First iteration of question answering agent

.gitignore

@@ -0,0 +1,3 @@
+# Environment
+.env
+venv/

agent.py

@@ -0,0 +1,69 @@
+from typing import List, TypedDict, Annotated
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
+from langgraph.graph.message import add_messages
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain.tools import Calculator
+from dotenv import load_dotenv
+
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage], add_messages]
+
+
+search_tool = DuckDuckGoSearchRun()
+calculator = Calculator()
+tools = [search_tool, calculator]
+
+load_dotenv()
+llm = ChatOpenAI("gpt-4o")
+llm_with_tools = llm.bind_tools(tools)
+
+
+def assistant(state: AgentState):
+    system_prompt = """
+    You are a well-educated research assistant with access to the web and a calculator. 
+    Please answer the questions by outputting only the answer and nothing else.
+    """
+
+    system_message = SystemMessage(content=system_prompt)
+
+    return {
+        "messages": [llm_with_tools.invoke([system_message] + state["messages"])],
+    }
+
+
+
+class Agent:
+    """
+    A research assistant capable of searching the web and basic arithmetics.
+    """
+
+    def __init__(self):
+        """
+        Initializes the agent.
+        """
+        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()
+
+    def __call__(self, question: str) -> str:
+        """
+        Answers a given question.
+
+        Args: 
+            question (str): Question to be answered.
+
+        Returns:
+            str: The answer to the question.
+        """
+        response = self.agent.invoke({"messages": [HumanMessage(content=f"Question:\n {question}")]})
+        return response['messages'][-1].content

app.py

@@ -1,7 +1,6 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
 # (Keep Constants as is)
@@ -10,6 +9,7 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+from agent import *
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")