update

.gitignore

@@ -0,0 +1 @@
+.env

agent.py

@@ -1,14 +1,17 @@
 from typing import Annotated, TypedDict
 
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_community.llms import HuggingFaceEndpoint
 from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 from langgraph.graph import START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
 
 from retriever import guest_info_tool
-from tools import (absolute, add, divide, exponential, floor_divide, logarithm,
-                   modulus, multiply, power, square_root, subtract, web_search)
+from tools import (absolute, add, divide, exponential, floor_divide,
+                   get_current_time_in_timezone, logarithm, modulus, multiply,
+                   power, roman_calculator_converter, square_root, subtract,
+                   web_search)
 
 # Generate the chat interface, including the tools
 llm = HuggingFaceEndpoint(
@@ -17,7 +20,6 @@ llm = HuggingFaceEndpoint(
 )
 
 chat = ChatHuggingFace(llm=llm, verbose=True)
-tools = [, weather_info_tool, hub_stats_tool]
 tools = [
     multiply,
     add,
@@ -31,6 +33,8 @@ tools = [
     logarithm,
     exponential,
     web_search,
+    roman_calculator_converter,
+    get_current_time_in_timezone,
 ]
 
 chat_with_tools = chat.bind_tools(tools)

app.py

@@ -4,24 +4,58 @@ import os
 import gradio as gr
 import pandas as pd
 import requests
-from agent import build_graph
+from dotenv import load_dotenv
+from langchain_community.chat_models import ChatHuggingFace
+from langchain_community.llms import HuggingFaceEndpoint
 from langchain_core.messages import HumanMessage
 
+from tools import (absolute, add, divide, exponential, floor_divide,
+                   get_current_time_in_timezone, logarithm, modulus, multiply,
+                   power, roman_calculator_converter, square_root, subtract,
+                   web_search)
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+MAX_AGENT_ITERATIONS = 15
+
+
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-        self.graph = build_graph()
+        self.llm = HuggingFaceEndpoint(repo_id="Qwen/Qwen2.5-Coder-32B-Instruct", huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,)
+        self.chat = ChatHuggingFace(llm=self.llm, verbose=True)
+        self.tools = [
+            multiply,
+            add,
+            subtract,
+            power,
+            divide,
+            modulus,
+            square_root,
+            floor_divide,
+            absolute,
+            logarithm,
+            exponential,
+            web_search,
+            roman_calculator_converter,
+            get_current_time_in_timezone,
+        ]
+        self.chat_with_tools = self.chat.bind_tools(tools)
+        print(f"Total tools available: {len(self.tools)}")
+
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
+
+        # Question in a HumanMessage from langchain_core
         messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
+        messages = self.chat_with_tools.invoke({"messages": messages})
         answer = messages['messages'][-1].content
         return answer[14:]
 

requirements.txt

@@ -2,8 +2,13 @@ gradio
 requests
 
 # LangChain dependencies
+langchain
 langchain-core
-
+langchain-community
+langgraph
+huggingface_hub
 
 #other
 # inspect
+python-dotenv
+gradio[oauth]

tools.py

@@ -180,6 +180,7 @@ def exponential(x: float) -> float:
     """
     return math.exp(x)
 
+
 @tool
 def web_search(query: str) -> str:
     """Performs a DuckDuckGo search for the given query and returns the results.
@@ -193,3 +194,53 @@ def web_search(query: str) -> str:
     search_tool = DuckDuckGoSearchRun()
     return search_tool.invoke(query)
 
+
+@tool
+def roman_calculator_converter(value1:int, value2:int, oper:str) -> str:
+    """A tool that performs an operator on 2 numbers to calculate the result
+    Args:
+        value1: the first value
+        value2: the second value
+        oper: operator for the calculation, like "add", "substract", "multiply", "divide"
+    """
+    roman_numerals = {
+        1000: "M", 900: "CM", 500: "D", 400: "CD",
+        100: "C", 90: "XC", 50: "L", 40: "XL",
+        10: "X", 9: "IX", 5: "V", 4: "IV", 1: "I"
+    }
+    roman_string = ""
+    
+    if oper == "add":
+        result = value1 + value2
+    elif oper == "subtract":
+        result = value2 - value1
+    elif oper == "divide":
+        result = value1 / value2
+    elif oper == "multiply":
+        result = value1 * value2
+
+    else:
+        return "Unsupported operation. Please use 'add' or 'subtract'."
+    
+    for value, numeral in roman_numerals.items():
+        while result >= value:
+            roman_string += numeral
+            result -= value
+    return f"The result of {oper} on the values {value1} and {value2} is the Roman numeral: {roman_string}"
+
+
+@tool
+def get_current_time_in_timezone(timezone: str) -> str:
+    """A tool that fetches the current local time in a specified timezone.
+    Args:
+        timezone: A string representing a valid timezone (e.g., 'America/New_York').
+    """
+    try:
+        # Create timezone object
+        tz = pytz.timezone(timezone)
+        # Get current time in that timezone
+        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
+        return f"The current local time in {timezone} is: {local_time}"
+    except Exception as e:
+        return f"Error fetching time for timezone '{timezone}': {str(e)}"
+