Update app.py

app.py

@@ -8,6 +8,7 @@ from smolagents import ToolCallingAgent
 from smolagents import DuckDuckGoSearchTool
 from smolagents import Tool
 import traceback
+from openai import OpenAI
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -51,18 +52,32 @@ CalculatorTool=Tool(
     description="it calculates basic arithmetics",
     func=calculatorTool
 )
+
 class BasicAgent:
     def __init__(self):
         try:
-            api_key=os.getenv("chatbotagenthf")
-            model =InferenceClientModel(model_id="HuggingFaceH4/zephyr-7b-beta",token=api_key)
+            # Load your HF token
+            hf_token = os.getenv("chatbotagenthf")
+            if not hf_token:
+                raise ValueError("HF token not found in environment variable 'chatbotagenthf'.")
+
+            # --- Connect to Hugging Face router ---
+            self.client = OpenAI(
+                base_url="https://router.huggingface.co/v1",
+                api_key=hf_token
+            )
 
+            # --- Choose any accessible instruct model ---
+            self.model_id = "moonshotai/Kimi-K2-Thinking:novita"  
+            # (You can change to: mistralai/Mistral-7B-Instruct-v0.3, zephyr-7b-beta, etc.)
+
+            # --- Define your ToolCallingAgent as before ---
             self.agent = ToolCallingAgent(
                 tools=[DuckDuckGoSearchTool()],
-                model=model
+                model=None  # Model handled manually via OpenAI client
             )
 
-            print("✅ Agent initialized successfully.")
+            print("✅ Agent initialized successfully using Hugging Face Router.")
 
         except Exception as e:
             print(f"❌ Error initializing agent: {e}")
@@ -70,17 +85,30 @@ class BasicAgent:
             raise e
 
     def __call__(self, question: str) -> str:
+        """Run a chat completion on the HF model."""
         try:
             print(f"\n➡️ Agent received question: {question[:80]}")
-            agent_answer = self.agent.run(question)
-            print(f"✅ Agent returning answer: {agent_answer}")
-            return agent_answer
+
+            # Send the prompt to the model
+            response = self.client.chat.completions.create(
+                model=self.model_id,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": question}
+                ],
+            )
+
+            answer = response.choices[0].message["content"].strip()
+            print(f"✅ Agent returning answer: {answer}")
+            return answer
+
         except Exception as e:
             print(f"❌ Agent encountered an error: {e}")
             traceback.print_exc()
             return f"Error generating answer: {e}"
 
 
+
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,