scripts/utils.py

import json
import os
import time
from typing import List, Any, Optional, Union, Tuple, Dict
import numpy as np
import pandas as pd
import gc
import re
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from enum import Enum
from string import Template
from json.decoder import JSONDecodeError

DEFAULT_MECH_FEE = 0.01
REDUCE_FACTOR = 0.25
SLEEP = 0.5
REQUEST_ID_FIELD = "request_id"
SCRIPTS_DIR = Path(__file__).parent
ROOT_DIR = SCRIPTS_DIR.parent
JSON_DATA_DIR = ROOT_DIR / "json_data"
HIST_DIR = ROOT_DIR / "historical_data"
TMP_DIR = ROOT_DIR / "tmp"
BLOCK_FIELD = "block"
CID_PREFIX = "f01701220"
REQUEST_ID = "requestId"
REQUEST_SENDER = "sender"
PROMPT_FIELD = "prompt"
HTTP = "http://"
HTTPS = HTTP[:4] + "s" + HTTP[4:]
FORMAT_UPDATE_BLOCK_NUMBER = 30411638
INVALID_ANSWER_HEX = (
    "0xffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff"
)
IPFS_ADDRESS = "https://gateway.autonolas.tech/ipfs/"
GCP_IPFS_ADDRESS = "https://gateway.gcp.autonolas.tech/ipfs/"

INC_TOOLS = [
    "prediction-online",
    "prediction-offline",
    "claude-prediction-online",
    "claude-prediction-offline",
    # "prediction-offline-sme",
    "prediction-online-sme",
    "prediction-request-rag",
    "prediction-request-reasoning",
    # "prediction-url-cot-claude",
    "prediction-request-rag-claude",
    "prediction-request-reasoning-claude",
    "superforcaster",
]
SUBGRAPH_URL = Template(
    """https://gateway-arbitrum.network.thegraph.com/api/${subgraph_api_key}/subgraphs/id/7s9rGBffUTL8kDZuxvvpuc46v44iuDarbrADBFw5uVp2"""
)
OMEN_SUBGRAPH_URL = Template(
    """https://gateway-arbitrum.network.thegraph.com/api/${subgraph_api_key}/subgraphs/id/9fUVQpFwzpdWS9bq5WkAnmKbNNcoBwatMR4yZq81pbbz"""
)
NETWORK_SUBGRAPH_URL = Template(
    """https://gateway-arbitrum.network.thegraph.com/api/${subgraph_api_key}/subgraphs/id/FxV6YUix58SpYmLBwc9gEHkwjfkqwe1X5FJQjn8nKPyA"""
)
# THEGRAPH_ENDPOINT = (
#     "https://api.studio.thegraph.com/query/78829/mech-predict/version/latest"
# )
MECH_SUBGRAPH_URL = Template(
    """https://gateway.thegraph.com/api/${subgraph_api_key}/subgraphs/id/4YGoX3iXUni1NBhWJS5xyKcntrAzssfytJK7PQxxQk5g"""
)

SUBGRAPH_API_KEY = os.environ.get("SUBGRAPH_API_KEY", None)
RPC = os.environ.get("RPC", None)


class MechEventName(Enum):
    """The mech's event names."""

    REQUEST = "Request"
    DELIVER = "Deliver"


@dataclass
class MechEvent:
    """A mech's on-chain event representation."""

    for_block: int
    requestId: int
    data: bytes
    sender: str

    def _ipfs_link(self) -> Optional[str]:
        """Get the ipfs link for the data."""
        return f"{IPFS_ADDRESS}{CID_PREFIX}{self.data.hex()}"

    @property
    def ipfs_request_link(self) -> Optional[str]:
        """Get the IPFS link for the request."""
        return f"{self._ipfs_link()}/metadata.json"

    @property
    def ipfs_deliver_link(self) -> Optional[str]:
        """Get the IPFS link for the deliver."""
        if self.requestId is None:
            return None
        return f"{self._ipfs_link()}/{self.requestId}"

    def ipfs_link(self, event_name: MechEventName) -> Optional[str]:
        """Get the ipfs link based on the event."""
        if event_name == MechEventName.REQUEST:
            if self.for_block < FORMAT_UPDATE_BLOCK_NUMBER:
                return self._ipfs_link()
            return self.ipfs_request_link
        if event_name == MechEventName.DELIVER:
            return self.ipfs_deliver_link
        return None


@dataclass(init=False)
class MechRequest:
    """A structure for a request to a mech."""

