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Update app.py
Browse files
app.py
CHANGED
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import sys
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# Silenciador del error fantasma de Linux (No congela, solo oculta el texto rojo)
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def silenciador_errores_basura(unraisable):
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if unraisable.exc_type == ValueError and "Invalid file descriptor: -1" in str(unraisable.exc_value): pass
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else: sys.__unraisablehook__(unraisable)
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sys.unraisablehook = silenciador_errores_basura
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import os
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import subprocess
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import pandas as pd
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from playwright.sync_api import sync_playwright
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import time
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import random
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import requests
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from PIL import Image
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import io
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import traceback
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# --- INSTALACIÓN DE DEPENDENCIAS ---
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try:
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subprocess.run(["pip", "install", "fake-useragent"], check=True)
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from fake_useragent import UserAgent
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# ---
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def descargar_imagen(url, idx):
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if not url or len(url) < 10 or url.startswith("data:"):
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return None
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try:
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r = requests.get(url, timeout=5, headers={"User-Agent": "Mozilla/5.0"})
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if r.status_code == 200:
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img = Image.open(io.BytesIO(r.content))
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if img.mode != 'RGB':
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img = img.convert('RGB')
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path = f"temp_img_{idx}.jpg"
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img.save(path, format="JPEG")
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return path
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except:
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return None # Si falla, devuelve None silenciosamente
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return None
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# --- 1. GENERADOR DE URLS ---
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def construir_urls_final(operacion, barrio, ciudad, tipo, hab, ban, park, antiguedad, m2_min, m2_max, ascensor, piscina):
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mapa_ant = {
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"Menos de 1 año": "de-0-a-1-anos",
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"
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}
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slug_ant = mapa_ant.get(antiguedad, "de-1-a-8-anos")
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slug_park = f"{int(park)}-parqueadero" if int(park) == 1 else f"{int(park)}-parqueaderos"
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b_slug = barrio.lower().strip().replace(" ", "-")
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c_slug = ciudad.lower().strip().replace(" ", "-")
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op_slug = operacion.lower().strip()
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tipo_slug = tipo.lower().strip()
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url_fr_base = f"https://www.fincaraiz.com.co/{op_slug}/{tipo_fr}/{b_slug}/{c_slug}/{int(hab)}-o-mas-habitaciones/{int(ban)}-o-mas-banos/{slug_park}/{slug_ant}/m2-desde-{int(m2_min)}/m2-hasta-{int(m2_max)}"
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if ascensor: url_fr_base += "/con-ascensor"
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if piscina: url_fr_base += "/con-piscina"
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url_mc = f"https://www.metrocuadrado.com/{tipo_slug}-casa-oficina/{op_slug}/{c_slug}/{b_slug}/{int(ban)}-banos-{int(hab)}-habitaciones/?search=form"
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return
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# --- 2.
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def
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patron = r'\$\s?(\d{1,3}(?:[.,]\d{3})*)'
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coincidencias = re.findall(patron, texto)
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if coincidencias:
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precios = [int(p.replace('.', '').replace(',', '')) for p in coincidencias]
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return 0
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if len(lineas) > 1:
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for linea in lineas[1:4]:
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if "$" not in linea and not linea.isdigit() and "m²" not in linea:
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return linea[:60]
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return "Ubicación en zona solicitada"
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# --- 3. MOTOR DE EXTRACCIÓN (SÍNCRONO Y ESTABLE) ---
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def motor_tramitia_visual(operacion, barrio, ciudad, area, m2_min, m2_max, tipo, hab, ban, park, antiguedad, ascensor, piscina):
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resultados = []
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log_visible = f"✅ INICIANDO EXTRACCIÓN:\nFR: {url_fr}\nMC: {url_mc}\n\n"
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ua = UserAgent()
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with sync_playwright() as p:
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browser = p.chromium.launch(headless=True, args=['--disable-blink-features=AutomationControlled', '--no-sandbox'])
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context = browser.new_context(viewport={'width': 1366, 'height': 768}, user_agent=ua.random)
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# --- FINCA RAÍZ ---
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try:
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page = context.new_page()
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log_visible += "🔄 FR: Buscando inmuebles...\n"
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page.goto(url_fr, wait_until="domcontentloaded", timeout=60000)
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for _ in range(3):
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page.mouse.wheel(0, 1000)
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time.sleep(1.5)
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except: continue # Si una tarjeta falla, salta a la siguiente
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page.close()
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except Exception as e: log_visible += f"⚠️ Error buscando en FR.\n"
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img_el = card.query_selector("img")
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img_url = img_el.get_attribute("src") if img_el else ""
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if href:
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resultados.append({
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"Portal": "Metrocuadrado",
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"Precio": precio,
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"Precio_M2": precio / area,
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"
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"
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"URL": f"https://www.metrocuadrado.com{href}" if href.startswith("/") else href,
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"Imagen": img_url
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})
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browser.close()
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if not resultados:
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return f"{log_visible}\n❌ NO SE ENCONTRARON DATOS.", pd.DataFrame(), None, "---"
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# --- LIMPIEZA ---
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df_crudo = pd.DataFrame(resultados).drop_duplicates(subset=['URL'])
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df_fr = df_crudo[df_crudo['Portal'] == 'Finca Raiz'].head(6)
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df_mc = df_crudo[df_crudo['Portal'] == 'Metrocuadrado'].head(6)
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df_final = pd.concat([df_fr, df_mc]).reset_index(drop=True)
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log_visible += f"\n✨ PROCESANDO PDF CON {len(df_final)} INMUEBLES..."
