updated new app.py and agent.py

to keep app.py as original as possible.

agent.py

@@ -0,0 +1,78 @@
+import os
+
+from dotenv import load_dotenv
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langgraph.graph import START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode, tools_condition
+
+from tools import (absolute, add, compound_interest, convert_temperature,
+                   divide, exponential, factorial, floor_divide,
+                   get_current_time_in_timezone, greatest_common_divisor,
+                   is_prime, least_common_multiple, logarithm, modulus,
+                   multiply, percentage_calculator, power,
+                   roman_calculator_converter, square_root, subtract,
+                   web_search)
+
+# Load Constants
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+
+tools = [
+    multiply, add, subtract, power, divide, modulus,
+    square_root, floor_divide, absolute, logarithm,
+    exponential, web_search, roman_calculator_converter,
+    get_current_time_in_timezone, compound_interest,
+    convert_temperature, factorial, greatest_common_divisor,
+    is_prime, least_common_multiple, percentage_calculator
+]
+
+def build_graph():
+    """Build the graph""" 
+
+    # First create the HuggingFaceEndpoint
+    llm_endpoint = HuggingFaceEndpoint(
+        repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+        huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
+        temperature=0.1,  # Lower temperature for more consistent responses
+        max_new_tokens=1024,
+        timeout=30,
+        )
+    
+    # Then wrap it with ChatHuggingFace to get chat model functionality
+    llm = ChatHuggingFace(llm=llm_endpoint)
+
+    # Bind tools to LLM
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Node
+    def assistant(state: MessagesState):
+        """Assistant node"""
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+
+    builder = StateGraph(MessagesState)
+    builder.add_node("assistant", assistant)
+    builder.add_node("tools", ToolNode(tools))
+    builder.add_conditional_edges(
+        "assistant",
+        tools_condition,
+    )
+    builder.add_edge(START, "assistant")
+    builder.add_edge("tools", "assistant")
+
+     # Compile graph
+    return builder.compile()
+
+# test
+if __name__ == "__main__":
+    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
+    # Build the graph
+    graph = build_graph()
+    # Run the graph
+    messages = [HumanMessage(content=question)]
+    messages = graph.invoke({"messages": messages})
+    for m in messages["messages"]:
+        m.pretty_print()

app.py

@@ -4,6 +4,10 @@ import os
 import gradio as gr
 import pandas as pd
 import requests
+# Additional libraries
+from langchain_core.messages import HumanMessage
+
+from agent import build_graph
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -14,11 +18,18 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 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
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
+
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """

requirements.txt

@@ -1,5 +1,5 @@
 # UI and OAuth
-gradio[oauth]
+gradio
 requests
 pandas
 
@@ -8,6 +8,7 @@ langchain
 langchain-core
 langchain-community
 langgraph
+langchain-huggingface
 
 # Hugging Face integration
 huggingface_hub

tools.py

@@ -1,6 +1,5 @@
 import datetime
 import math
-import re
 from typing import Union
 
 import pytz
@@ -350,9 +349,7 @@ def get_current_time_in_timezone(timezone: str) -> str:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
 
-# Additional utility tools that might be helpful
-
-@tool  
+@tool
 def factorial(n: int) -> Union[int, str]:
     """Calculates the factorial of a non-negative integer.
     
@@ -551,4 +548,3 @@ def convert_temperature(value: Union[int, float], from_unit: str, to_unit: str)
         return round(result, 2)
     except Exception as e:
         return f"Error converting temperature: {str(e)}"
-