Update app.py

app.py

@@ -1,14 +1,14 @@
 import os
-from llama_index.core.agent import ReActAgent
-from llama_index.core.tools import FunctionTool
-from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
-from llama_index.core import Settings
 import gradio as gr
 import datetime
 import pytz
+# Ensure we are using the core agent class
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 
-# 1. Setup LLM (Using the Hugging Face Inference API)
-# Make sure to add HF_TOKEN to your Space "Secrets"
+# 1. Setup LLM
+# Reminder: Add HF_TOKEN to your Space "Secrets" in Settings
 hf_token = os.getenv("HF_TOKEN")
 
 llm = HuggingFaceInferenceAPI(
@@ -16,7 +16,7 @@ llm = HuggingFaceInferenceAPI(
     token=hf_token
 )
 
-# 2. Define your Tools (Required for Unit 2)
+# 2. Define your Tools
 def get_tokyo_time() -> str:
     """Returns the current time in Tokyo, Japan."""
     tz = pytz.timezone('Asia/Tokyo')
@@ -26,18 +26,31 @@ def multiply(a: float, b: float) -> float:
     """Multiplies two numbers and returns the result."""
     return a * b
 
-# Wrap them into LlamaIndex tools
+# Wrap tools
 tools = [
     FunctionTool.from_defaults(fn=get_tokyo_time),
     FunctionTool.from_defaults(fn=multiply)
 ]
 
-# 3. Create the Agent
-agent = ReActAgent.from_tools(tools, llm=llm, verbose=True)
+# 3. Create the Agent 
+# Note: Using ReActAgent.from_tools is correct for the core class
+agent = ReActAgent.from_tools(
+    tools, 
+    llm=llm, 
+    verbose=True
+)
 
 # 4. Gradio Interface
 def chat(message, history):
+    # The agent handles the conversation state internally
     response = agent.chat(message)
     return str(response)
 
-gr.ChatInterface(chat, title="Unit 2: LlamaIndex Agent").launch()
+demo = gr.ChatInterface(
+    fn=chat, 
+    title="Unit 2: LlamaIndex Agent",
+    description="I am a LlamaIndex agent with access to Tokyo time and multiplication tools."
+)
+
+if __name__ == "__main__":
+    demo.launch()