scripts/run_kaggle_eval.py

"""
Kaggle Local Evaluation Harness Simulation:
Simulates multi-turn tool calling and bug resolution loops for Gemma 4.
"""

import json
import sys
from pathlib import Path
from typing import Dict, Any, List

# Ensure parent path is in sys.path
sys.path.append(str(Path(__file__).resolve().parent.parent))

from src.agent.loop import GemmaDevAgent

class KaggleEvalHarness:
    def __init__(self, repo_root: str = "."):
        self.agent = GemmaDevAgent(repo_root)
        self.schemas_path = Path(repo_root) / "src" / "schemas" / "tool_schemas.json"

    def load_tool_schemas(self) -> List[Dict[str, Any]]:
        """Loads declarative JSON schemas for tool calling."""
        if self.schemas_path.exists():
            return json.loads(self.schemas_path.read_text(encoding="utf-8"))
        return []

    def simulate_task_run(self, task_description: str, max_steps: int = 5) -> Dict[str, Any]:
        """Simulates an evaluation loop handling tool execution steps."""
        print(f"\n[Kaggle Eval Harness] Starting Task: '{task_description}'")
        
        # Load system prompt & configurations
        schemas = self.load_tool_schemas()
        print(f"[Kaggle Eval Harness] Loaded {len(schemas)} tool schemas successfully.")

        messages = [
            {"role": "system", "content": "You are a software engineering agent fixing repository bugs."},
            {"role": "user", "content": task_description}
        ]

        # Simulated initial step: searching the symbol table for task context
        simulated_tool_call = {
            "name": "code_graph_search",
            "arguments": {"symbol_name": "FileOperations"}
        }

        print(f"[Kaggle Eval Harness] Step 1: Agent called '{simulated_tool_call['name']}'")
        messages = self.agent.process_step(messages, simulated_tool_call)

        # Print latest tool response summary
        latest_response = json.loads(messages[-1]["content"])
        print(f"[Kaggle Eval Harness] Step 1 Response Status: {latest_response.get('status')}")

        return {
            "task": task_description,
            "total_messages": len(messages),
            "final_status": "evaluated"
        }

if __name__ == "__main__":
    harness = KaggleEvalHarness()
    result = harness.simulate_task_run("Locate the FileOperations class and verify scope reading functionality.")
    print(f"\n[Kaggle Eval Harness] Run Complete: {result}")