import pandas as pd
import logging
import json
import jmespath
import regex as re
from pre_processing import pre_processing
from processing import processing
from post_processing import post_processing

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
)
logger = logging.getLogger(__name__)


def extract_value(blob, expression):
    try:
        return jmespath.search(expression, blob)
    except Exception:
        return None

# Coalesce function to return the first non-None value
def coalesce(*args):
    for value in args:
        if value is not None:
            return value
    return None

# New sanitize blob function
def sanitize_blob(blob):
    try:
        blob = re.sub(r'"(\w+)":"(\{[^}]+\})"', r'"\1":\2', blob)
        blob = re.sub(r'"tps_vendor_raw_response"\s*:\s*"\?\{', '"tps_vendor_raw_response":{', blob)
        blob = blob.replace('\\"', '"')
        blob = blob.replace('\\n', '')
        blob = blob.replace('\\t', '')
        blob = blob.replace('\\\\', '')
        blob = re.sub(r'(\}\})"', r'\1', blob)
        blob = re.sub(r',\s*([\}\]])', r'\1', blob)
        return json.loads(blob)
    except json.JSONDecodeError as e:
        logger.error(f"JSON Decode Error: {e}")
        error_pos = e.pos
        snippet = blob[max(0, error_pos - 50): error_pos + 50]
        logger.error(f"Error near:\n{snippet}")
        return None
#---------------- Sanitise ends here 

# Function to extract a value using JMESPath
# Expressions to extract values
expressions = {
    "first_seen_days": [
        "tps_vendor_raw_response.query.results[0].first_seen_days",
        "emailage.emailriskscore.first_seen_days"
    ],
    "ea_score": [
        "tps_vendor_raw_response.query.results[0].EAScore",
        "emailage.emailriskscore.eascore"
    ],
    "email_creation_days": [
        "tps_vendor_raw_response.query.results[0].email_creation_days"
    ],
    "summary_risk_score": ["summary_risk_score"],
    "digital_id_trust_score_rating": ["digital_id_trust_score_rating"],
    "os_version": ["os_version"],
    "account_email_worst_score": ["account_email_worst_score"],
    "true_ip_score": ["true_ip_score"],
    "ip_net_speed_cell": [
        "tps_vendor_raw_response.query.results[0].ip_netSpeedCell",
        # "true_ip_connection_type"
    ],
    "account_email_score": ["account_email_score"],
    "true_ip_worst_score": ["true_ip_worst_score"],
    "proxy_ip_worst_score": ["proxy_ip_worst_score"],
    "proxy_ip_score": ["proxy_ip_score"],
    "fuzzy_device_score": ["fuzzy_device_score"],
    "ip_region_confidence": ["tps_vendor_raw_response.query.results[0].ip_regionconf"],
    "true_ip_state_confidence": ["true_ip_state_confidence"],
    "fuzzy_device_worst_score": ["fuzzy_device_worst_score"],
    "digital_id_confidence_rating": ["digital_id_confidence_rating"]
}


def __main__(
    #Application->
    application_key: str,
    application_timestamp: str,
    application_ssn : str,
    application_email_address: str,
    application_bank_account_number: str,
    application_is_rejected: str,
    application_date_of_birth: str,
    #uprovaloanapplication->
    educationlevel:str,
    employmentstatus: str,
    lengthatbank: str,
    lengthatjob: str,
    ownhome: str,
    payfrequency: str,
    monthsatresidence: str,
    state: str,
    zip: str,
    #thxresponse->
    EventType: str,
    DigitalIdConfidence: str,
    RiskRating: str,
    TmxSummaryReasonCode: str,
    TrueIpGeo: str,
    Blob:str,
    DeviceId:str,
    FuzzyDeviceId: str
    ) -> dict:

    # Convert input parameters into a flat dictionary
    data = {
        "application_key" : application_key,
        "application_timestamp" : application_timestamp,
        "application_ssn " : application_ssn ,
        "application_email_address" : application_email_address,
        "application_bank_account_number" : application_bank_account_number,
        "application_is_rejected" : application_is_rejected,
        "application_date_of_birth" : application_date_of_birth,
        "educationlevel" : educationlevel,
        "employmentstatus" : employmentstatus,
        "lengthatbank" : lengthatbank,
        "lengthatjob" : lengthatjob,
        "ownhome" : ownhome,
        "payfrequency" : payfrequency,
        "monthsatresidence" : monthsatresidence,
        "state" : state,
        "zip" : zip,
        "EventType" : EventType,
        "DigitalIdConfidence" : DigitalIdConfidence,
        "RiskRating" : RiskRating,
        "TmxSummaryReasonCode" : TmxSummaryReasonCode,
        "TrueIpGeo" : TrueIpGeo,
        "Blob" : Blob,
        "DeviceId" : DeviceId,
        "FuzzyDeviceId" : FuzzyDeviceId
    }

    # Convert dictionary to a single-row DataFrame
    combined_df = pd.DataFrame([data])
    combined_df.columns = combined_df.columns.str.lower()
    combined_df["application_email_address"] = combined_df["application_email_address"].str.lower()
    if Blob:
        combined_df["blob"] = combined_df["blob"].apply(sanitize_blob)

        # Step 2: Extract values using the expressions dictionary
        for column, expressions_list in expressions.items():
            combined_df[column] = combined_df["blob"].apply(lambda x: coalesce(*[extract_value(x, expr) for expr in expressions_list]))

        logger.info("pre_flowx data")
        logger.info(combined_df.iloc[0].drop('blob').to_dict())
    else:
        for column, expressions_list in expressions.items():
            combined_df[column] = None
        logger.info("pre_flowx data")
        logger.info(combined_df.iloc[0].to_dict())
    pre_processed_data = pre_processing(combined_df)
    # logger.info(f"pre_processed_data: {pre_processed_data}")
    logger.info("pre_processed data")
    logger.info(pre_processed_data.iloc[0].to_dict())
    df = processing(pre_processed_data)
    logger.info("procesed_data")
    logger.info(df.iloc[0].to_dict())
    df["application_timestamp"] = df["application_timestamp"].astype(str)
    # logger.info("prediction: %.8f", float(df['prediction'].iloc[0]))
    result = post_processing(df)
    logger.info("post_processed_data")
    logger.info(result)
    # State Check
    state_value = combined_df["state"].iloc[0]
    zip_value = combined_df["zip"].iloc[0]
    if (pd.notnull(state_value) and state_value == "ZZ") or (pd.notnull(zip_value) and zip_value == "86445"):
        result["hd_score_m1"] = 1250
        logger.info("post_processed_data after state check")
        logger.info(result) 

    return result

# testing :
# __main__