Update app.py

app.py

@@ -1,23 +1,61 @@
+import inspect
 import os
+
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
+import requests
+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.llm = HuggingFaceEndpoint(repo_id="Qwen/Qwen2.5-Coder-32B-Instruct", huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,)
+        self.chat = ChatHuggingFace(llm=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 = 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."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        messages = [HumanMessage(content=question)]
+        messages = self.chat_with_tools.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """