Update agent.py

agent.py

@@ -1,5 +1,5 @@
 import os
-from smolagents import CodeAgent, DuckDuckGoSearchTool, tool, ApiModel
+from smolagents import CodeAgent, DuckDuckGoSearchTool, tool
 
 @tool
 def calculate_math(expression: str) -> str:
@@ -17,20 +17,15 @@ def calculate_math(expression: str) -> str:
 
 class CourseEvaluationAgent:
     def __init__(self):
-        print("[Agent System] Booting Engine...")
+        print("[Agent System] Booting Engine using direct Hub pipeline...")
         
-        # FIX: We use ApiModel, but pass a custom 'custom_client' configuration dict.
-        # This gives ApiModel the concrete implementation details it needs so it 
-        # doesn't throw the "Subclasses must implement this method" error.
-        self.model = ApiModel(
-            model_id="meta-llama/Llama-3.3-70B-Instruct",
-            custom_client={"model_type": "hf_hub"}
-        )
-        
-        # Build the functional CodeAgent 
+        # FIX: We bypass all model objects/classes completely. 
+        # In this smolagents version, passing the model ID as a string parameter 
+        # to 'model_id=' tells the CodeAgent constructor to directly instantiate 
+        # its own internal client, safely avoiding the abstract ApiModel constraint.
         self.agent = CodeAgent(
             tools=[DuckDuckGoSearchTool(), calculate_math], 
-            model=self.model,
+            model_id="meta-llama/Llama-3.3-70B-Instruct",
             max_steps=5,      # Prevents long execution delays or infinite loops
             verbosity_level=1
         )
@@ -41,13 +36,12 @@ class CourseEvaluationAgent:
             "Do not include unnecessary conversational filler."
         )
         
-        # Modify structural templates cleanly without breaking expected input variables
         if "system_prompt" in self.agent.prompt_templates:
             self.agent.prompt_templates["system_prompt"] += extra_instructions
 
     def run_task(self, question: str) -> str:
         try:
-            # Let the agent think, leverage its tools, and construct the solution
+            # Run calculation directly
             output = self.agent.run(question)
             return str(output).strip()
         except Exception as e: