Update agent/rica_agent.py

agent/rica_agent.py

@@ -1,20 +1,27 @@
 """
 RICA Agent optimized for Hugging Face Spaces
+Fixed for latest smolagents API
 """
 
 import os
 from smolagents import CodeAgent
-from openai import OpenAI
+from smolagents.models import OpenAIServerModel
 from agent_tools.ml_tools import predict_customer_churn_hf, get_model_status
 
 def create_rica_agent_hf():
-    """Create RICA agent optimized for HF Spaces"""
+    """Create RICA agent using correct smolagents API"""
     
     # Check API key availability
     api_key = os.getenv("OPENAI_API_KEY")
     if not api_key:
         raise ValueError("OpenAI API key not configured")
     
+    # Initialize OpenAI model using smolagents model class
+    model = OpenAIServerModel(
+        model_id="gpt-3.5-turbo",
+        api_key=api_key
+    )
+    
     # HF Spaces optimized tools
     hf_tools = [
         predict_customer_churn_hf,
@@ -22,11 +29,11 @@ def create_rica_agent_hf():
     ]
     
     try:
+        # Create agent with correct parameters (no max_iterations)
         agent = CodeAgent(
             tools=hf_tools,
-            model=OpenAI(api_key=api_key),
-            add_base_tools=False,
-            max_iterations=3  # Reduced for HF Spaces performance
+            model=model,
+            add_base_tools=False
         )
         return agent
     except Exception as e:
@@ -56,7 +63,7 @@ def execute_rica_analysis_hf(analysis_type: str, parameters: dict = None):
             
             "churn_focus": f"""
             Focus on customer churn analysis:
-            1) Predict customer churn with predict_customer_churn_hf(risk_threshold={parameters.get('risk_threshold', 0.6)})
+            1) Predict customer churn with predict_customer_churn_hf(risk_threshold={parameters.get('risk_threshold', 0.6) if parameters else 0.6})
             2) Identify high-risk customers requiring immediate attention
             3) Provide specific intervention strategies