversation_state = gr.State([])
-
-    submit.click(
-        respond,
-        [msg, chatbot, temp, max_new_tokens, max_tokens, multi_agent, conversation_state, max_rounds],
-        chatbot
-    )
-    clear.click(lambda: [], None, chatbot)
-    msg.submit(
-        respond,
-        [msg, chatbot, temp, max_new_tokens, max_tokens, multi_agent, conversation_state, max_rounds],
-        chatbot
-    )
-
-    # === Hidden trigger to load model safely on app start ===
-    init_button = gr.Button(visible=False)
-
-    def load_model():
-        global tx_app
-        import torch
-        from txagent import TxAgent
-        from importlib.resources import files
-
-        logger.info("🔧 Loading full TxAgent model...")
-
-        tool_files = {
-            "opentarget": str(files('tooluniverse.data').joinpath('opentarget_tools.json')),
-            "fda_drug_label": str(files('tooluniverse.data').joinpath('fda_drug_labeling_tools.json')),
-            "special_tools": str(files('tooluniverse.data').joinpath('special_tools.json')),
-            "monarch": str(files('tooluniverse.data').joinpath('monarch_tools.json'))
-        }
-
-        tx_app = TxAgent(
-            model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
-            rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
-            tool_files_dict=tool_files,
-            enable_finish=True,
-            enable_rag=True,
-            enable_summary=False,
-            init_rag_num=0,
-            step_rag_num=10,
-            summary_mode='step',
-            summary_skip_last_k=0,
-            summary_context_length=None,
-            force_finish=True,
-            avoid_repeat=True,
-            seed=42,
-            enable_checker=True,
-            enable_chat=False,
-            additional_default_tools=["DirectResponse", "RequireClarification"]
-        )
-
-        tx_app.init_model()
-        logger.info("✅ Model initialized successfully")
-        return gr.update(visible=False)
-
-    app.load(init_button.click(fn=load_model))
+    msg.submit(echo, [msg, chatbot], chatbot)