mirror of
https://github.com/status-im/status-python-sdk.git
synced 2026-08-31 14:11:07 +00:00
224 lines
7.8 KiB
Python
224 lines
7.8 KiB
Python
import datetime, os, pickle, yaml, time
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import pandas as pd
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from pathlib import Path
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from dotenv import load_dotenv
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# Manual file imports
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from bot import Account, Logger
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from postgres import Postgres
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def load_config(file_path: str) -> dict:
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"""
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Load the config file and the `.env` variables
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Parameter:
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- `file_path` - the file path of the config yaml file. The `.env` variable must be in the same folder
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Output:
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- The config variables and secret from `.env`
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"""
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with open(file_path, "r") as f:
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config: dict = yaml.safe_load(f)
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env_file_path = os.path.join(os.path.dirname(file_path), ".env")
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load_dotenv(env_file_path)
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config["env_vars"] = {
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key: value
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for key, value in os.environ.items()
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if key.startswith(("POSTGRES_", "STATUS_"))
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}
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return config
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def extract_community_members(account: Account, community_id: str) -> pd.DataFrame:
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"""
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Extract the community members. Uses manual RPC call function.
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Parameters:
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- `account` - logged in Status Bot account
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- `community_id` - community ID as it is in `account.communities`
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Output:
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- DataFrame with all of the members
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"""
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data = account.call_rpc("messaging", "communities")
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for result in data["result"]:
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if result["id"] != community_id:
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continue
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extract_timestamp = datetime.datetime.now()
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members = pd.DataFrame([
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{
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"community_id": community_id,
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"member_id": member_id,
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"last_checked": datetime.datetime.fromtimestamp(info["last_update_clock"]) if "last_update_clock" in info else None,
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"extract_timestamp": extract_timestamp
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}
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for member_id, info in result["members"].items()
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])
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return members
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return pd.DataFrame()
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def extract_community_channels(account: Account, community: dict, start_timestamp: datetime.datetime, end_timestamp: datetime.datetime) -> pd.DataFrame:
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"""
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Extract the community channel messages.
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Parameters:
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- `account` - logged in Status Bot account
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- `community` - the current community from `account`
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- `start_timestamp` - start timestamp for message fetching
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- `end_timestamp` - end timestamp for message fetching
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Output:
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- DataFrame with all of the community messages for the given start and end timestamps
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"""
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final = []
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for channel in community["channels"]:
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messages = account.get_messages(channel["chat_id"], start_timestamp, end_timestamp)
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messages = pd.DataFrame(messages)
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if len(messages) == 0:
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account.logger.info(f"No messages found for # {channel['name']}")
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continue
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account.logger.info(f"Extracted {len(messages)} message(s) from # {channel['name']}")
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messages = messages.assign(
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community_id = community["id"],
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extracted_timestamp = datetime.datetime.now()
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)
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final.append(messages)
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return pd.concat(final, ignore_index=True) if final else pd.DataFrame()
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def download(folder: str, config: dict):
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"""
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Download Status App messages / info from communities and store them in pickle files.
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Parameters:
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- `folder` - the folder where the files will be created. Sub folders are automatically created
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- `config` - the `load_config` configuration
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"""
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account = Account(**config.get("bot_params", {}))
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available_accounts = [acc["display_name"] for acc in account.available_accounts]
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messages_folder = os.path.join(folder, "messages")
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os.makedirs(messages_folder, exist_ok=True)
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community_info_folder = os.path.join(folder, "community")
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os.makedirs(community_info_folder, exist_ok=True)
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members_info_folder = os.path.join(folder, "members")
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os.makedirs(members_info_folder, exist_ok=True)
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prefix = "STATUS_"
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params = {
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key.replace(prefix, "").lower(): value
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for key, value in os.environ.items()
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if key.startswith(prefix)
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}
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if params["display_name"] in available_accounts:
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params.pop("mnemonic")
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account.login(**params)
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now = datetime.datetime.now()
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# Node will only return known / fetched messages for this channel.
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# Without enabling community archives feature the node can only fetch last 30 days (from store nodes).
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to_midnight = lambda date: date.replace(minute=0, second=0, hour=0, microsecond=0)
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start_timestamp: datetime.datetime = to_midnight(now - datetime.timedelta(days=30))
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end_timestamp: datetime.datetime = to_midnight(now - datetime.timedelta(days=1))
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get_file_name = lambda: str(to_midnight(datetime.datetime.now()).timestamp()).replace(".", "") + ".pkl"
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for community in account.communities:
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account.logger.info(f"Extracting data for {community['name']} from {start_timestamp} to {end_timestamp}")
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community["extracted_timestamp"] = datetime.datetime.now()
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file_path = os.path.join(community_info_folder, get_file_name())
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if not os.path.exists(file_path):
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with open(file_path, "wb") as f:
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pickle.dump(community, f)
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account.logger.info(f"Created {file_path}")
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file_path = os.path.join(members_info_folder, get_file_name())
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if not os.path.exists(file_path):
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members = extract_community_members(account, community["id"])
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if len(members) > 0:
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members.to_pickle(file_path)
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account.logger.info(f"Created {file_path}")
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file_path = os.path.join(messages_folder, get_file_name())
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if not os.path.exists(file_path):
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messages = extract_community_channels(account, community, start_timestamp, end_timestamp)
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if len(messages) > 0:
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messages.to_pickle(file_path)
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account.logger.info(f"Created {file_path}")
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def store(folder: str, config: dict):
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"""
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Upload Status App `download` file to Postgres.
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NOTE: The Postgres schema must already exist
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Parameters:
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- `folder` - the folder where the files will be created. Sub folders are automatically created
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- `config` - the `load_config` configuration
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"""
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path = Path(folder)
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table_name_mapping: dict[str, str] = config["postgres"]["tables"]
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table_schema = config["postgres"]["schema"]
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upload: dict[str, list] = {}
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completed = []
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for file_path in path.rglob("*.pkl"):
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table_name = table_name_mapping.get(file_path.parent.name)
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if not table_name:
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continue
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data = pd.read_pickle(file_path)
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if isinstance(data, dict):
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data = pd.DataFrame([data])
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if table_name not in upload:
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upload[table_name] = []
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upload[table_name].append(data)
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completed.append(str(file_path))
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prefix = "POSTGRES_"
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params = {
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key.replace(prefix, "").lower(): value
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for key, value in config["env_vars"].items()
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if key.startswith(prefix)
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}
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connector = Postgres(**params)
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for table_name, data in upload.items():
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if len(data) == 0:
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continue
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df = pd.concat(data, ignore_index=True)
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json_columns = [
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column
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for column in df.columns
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if isinstance(df[column].dropna().reset_index(drop=True).iloc[0], (dict, list))
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]
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connector.insert(df, table_name, table_schema, json_columns)
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for file_path in completed:
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os.remove(file_path)
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if __name__ == "__main__":
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folder = os.path.dirname(__file__)
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config = load_config(os.path.join(folder, "config.yaml"))
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upload_folder = os.path.join(os.path.dirname(__file__), "uploads")
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logger = Logger()
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while True:
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download(upload_folder, config)
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store(upload_folder, config)
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logger.info(f"Sleeping for {config['sleep']} minute(s)")
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time.sleep(config["sleep"])
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