    request_id: Optional[int]
    request_block: Optional[int]
    prompt_request: Optional[str]
    tool: Optional[str]
    nonce: Optional[str]
    trader_address: Optional[str]

    def __init__(self, **kwargs: Any) -> None:
        """Initialize the request ignoring extra keys."""
        self.request_id = int(kwargs.pop(REQUEST_ID, 0))
        self.request_block = int(kwargs.pop(BLOCK_FIELD, 0))
        self.prompt_request = kwargs.pop(PROMPT_FIELD, None)
        self.tool = kwargs.pop("tool", None)
        self.nonce = kwargs.pop("nonce", None)
        self.trader_address = kwargs.pop("sender", None)


@dataclass(init=False)
class PredictionResponse:
    """A response of a prediction."""

    p_yes: float
    p_no: float
    confidence: float
    info_utility: float
    vote: Optional[str]
    win_probability: Optional[float]

    def __init__(self, **kwargs: Any) -> None:
        """Initialize the mech's prediction ignoring extra keys."""
        try:
            self.p_yes = float(kwargs.pop("p_yes"))
            self.p_no = float(kwargs.pop("p_no"))
            self.confidence = float(kwargs.pop("confidence"))
            self.info_utility = float(kwargs.pop("info_utility"))
            self.win_probability = 0

            # Validate probabilities
            probabilities = {
                "p_yes": self.p_yes,
                "p_no": self.p_no,
                "confidence": self.confidence,
                "info_utility": self.info_utility,
            }

            for name, prob in probabilities.items():
                if not 0 <= prob <= 1:
                    raise ValueError(f"{name} probability is out of bounds: {prob}")

            if self.p_yes + self.p_no != 1:
                raise ValueError(
                    f"Sum of p_yes and p_no is not 1: {self.p_yes} + {self.p_no}"
                )

            self.vote = self.get_vote()
            self.win_probability = self.get_win_probability()

        except KeyError as e:
            raise KeyError(f"Missing key in PredictionResponse: {e}")
        except ValueError as e:
            raise ValueError(f"Invalid value in PredictionResponse: {e}")

    def get_vote(self) -> Optional[str]:
        """Return the vote."""
        if self.p_no == self.p_yes:
            return None
        if self.p_no > self.p_yes:
            return "No"
        return "Yes"

    def get_win_probability(self) -> Optional[float]:
        """Return the probability estimation for winning with vote."""
        return max(self.p_no, self.p_yes)


@dataclass(init=False)
class MechResponse:
    """A structure for the response of a mech."""

    request_id: int
    deliver_block: Optional[int]
    result: Optional[PredictionResponse]
    error: Optional[str]
    error_message: Optional[str]
    prompt_response: Optional[str]
    mech_address: Optional[str]

    def __init__(self, **kwargs: Any) -> None:
        """Initialize the mech's response ignoring extra keys."""
        self.error = kwargs.get("error", None)
        self.request_id = int(kwargs.get(REQUEST_ID, 0))
        self.deliver_block = int(kwargs.get(BLOCK_FIELD, 0))
        self.result = kwargs.get("result", None)
        self.prompt_response = kwargs.get(PROMPT_FIELD, None)
        self.mech_address = kwargs.get("sender", None)

        if self.result != "Invalid response":
            self.error_message = kwargs.get("error_message", None)

            try:
                if isinstance(self.result, str):
                    kwargs = json.loads(self.result)
                    self.result = PredictionResponse(**kwargs)
                    self.error = 0

            except JSONDecodeError:
                self.error_message = "Response parsing error"
                self.error = 1

            except Exception as e:
                self.error_message = str(e)
                self.error = 1

        else:
            self.error_message = "Invalid response from tool"
            self.error = 1
            self.result = None


EVENT_TO_MECH_STRUCT = {
    MechEventName.REQUEST: MechRequest,
    MechEventName.DELIVER: MechResponse,
}


def transform_to_datetime(x):
    return datetime.fromtimestamp(int(x), tz=timezone.utc)


def measure_execution_time(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        execution_time = end_time - start_time
        print(f"Execution time: {execution_time:.6f} seconds")
        return result

    return wrapper


def limit_text(text: str, limit: int = 200) -> str:
    """Limit the given text"""
    if len(text) > limit:
        return f"{text[:limit]}..."
    return text


def check_for_dicts(df: pd.DataFrame) -> List[str]:
    """Check for columns that contain dictionaries."""
    dict_columns = []
    for column in df.columns:
        if df[column].apply(lambda x: isinstance(x, dict)).any():
            dict_columns.append(column)
    return dict_columns


