Model/backend/postcode_splitter/main.py

import json
import pandas as pd
import requests
from uuid import UUID
from urllib.parse import unquote
from backend.address2UPRN.main import (
    resolve_uprns_for_postcode_group,
    get_epc_data_with_postcode,
)
from backend.app.db.functions.tasks.Tasks import SubTaskInterface
from utils.s3 import read_csv_from_s3 as read_csv_from_s3_dict
from tqdm import tqdm


def parse_s3_console_url(s3_uri: str) -> tuple[str, str]:
    """
    Parse AWS console S3 URL to extract bucket and key.

    Format: https://account-id-hash.region.console.aws.amazon.com/s3/object/bucket?region=...&prefix=path
    """
    if "console.aws.amazon.com" in s3_uri and "?prefix=" in s3_uri:
        base, query = s3_uri.split("?", 1)
        path_parts = base.split("/s3/object/")
        if len(path_parts) > 1:
            bucket = path_parts[1]
            params = dict(item.split("=") for item in query.split("&") if "=" in item)
            key = unquote(params.get("prefix", ""))
            return bucket, key
    raise ValueError(f"Could not parse S3 URI: {s3_uri}")


def sanitise_postcode(postcode: str) -> str | None:
    """
    Normalise postcode for grouping.

    - Uppercase
    - Remove all whitespace
    """
    if pd.isna(postcode):
        return None

    return postcode.upper().replace(" ", "")


def is_valid_postcode(postcode_clean: str) -> bool:
    """
    Validate postcode using postcodes.io.

    Expects a sanitised postcode (e.g. E84SQ).
    Returns True if valid, False otherwise.
    """
    POSTCODES_IO_VALIDATE_URL = "https://api.postcodes.io/postcodes/{postcode}/validate"
    if not postcode_clean:
        return False

    try:
        resp = requests.get(
            POSTCODES_IO_VALIDATE_URL.format(postcode=postcode_clean),
            timeout=5,
        )
        resp.raise_for_status()
        return resp.json().get("result", False)
    except requests.RequestException:
        # Network issues, rate limits, etc.
        return False


def main():
    df = pd.read_excel("hackney.xlsx", sheet_name="Sustainability")
    df = df.head(500)

    # Sanitise postcodes
    df["postcode_clean"] = df["Postcode"].apply(sanitise_postcode)

    # --- validate AFTER grouping (save API calls) ---

    # Get unique, non-null postcodes
    unique_postcodes = df["postcode_clean"].dropna().unique()

    # Validate each postcode once, TODOadd a progress bar
    postcode_validity = {
        pc: is_valid_postcode(pc)
        for pc in tqdm(unique_postcodes, total=len(unique_postcodes))
    }

    # Map validity back onto dataframe
    df["postcode_valid"] = df["postcode_clean"].map(postcode_validity)

    results = []

    for postcode, group_df in tqdm(
        df[df["postcode_valid"]].groupby("postcode_clean"),
        desc="Resolving UPRNs by postcode",
    ):
        try:
            epc_df = get_epc_data_with_postcode(postcode)

            if epc_df.empty:
                tmp = group_df.copy()
                tmp["found_uprn"] = None
                tmp["status"] = "no_epc_results"
                results.append(tmp)
                continue

            resolved = resolve_uprns_for_postcode_group(
                group_df=group_df,
                epc_df=epc_df,
            )

            results.append(resolved)

        except Exception as e:
            tmp = group_df.copy()
            tmp["found_uprn"] = None
            tmp["status"] = "exception"
            tmp["error"] = str(e)
            results.append(tmp)

    final_df = pd.concat(results, ignore_index=True)
    a = final_df[
        [
            "best_match_lexiscore",
            "Address 1",
            "best_match_address",
            "Postcode",
            "UPRN",
            "best_match_uprn",
        ]
    ]  # add levi score to viewing
    b = final_df[final_df["best_match_lexiscore"] > 0]  # add levi score to viewing
    b = b[
        [
            "best_match_lexiscore",
            "Address 1",
            "best_match_address",
            "Postcode",
            "UPRN",
            "best_match_uprn",
        ]
    ]


def handler(event, context):
    print(f"Function: {context.function_name}")
    print(f"Request ID: {context.aws_request_id}")

