Update app.py

app.py

@@ -11,6 +11,115 @@ import pandas as pd
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
+# ==============================================================================
+# 1. IMPORTS AND SETUP
+# ==============================================================================
+import os
+from dotenv import load_dotenv
+from typing import TypedDict, Annotated, List
+
+# LangChain and LangGraph imports
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_experimental.tools import PythonREPLTool
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langgraph.graph import StateGraph, END
+from langgraph.prebuilt import ToolNode
+
+# ==============================================================================
+# 2. LOAD API KEYS AND DEFINE TOOLS
+# ==============================================================================
+load_dotenv()
+hf_token = os.getenv("HF_TOKEN")
+tavily_api_key = os.getenv("TAVILY_API_KEY")
+
+if not hf_token or not tavily_api_key:
+    # This will show a clear error in the logs if keys are missing
+    raise ValueError("HF_TOKEN or TAVILY_API_KEY not set. Please add them to your Space secrets.")
+os.environ["TAVILY_API_KEY"] = tavily_api_key
+
+# The agent's tools
+tools = [TavilySearchResults(max_results=3, description="A search engine for finding up-to-date information on the web."), PythonREPLTool()]
+tool_node = ToolNode(tools)
+
+# ==============================================================================
+# 3. CONFIGURE THE LLM (THE "BRAIN")
+# ==============================================================================
+# The model we'll use as the agent's brain
+repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# The system prompt gives the agent its mission and instructions
+SYSTEM_PROMPT = """You are a highly capable AI agent named 'GAIA-Solver'. Your mission is to accurately answer complex questions.
+**Your Instructions:**
+1.  **Analyze:** Carefully read the user's question to understand all parts of what is being asked.
+2.  **Plan:** Think step-by-step. Break the problem into smaller tasks. Decide which tool is best for each task. (e.g., use 'tavily_search_results_json' for web searches, use 'python_repl' for calculations or code execution).
+3.  **Execute:** Call ONE tool at a time.
+4.  **Observe & Reason:** After getting a tool's result, observe it. Decide if you have the final answer or if you need to use another tool.
+5.  **Final Answer:** Once you are confident, provide a clear, direct, and concise final answer. Do not include your thought process in the final answer.
+"""
+
+# Initialize the LLM endpoint
+llm = HuggingFaceEndpoint(
+    repo_id=repo_id,
+    huggingfacehub_api_token=hf_token,
+    temperature=0, # Set to 0 for deterministic, less random output
+    max_new_tokens=2048,
+)
+
+# ==============================================================================
+# 4. BUILD THE LANGGRAPH AGENT
+# ==============================================================================
+
+# Define the Agent's State (its memory)
+class AgentState(TypedDict):
+    messages: Annotated[List[BaseMessage], lambda x, y: x + y]
+
+# This is a more robust way to combine the prompt, model, and tool binding
+# It ensures the system prompt is always used.
+llm_with_tools = llm.bind_tools(tools)
+
+# Define the Agent Node
+def agent_node(state):
+    # Get the last message to pass to the model
+    last_message = state['messages'][-1]
+
+    # Prepend the system prompt to every call
+    prompt_with_system = [
+        HumanMessage(content=SYSTEM_PROMPT, name="system_prompt"),
+        last_message
+    ]
+
+    response = llm_with_tools.invoke(prompt_with_system)
+    return {"messages": [response]}
+
+# Define the Edge Logic
+def should_continue(state):
+    last_message = state["messages"][-1]
+    if last_message.tool_calls:
+        return "tools" # Route to the tool node
+    return END # End the process
+
+# Assemble the graph
+workflow = StateGraph(AgentState)
+workflow.add_node("agent", agent_node)
+workflow.add_node("tools", tool_node)
+workflow.set_entry_point("agent")
+workflow.add_conditional_edges(
+    "agent",
+    should_continue,
+    {"tools": "tools", "end": END},
+)
+workflow.add_edge("tools", "agent")
+
+# Compile the graph into a runnable app
+app = workflow.compile()
+
+
+# ==============================================================================
+# 5. THE BASICAGENT CLASS (FOR THE TEST HARNESS)
+# This MUST be at the end, after `app` is defined.
+# ==============================================================================
 class BasicAgent:
     """
     This is the agent class that the GAIA test harness will use.