def drop_dict_rows(df: pd.DataFrame, dict_columns: List[str]) -> pd.DataFrame:
    """Drop rows that contain dictionaries."""
    for column in dict_columns:
        df = df[~df[column].apply(lambda x: isinstance(x, dict))]
    return df


def clean(df: pd.DataFrame) -> pd.DataFrame:
    """Clean the dataframe."""
    dict_columns = check_for_dicts(df)
    df = drop_dict_rows(df, dict_columns)
    cleaned = df.drop_duplicates()
    cleaned[REQUEST_ID_FIELD] = cleaned[REQUEST_ID_FIELD].astype("str")
    return cleaned


def save_json_file(data: Dict[str, Any], filename: str):
    """Function to save the content into a json file"""
    filename_path = JSON_DATA_DIR / filename
    with open(filename_path, "w", encoding="utf-8") as file:
        json.dump(data, file, indent=2)


def gen_event_filename(event_name: MechEventName) -> str:
    """Generate the filename of an event."""
    return f"{event_name.value.lower()}s.parquet"


def read_n_last_lines(filename: str, n: int = 1) -> str:
    """Return the `n` last lines' content of a file."""
    num_newlines = 0
    with open(filename, "rb") as f:
        try:
            f.seek(-2, os.SEEK_END)
            while num_newlines < n:
                f.seek(-2, os.SEEK_CUR)
                if f.read(1) == b"\n":
                    num_newlines += 1
        except OSError:
            f.seek(0)
        last_line = f.readline().decode()
    return last_line


def get_question(text: str) -> str:
    """Get the question from a text."""
    # Find text between the first and last quote
    start = text.find('"')
    end = text.rfind('"')

    if start != -1 and end != -1 and start != end:
        question = text[start + 1 : end]
        return question

    return None


def current_answer(text: str, fpmms: pd.DataFrame) -> Optional[str]:
    """Get the current answer for a question."""
    row = fpmms[fpmms["title"] == text]
    if row.shape[0] == 0:
        return None
    return row["currentAnswer"].values[0]


def convert_hex_to_int(x: Union[str, float]) -> Union[int, float]:
    """Convert hex to int"""
    if isinstance(x, float):
        return np.nan
    if isinstance(x, str):
        if x == INVALID_ANSWER_HEX:
            return -1
        return int(x, 16)


def wei_to_unit(wei: int) -> float:
    """Converts wei to currency unit."""
    return wei / 10**18


def get_vote(p_yes, p_no) -> Optional[str]:
    """Return the vote."""
    if p_no == p_yes:
        return None
    if p_no > p_yes:
        return "No"
    return "Yes"


def get_win_probability(p_yes, p_no) -> Optional[float]:
    """Return the probability estimation for winning with vote."""
    return max(p_no, p_yes)


def get_result_values(result: str) -> Tuple:
    """Function to decide if the result is valid or not"""
    if isinstance(result, str) and result == "Invalid response":
        print("Invalid response found")
        return 1, "Invalid response from tool", None

    try:
        if isinstance(result, str):
            params = json.loads(result)
            return 0, None, params

    except Exception as e:
        print("Non valid json format")

    return 1, str(result), None


def get_prediction_values(params: dict) -> Tuple:
    p_yes = float(params.pop("p_yes"))
    p_no = float(params.pop("p_no"))
    confidence = float(params.pop("confidence"))
    info_utility = float(params.pop("info_utility"))
    return p_yes, p_no, confidence, info_utility


def to_content(q: str) -> dict[str, Any]:
    """Convert the given query string to payload content, i.e., add it under a `queries` key and convert it to bytes."""
    finalized_query = {
        "query": q,
        "variables": None,
        "extensions": {"headers": None},
    }
    return finalized_query


def read_parquet_files(tools_filename: str, trades_filename: str):
    # Check if tools.parquet is in the same directory
    try:
        tools = pd.read_parquet(ROOT_DIR / tools_filename)

        # make sure creator_address is in the columns
        assert "trader_address" in tools.columns, "trader_address column not found"

        # lowercase and strip creator_address
        tools["trader_address"] = tools["trader_address"].str.lower().str.strip()

        # drop duplicates
        tools.drop_duplicates(inplace=True)

        print(f"{tools_filename} loaded")
    except FileNotFoundError:
        print("tools.parquet not found. Please run tools.py first.")
        return
    try:
        fpmmTrades = pd.read_parquet(ROOT_DIR / trades_filename)
        fpmmTrades["trader_address"] = (
            fpmmTrades["trader_address"].str.lower().str.strip()
        )
    except FileNotFoundError:
        print("fpmmsTrades.parquet not found.")
        return

    return tools, fpmmTrades