138 lines
4.7 KiB
Python
138 lines
4.7 KiB
Python
import base64
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import json
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import os
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import gspread
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import pandas as pd
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from dotenv import load_dotenv
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from oauth2client.service_account import ServiceAccountCredentials
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# Load environment variables from .env file
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load_dotenv()
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# Get the base64 string from the environment variable
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base64_creds = os.getenv("GDRIVE_BASE64")
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if base64_creds is None:
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raise ValueError("The GDRIVE_BASE64 environment variable is not set")
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# Decode the base64 string into bytes
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creds_bytes = base64.b64decode(base64_creds)
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# Convert the bytes into a string
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creds_string = creds_bytes.decode("utf-8")
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# Parse the string into a JSON object
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creds_info = json.loads(creds_string)
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# Define the base directory containing JSON files
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base_dir = "reports"
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# Create a list to store each row of data
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rows = []
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def process_test(
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test_name: str, test_info: dict, agent_name: str, common_data: dict
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) -> None:
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"""Recursive function to process test data."""
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parts = test_name.split("_", 1) # Split by underscore only once
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test_suite = parts[0] if len(parts) > 1 else None
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row = {
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"Agent": agent_name,
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"Command": common_data.get("command", ""),
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"Completion Time": common_data.get("completion_time", ""),
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"Benchmark Start Time": common_data.get("benchmark_start_time", ""),
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"Total Run Time": common_data.get("metrics", {}).get("run_time", ""),
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"Highest Difficulty": common_data.get("metrics", {}).get(
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"highest_difficulty", ""
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),
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"Workspace": common_data.get("config", {}).get("workspace", ""),
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"Test Name": test_name,
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"Data Path": test_info.get("data_path", ""),
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"Is Regression": test_info.get("is_regression", ""),
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"Difficulty": test_info.get("metrics", {}).get("difficulty", ""),
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"Success": test_info.get("metrics", {}).get("success", ""),
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"Success %": test_info.get("metrics", {}).get("success_%", ""),
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"Non mock success %": test_info.get("metrics", {}).get(
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"non_mock_success_%", ""
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),
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"Run Time": test_info.get("metrics", {}).get("run_time", ""),
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"Benchmark Git Commit Sha": common_data.get("benchmark_git_commit_sha", None),
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"Agent Git Commit Sha": common_data.get("agent_git_commit_sha", None),
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"Cost": test_info.get("metrics", {}).get("cost", ""),
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"Attempted": test_info.get("metrics", {}).get("attempted", ""),
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"Test Suite": test_suite,
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}
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rows.append(row)
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# Check for nested tests and process them if present
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nested_tests = test_info.get("tests")
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if nested_tests:
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for nested_test_name, nested_test_info in nested_tests.items():
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process_test(nested_test_name, nested_test_info, agent_name, common_data)
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# Usage:
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# Loop over each directory in the base directory
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for agent_dir in os.listdir(base_dir):
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agent_dir_path = os.path.join(base_dir, agent_dir)
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# Ensure the agent_dir_path is a directory
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if os.path.isdir(agent_dir_path):
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# Loop over each sub-directory in the agent directory (e.g., "folder49_07-28-03-53")
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for report_folder in os.listdir(agent_dir_path):
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report_folder_path = os.path.join(agent_dir_path, report_folder)
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# Ensure the report_folder_path is a directory
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if os.path.isdir(report_folder_path):
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# Check for a file named "report.json" in the sub-directory
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report_path = os.path.join(report_folder_path, "report.json")
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if os.path.exists(report_path):
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# Load the JSON data from the file
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with open(report_path, "r") as f:
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data = json.load(f)
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# Loop through each test
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for test_name, test_info in data["tests"].items():
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process_test(test_name, test_info, agent_dir, data)
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# Convert the list of rows into a DataFrame
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df = pd.DataFrame(rows)
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# Define the scope
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scope = [
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"https://spreadsheets.google.com/feeds",
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"https://www.googleapis.com/auth/drive",
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]
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# Add your service account credentials
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creds = ServiceAccountCredentials.from_json_keyfile_dict(creds_info, scope)
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# Authorize the clientsheet
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client = gspread.authorize(creds)
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# Get the instance of the Spreadsheet
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branch_name = os.getenv("GITHUB_REF_NAME")
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sheet = client.open(f"benchmark-{branch_name}")
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# Get the first sheet of the Spreadsheet
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sheet_instance = sheet.get_worksheet(0)
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# Convert dataframe to list of lists for uploading to Google Sheets
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values = df.values.tolist()
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# Prepend the header to the values list
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values.insert(0, df.columns.tolist())
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# Clear the existing values in the worksheet
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sheet_instance.clear()
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# Update the worksheet with the new values
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sheet_instance.append_rows(values)
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