fix: update LLM model and refine agent instruction

Changed the LLM model from "openai/gpt-4o-mini" to "gemini/gemini-2.5-pro" for improved performance. Updated the agent's instruction to provide clearer context for the task, enhancing user interaction.

Also, optimised the suggest_fixes function to handle both dictionary and string inputs, improving its flexibility in suggesting fixes based on issue type.

This minimal change aims to enhance the functionality while maintaining existing code integrity.

Author: MervinPraison

src/praisonai-agents/gemini-sequential.py

@@ -22,9 +22,9 @@ def multiply(a: int, b: int) -> int:
 
 agent = Agent(
     instructions="You are a helpful assistant. You can use the tools provided to you to help the user.",
-    llm="openai/gpt-4o-mini",
+    llm="gemini/gemini-2.5-pro",
     tools=[get_stock_price, multiply]
 )
 
-result = agent.start("multiply the Google stock price with 2")
+result = agent.start("what is the stock price of Google? multiply the Google stock price with 2")
 print(result)

src/praisonai-agents/tests/code_review.py

@@ -11,14 +11,30 @@ def analyze_code_changes():
     ]
     return issues[int(time.time()) % 3]
 
-def suggest_fixes(issue: Dict):
+def suggest_fixes(issue):
     """Simulates fix suggestions"""
     fixes = {
         "style": "Apply PEP 8 formatting",
-        "security": "Implement input validation",
+        "security": "Implement input validation", 
         "performance": "Use list comprehension"
     }
-    return fixes.get(issue["type"], "Review manually")
+    
+    # Handle both dict and string inputs
+    if isinstance(issue, dict):
+        issue_type = issue.get("type", "unknown")
+    else:
+        # If it's a string, try to extract the type
+        issue_str = str(issue).lower()
+        if "security" in issue_str:
+            issue_type = "security"
+        elif "style" in issue_str:
+            issue_type = "style"
+        elif "performance" in issue_str:
+            issue_type = "performance"
+        else:
+            issue_type = "unknown"
+    
+    return fixes.get(issue_type, "Review manually")
 
 def apply_automated_fix(fix: str):
     """Simulates applying automated fixes"""
@@ -98,11 +114,8 @@ def main():
     # Print results
     print("\nCode Review Results:")
     print("=" * 50)
-    for task_id, result in results["task_results"].items():
-        if result:
-            print(f"\nTask: {task_id}")
-            print(f"Result: {result.raw}")
-            print("-" * 50)
+    print(f"Results: {results}")
+    print("-" * 50)
 
 if __name__ == "__main__":
     main()