add initial agent

.gitignore

@@ -1 +1,5 @@
-venv/
+venv/
+.env
+__pycache__/
+*.py[cod]
+*$py.class

agent.py

@@ -0,0 +1,197 @@
+import os
+from dotenv import load_dotenv
+
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.document_loaders import WikipediaLoader
+
+from langgraph.graph import StateGraph, START, END, MessagesState
+from langgraph.prebuilt import ToolNode, tools_condition
+
+load_dotenv()
+
+
+@tool
+def add(a: float, b: float) -> float:
+    """Add two numbers together.
+
+    Args:
+        a: First number
+        b: Second number
+    """
+    return a + b
+
+
+@tool
+def subtract(a: float, b: float) -> float:
+    """Subtract b from a.
+
+    Args:
+        a: Number to subtract from
+        b: Number to subtract
+    """
+    return a - b
+
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers together.
+
+    Args:
+        a: First number
+        b: Second number
+    """
+    return a * b
+
+
+@tool
+def divide(a: float, b: float) -> float:
+    """Divide a by b.
+
+    Args:
+        a: Dividend
+        b: Divisor
+    """
+    if b == 0:
+        return "Error: Division by zero"
+    return a / b
+
+
+@tool
+def modulo(a: float, b: float) -> float:
+    """Return the remainder of a divided by b.
+
+    Args:
+        a: Dividend
+        b: Divisor
+    """
+    if b == 0:
+        return "Error: Division by zero"
+    return a % b
+
+
+@tool
+def power(a: float, b: float) -> float:
+    """Raise a to the power of b.
+
+    Args:
+        a: Base number
+        b: Exponent
+    """
+    return a ** b
+
+
+@tool
+def square_root(a: float) -> float:
+    """Calculate the square root of a number.
+
+    Args:
+        a: Number to calculate square root of
+    """
+    if a < 0:
+        return "Error: Cannot calculate square root of negative number"
+    return a ** 0.5
+
+
+@tool
+def web_search(query: str) -> str:
+    """Search the web for current information and facts.
+
+    Args:
+        query: Search query string
+    """
+    try:
+        search_tool = TavilySearchResults(max_results=3)
+        results = search_tool.invoke(query)
+
+        if not results:
+            return "No search results found."
+
+        formatted_results = []
+        for i, result in enumerate(results, 1):
+            title = result.get('title', 'No title')
+            content = result.get('content', 'No content')
+            url = result.get('url', 'No URL')
+            formatted_results.append(f"{i}. {title}\n{content}\nSource: {url}")
+
+        return "\n\n ==== \n\n".join(formatted_results)
+    except Exception as e:
+        return f"Error performing search: {str(e)}"
+
+
+@tool
+def wikipedia_search(query: str) -> str:
+    """Search Wikipedia for factual information.
+
+    Args:
+        query: Wikipedia search query
+    """
+    try:
+        loader = WikipediaLoader(query=query, load_max_docs=2)
+        docs = loader.load()
+
+        if not docs:
+            return "No Wikipedia results found."
+
+        formatted_docs = []
+        for i, doc in enumerate(docs, 1):
+            title = doc.metadata.get('title', 'No title')
+            content = doc.page_content
+            formatted_docs.append(f"{i}. {title}\n{content}")
+
+        return "\n\n ==== \n\n".join(formatted_docs)
+    except Exception as e:
+        return f"Error searching Wikipedia: {str(e)}"
+
+
+tools = [
+    add, subtract, multiply, divide, modulo, power, square_root,
+    web_search, wikipedia_search
+]
+
+
+def get_llm():
+    """Initialize the llm"""
+    return ChatGoogleGenerativeAI(
+        model="gemini-2.5-pro",
+        temperature=0,
+        api_key=os.getenv("GEMINI_API_KEY")
+    )
+
+
+def call_model(state: MessagesState):
+    """Call the LLM with the current state.
+
+    Args:
+        state: Current state containing messages
+    """
+    llm = get_llm()
+    llm_with_tools = llm.bind_tools(tools)
+
+    messages = state['messages']
+    response = llm_with_tools.invoke(messages)
+
+    return {"messages": [response]}
+
+
+def build_graph():
+    """Build and return the LangGraph workflow."""
+    workflow = StateGraph(MessagesState)
+
+    workflow.add_node("agent", call_model)
+    workflow.add_node("tools", ToolNode(tools))
+
+    workflow.add_edge(START, "agent")
+    workflow.add_conditional_edges("agent", tools_condition)
+    workflow.add_edge("tools", "agent")
+
+    return workflow.compile()
+
+
+if __name__ == "__main__":
+    graph = build_graph()
+    test_message = [HumanMessage(content="What is 15 + 27?")]
+    result = graph.invoke({"messages": test_message})
+    print(f"Test result: {result['messages'][-1].content}")

app.py

@@ -3,21 +3,23 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from langchain_core.messages import HumanMessage
 
 # (Keep Constants as is)
 # --- Constants ---
 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 build_graph
+
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        self.graph = build_graph()
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        return result['messages'][-1].content
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

requirements.txt

@@ -1,2 +1,12 @@
 gradio
-requests
+requests
+langgraph
+langchain
+langchain-community
+langchain-tavily
+langchain-core
+langchain-google-genai
+wikipedia
+google-genai
+python-dotenv
+pandas