Delete utils.py

utils.py

@@ -1,104 +0,0 @@
-# -*- coding: utf-8 -*-
-# Funciones auxiliares para limpieza, conversión y scraping
-
-import re
-import numpy as np
-import pandas as pd
-import requests
-from bs4 import BeautifulSoup
-from rapidfuzz import process
-from config import SCRAPERAPI_KEY, YAHOO_FINANCE_SEARCH_URL
-
-def limpiar_texto(texto):
-    """Elimina caracteres especiales y espacios extra."""
-    texto_limpio_parcial = re.sub(r'[^a-zA-Z0-9.,\\s]', '', texto)
-    texto_final = texto_limpio_parcial.strip()
-    return texto_final
-
-def expand_money(val):
-    """Convierte strings de dinero (ej. '$1.2B') a valores flotantes."""
-    if pd.isna(val):
-        return np.nan
-    val = str(val).replace('$', '').replace(',', '').upper()
-    try:
-        if 'B' in val:
-            return float(val.replace('B', '')) * 1_000_000_000
-        elif 'M' in val:
-            return float(val.replace('M', '')) * 1_000_000
-        elif 'K' in val:
-            return float(val.replace('K', '')) * 1_000
-        else:
-            return float(val)
-    except:
-        return np.nan
-
-def extract_file_id(url):
-    """Extrae el ID del archivo de una URL de Google Drive."""
-    match = re.search(r'/d/([a-zA-Z0-9_-]+)', url)
-    if match:
-        return match.group(1)
-    return None
-
-def descargar_tabla_web(url, indice_tabla=0):
-    """Descarga una tabla de una URL usando ScraperAPI para evitar bloqueos."""
-    api_url = f"http://api.scraperapi.com?api_key={SCRAPERAPI_KEY}&url={url}"
-    try:
-        response = requests.get(api_url)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.text, 'html.parser')
-        tables = soup.find_all('table')
-        if len(tables) > indice_tabla:
-            table = tables[indice_tabla]
-        else:
-            print(f"No se encontró la tabla con índice {indice_tabla} en la página: {url}.")
-            return None
-        headers = [th.text.strip() for th in table.find_all('th')]
-        data = []
-        for row in table.find_all('tr'):
-            cols = row.find_all('td')
-            cols = [ele.text.strip() for ele in cols]
-            if cols:
-                data.append(cols)
-        if headers and data and len(headers) == len(data[0]):
-            df = pd.DataFrame(data, columns=headers)
-        else:
-            df = pd.DataFrame(data)
-        return df
-    except requests.exceptions.RequestException as e:
-        print(f"Error al acceder a la URL: {e}")
-        return None
-    except Exception as e:
-        print(f"Ocurrió un error inesperado: {e}")
-        return None
-
-def find_company_online(query, priority_tickers=None):
-    """Busca una empresa en Yahoo Finance y devuelve (nombre, ticker)."""
-    query_clean = query.strip()
-    url = f"{YAHOO_FINANCE_SEARCH_URL}?q={query_clean}&quotesCount=10&newsCount=0"
-    headers = {"User-Agent": "Mozilla/5.0"}
-    r = requests.get(url, headers=headers)
-    data = r.json()
-    if "quotes" not in data or not data["quotes"]:
-        return None, None
-    results = [
-        (item.get("shortname") or item.get("longname") or item.get("symbol"), item["symbol"])
-        for item in data["quotes"]
-        if "symbol" in item
-    ]
-    if priority_tickers:
-        for name, ticker in results:
-            if ticker in priority_tickers:
-                return name, ticker
-    tickers = [sym for _, sym in results]
-    best_ticker_match = process.extractOne(query_clean.upper(), tickers, score_cutoff=80)
-    if best_ticker_match:
-        ticker = best_ticker_match[0]
-        name = next(name for name, sym in results if sym == ticker)
-        return name, ticker
-    names = [name for name, _ in results]
-    best_name_match = process.extractOne(query_clean, names, score_cutoff=60)
-    if best_name_match:
-        name = best_name_match[0]
-        ticker = next(sym for nm, sym in results if nm == name)
-        return name, ticker
-    return results[0] if results else (None, None)