add the code for agent

.env

@@ -0,0 +1,2 @@
+SERPAPI_KEY = "046aa74b206478f196088246569b77000619bcae1c5ccda5d0143ce274a932e1"
+OPENAI_API_KEY = "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"

app.py

@@ -3,7 +3,10 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_core.messages import HumanMessage
+from tools import assistant, AgentState, tools
+from langgraph.graph import StateGraph, START
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -12,12 +15,27 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
+        builder = StateGraph(AgentState)
+        builder.add_node("assistant", assistant)
+        builder.add_node("tools", ToolNode(tools))
+        builder.add_edge(START, "assistant")
+        builder.add_conditional_edges("assistant", tools_condition)
+        builder.add_edge("tools", "assistant")
+        
+        self.agent = builder.compile()
+        
         print("BasicAgent initialized.")
+    
     def __call__(self, question: str) -> str:
+        
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
+        
+        human_message = [HumanMessage(content = question)]
+        fixed_answer = self.agent.invoke({"messages": human_message})
+        
         print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        answer = fixed_answer["messages"][-1].content
+        return answer[14:]
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

pyproject.toml

@@ -3,7 +3,7 @@ name = "poetry-gaia"
 version = "0.1.0"
 description = "GAIA Benchmarking"
 authors = [
-    {name = "sharath", email = ""}
+    {name = "sharath"}
 ]
 readme = "README.md"
 requires-python = ">=3.9"
@@ -12,7 +12,15 @@ dependencies = [
 "requests",
 "pandas",
 "numpy",
-"langgraph"
+"langgraph",
+"openai",
+"tqdm",
+"pydantic",
+"langchain",
+"langchain-openai",
+"serpapi",
+"python-dotenv",
+"google-search-results"
 ]
 
 [build-system]

tools.py

@@ -1,25 +1,84 @@
-from typing import List, TypedDict, Annotated, Optional
+from typing import List, TypedDict, Annotated, Optional, Dict, Union
 from langchain_openai import ChatOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage, AnyMessage
 from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode, tools_condition
+from serpapi import GoogleSearch
+from dotenv import load_dotenv
+import os
 
+load_dotenv()
 class AgentState(TypedDict):
     """Agent state to be passed to the tool."""
     messages: Annotated[List[AnyMessage], add_messages]
     
-def add(a: int, b: int) -> int:
+def add(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
     """Add two numbers."""
     return a + b
-def subtract(a: int, b: int) -> int:
+def subtract(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
     """Subtract two numbers."""
     return a - b
-def multiply(a: int, b: int) -> int:
+def multiply(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
     """Multiply two numbers."""
     return a * b
-def divide(a: int, b: int) -> Optional[float]:
+def divide(a: Union[float , int], b: Union[float , int]) -> Union[float , int , None]:
     """Divide two numbers."""
     if b == 0:
         return None
     return a / b
-    
+
+def web_search(query: str) -> str: 
+    """Perform a web search using SerpAPI."""
+    params = {
+        "engine": "google",
+        "q": query,
+        "api_key": os.getenv("SERPAPI_API_KEY"),
+        "num": 5
+    }
+    search = GoogleSearch(params)
+    results = search.get_dict()["organic_results"]
+    context = "\n---\n".join([
+        "Title: " + result['title'] + "\nLink: " + result['link'] + "\nSnippet: " + result.get('snippet', 'No snippet available')
+        for result in results if 'title' in result and 'link' in result
+    ]
+    )
+    return context if context else "No results found."
+
+llm = ChatOpenAI(model = "gpt-4o")
+tools = [add, subtract, divide, web_search]
+llm_with_tools = llm.bind_tools(tools, parallel_tool_calls = False)
+
+def assistant(state: AgentState) -> Dict:
+    
+    system_message = """
+    You are a helpful assistant tasked with answering questions using a set of tools. 
+Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
+FINAL ANSWER: [YOUR FINAL ANSWER]. 
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
+"""
+    tools_description = """
+    
+    You have the following tools available to perform actions
+    
+    websearch(query: str) -> str:
+    Args:
+        query: Search query 
+    Returns:
+        A string containing 5 relevant search results
+    
+    add(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
+        Add two numbers
+    
+    subtract(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
+        Subtract two numbers
+    
+    multiply(a: Union[float , int], b: Union[float , int]) -> Union[float , int]:
+        Multiply two numbers
+    
+    divide(a: Union[float , int], b: Union[float , int]) -> Union[float , int , None]:
+        Divide two numbers
+    """
+    
+    sys_msg = SystemMessage(content=system_message + tools_description)
+    
+    return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}