    # Example SQS message for testing (copy and paste into SQS):
    # {
    #     "task_id": "e31f2f21-175b-4a91-a3ec-a6baa325e917",
    #     "s3_uri": "https://337213553626-7ovirzjr.eu-west-2.console.aws.amazon.com/s3/object/retrofit-data-dev?region=eu-west-2&prefix=ara_raw_inputs/peabody/2025_11_11+-+Peabody+-+Data+Extracts+for+Domna_transformed.csv",
    # }

    # Handle both single event and batch events (SQS, etc.)
    records = event.get("Records", [event])
    results = []
    errors = []
    subtask_interface = SubTaskInterface()

    for record in records:
        task_id = None
        subtask_id = None
        try:
            # Parse body
            if isinstance(record.get("body"), str):
                body = json.loads(record["body"])
            else:
                body = record.get("body", {})

            # Validate required fields
            task_id = body.get("task_id")
            s3_uri = body.get("s3_uri")

            if not task_id:
                errors.append({"error": "Missing required field: task_id"})
                continue

            if not s3_uri:
                errors.append({"error": "Missing required field: s3_uri"})
                continue

            # Convert task_id to UUID
            try:
                task_id = UUID(task_id) if isinstance(task_id, str) else task_id
            except ValueError as e:
                errors.append({"error": f"Invalid UUID format for task_id: {str(e)}"})
                continue

            # Create a new subtask for this postcode splitter invocation
            subtask_id = subtask_interface.create_subtask(
                task_id=task_id, inputs={"s3_uri": s3_uri}
            )
            print(f"Created subtask {subtask_id} for task {task_id}")

            # Process normal flow
            print(f"Processing task_id: {task_id}")
            print(f"Processing s3_uri: {s3_uri}")

            # Read CSV from S3
            print("Reading CSV from S3...")
            bucket, key = parse_s3_console_url(s3_uri)
            print(f"Parsed S3 - Bucket: {bucket}, Key: {key}")
            csv_data = read_csv_from_s3_dict(bucket, key)
            df = pd.DataFrame(csv_data)
            print(f"CSV loaded: {len(df)} rows, {len(df.columns)} columns")

            # Get head for demo
            df_head = df.head()
            print("DataFrame head:")
            print(df_head)
            df_head_dict = df_head.to_dict("records")

            results.append(
                {
                    "message": "Postcode splitter processing started",
                    "task_id": str(task_id),
                    "s3_uri": s3_uri,
                    "subtask_id": str(subtask_id),
                }
            )

            # Mark subtask as complete after successful processing
            subtask_interface.update_subtask_status(
                subtask_id,
                "complete",
                outputs={
                    "status": "processing_complete",
                    "s3_uri": s3_uri,
                    "rows_processed": len(df),
                },
            )
            print(f"Subtask {subtask_id} marked as complete")

        except json.JSONDecodeError as e:
            errors.append({"error": "Invalid JSON in request body", "details": str(e)})
            # Mark subtask as failed if we have one
            if subtask_id:
                try:
                    subtask_interface.update_subtask_status(
                        subtask_id, "failed", outputs={"error": str(e)}
                    )
                except Exception as db_error:
                    print(f"Failed to update subtask status: {db_error}")
        except Exception as e:
            print(f"Unexpected error processing record: {e}")
            errors.append({"error": "Unexpected error", "details": str(e)})
            # Mark subtask as failed if we have one
            if subtask_id:
                try:
                    subtask_interface.update_subtask_status(
                        subtask_id, "failed", outputs={"error": str(e)}
                    )
                except Exception as db_error:
                    print(f"Failed to update subtask status: {db_error}")

    # Return error if all records failed
    if errors and not results:
        return {"statusCode": 500, "body": json.dumps({"errors": errors})}

    return {
        "statusCode": 200,
        "body": json.dumps(
            {"processed": results, "errors": errors if errors else None}
        ),
    }


if __name__ == "__main__":
    main()