Update app.py

app.py

@@ -7,11 +7,12 @@ from typing import Dict, Any, List, TypedDict, Optional
 from langgraph.graph import Graph, StateGraph
 from langgraph.prebuilt import ToolNode
 from tools import simple_search
+from langchain_community.llms import HuggingFaceEndpoint
+
 print("trial")
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-MODEL_API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-7B-Instruct"
 HF_TOKEN = os.getenv("HF_TOKEN")  # Make sure to set this environment variable
 
 class AgentState(TypedDict):
@@ -25,21 +26,30 @@ class AgentState(TypedDict):
 
 class BasicAgent:
     def __init__(self):
-        print("Initializing BasicAgent with Qwen2.5-Coder-32B-Instruct API...")
+        print("Initializing BasicAgent with Qwen2.5-Coder-7B-Instruct...")
         if not HF_TOKEN:
             raise ValueError("HF_TOKEN environment variable not set. Please set your Hugging Face API token.")
         
-        # Set up headers for API calls
-        self.headers = {
-            "Authorization": f"Bearer {HF_TOKEN}",
-            "Content-Type": "application/json"
-        }
+        # Initialize LLM
+        self.llm = HuggingFaceEndpoint(
+            repo_id="Qwen/Qwen2.5-Coder-7B-Instruct",
+            huggingfacehub_api_token=HF_TOKEN
+        )
         
         # Create the agent workflow
         print("Creating workflow variable")
         self.workflow = self._create_workflow()
         print("BasicAgent initialization complete.")
 
+    def _call_llm_api(self, prompt: str) -> str:
+        """Call the Qwen model through the Hugging Face API."""
+        try:
+            response = self.llm.invoke(prompt)
+            return response
+        except Exception as e:
+            print(f"Error calling LLM API: {e}")
+            return f"Error getting response from LLM: {str(e)}"
+
     def _create_workflow(self) -> Graph:
         """Create the agent workflow using LangGraph."""
         # Create the workflow with state schema
@@ -79,20 +89,6 @@ class BasicAgent:
         
         return workflow.compile()
 
-    def _call_llm_api(self, prompt: str) -> str:
-        """Call the Qwen model through the Hugging Face API."""
-        try:
-            response = requests.post(
-                MODEL_API_URL,
-                headers=self.headers,
-                json={"inputs": prompt, "parameters": {"max_length": 200}}
-            )
-            response.raise_for_status()
-            return response.json()[0]["generated_text"]
-        except Exception as e:
-            print(f"Error calling LLM API: {e}")
-            return f"Error getting response from LLM: {str(e)}"
-
     def _analyze_question(self, state: AgentState) -> AgentState:
         """Analyze the question and determine the next step."""
         prompt = f"""Analyze this question and determine what needs to be done: {state['question']}