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# --- PDF ---
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pdf_path = f"Reporte_Visual_{int(time.time())}.pdf"
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pdf = FPDF()
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pdf.add_page()
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pdf.set_fill_color(40, 53, 147)
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pdf.set_text_color(255, 255, 255)
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pdf.cell(0, 15, f" REPORTE INMOBILIARIO: {barrio.upper()} ({operacion.upper()})", ln=True, fill=True)
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pdf.set_text_color(0, 0, 0)
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pdf.ln(5)
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y_start = pdf.get_y()
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img_path = descargar_imagen(r['Imagen'], idx)
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text_x = 10
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if img_path and os.path.exists(img_path):
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try:
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pdf.image(img_path, x=10, y=y_start, w=45, h=30)
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text_x = 60
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except: pass
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pdf.set_font("Arial", '', 8)
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pdf.set_text_color(0, 0, 0)
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pdf.multi_cell(0, 4, f"{r['Descripcion']}")
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pdf.set_x(text_x)
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pdf.set_font("Arial", 'U', 8)
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pdf.set_text_color(0, 102, 204)
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pdf.cell(0, 5, "🔗 Hacer clic aquí para ver publicación", link=r['URL'], ln=True)
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pdf.set_text_color(0, 0, 0)
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y_end = pdf.get_y()
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pdf.set_y(max(y_start + 35, y_end + 5))
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pdf.set_draw_color(200, 200, 200)
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pdf.line(10, pdf.get_y(), 200, pdf.get_y())
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pdf.ln(5)
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if img_path and os.path.exists(img_path):
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try: os.remove(img_path)
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except: pass
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promedio = df_final['Precio_M2'].mean() * area
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resumen = (
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f"💰 **ESTIMACIÓN DE {operacion.upper()}**\n"
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f"🔹 **Valor Sugerido:** ${promedio:,.0f}\n"
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f"📉 Mínimo Zona: ${
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f"📈 Máximo Zona: ${
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)
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except Exception as error_fatal:
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# PARACAÍDAS: Si todo colapsa, te mostrará exactamente DÓNDE y POR QUÉ en la pantalla
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traza = traceback.format_exc()
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return f"❌ ERROR GRAVE DEL SISTEMA:\n{str(error_fatal)}\n\nDetalles:\n{traza}", pd.DataFrame(), None, "⚠️ Falló la ejecución"
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# --- INTERFAZ GRÁFICA ---
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("##
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with gr.Row():
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with gr.Column(scale=1):
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op = gr.Radio(["Arriendo", "Venta"], label="¿Qué deseas hacer?", value="Arriendo")
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c = gr.Textbox(label="Ciudad", value="Barranquilla")
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b = gr.Textbox(label="Barrio (Ej: La Concepcion)", value="La Concepcion")
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with gr.Row():
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t = gr.Dropdown(["Apartamento", "Casa", "Bodega", "Lote", "Oficina"], label="Tipo", value="Apartamento")
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a = gr.Number(label="Área M2", value=70)
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with gr.Row():
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m2_min = gr.Number(label="M2 Mínimo", value=10)
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m2_max = gr.Number(label="M2 Máximo", value=200)
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piscina = gr.Checkbox(label="Con Piscina")
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with gr.Row():
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h = gr.Number(label="
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ban = gr.Number(label="Baños", value=2)
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p = gr.Number(label="
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e = gr.Dropdown(
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with gr.Column(scale=2):
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res_fin = gr.Markdown("### 💰 El resultado aparecerá aquí...")
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with gr.Tabs():
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with gr.TabItem("Descargar PDF"): out_pdf = gr.File()
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with gr.TabItem("Tabla de Datos"): out_df = gr.Dataframe()
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with gr.TabItem("Auditoría"): msg = gr.Textbox(lines=10)
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btn.click(
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demo.launch()
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import os
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import subprocess
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import pandas as pd
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from playwright.sync_api import sync_playwright
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import time
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import random
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# --- INSTALACIÓN DE DEPENDENCIAS ---
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try:
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subprocess.run(["pip", "install", "fake-useragent"], check=True)
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from fake_useragent import UserAgent
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# --- 1. GENERADOR DE URLS (VENTA/ARRIENDO Y EXTRAS) ---
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def construir_urls_final(operacion, barrio, ciudad, tipo, hab, ban, park, antiguedad, m2_min, m2_max, ascensor, piscina):
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mapa_ant = {
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"Menos de 1 año": "de-0-a-1-anos",
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"1 a 8 años": "de-1-a-8-anos",
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"9 a 15 años": "de-9-a-15-anos",
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"16 a 30 años": "de-16-a-30-anos",
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"Más de 30 años": "mas-de-30-anos"
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}
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slug_ant = mapa_ant.get(antiguedad, "de-1-a-8-anos")
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slug_park = f"{int(park)}-parqueadero" if int(park) == 1 else f"{int(park)}-parqueaderos"
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b_slug = barrio.lower().strip().replace(" ", "-")
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c_slug = ciudad.lower().strip().replace(" ", "-")
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op_slug = operacion.lower().strip() # 'venta' o 'arriendo'
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tipo_slug = tipo.lower().strip()
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if tipo_slug in ["apartamento", "casa"]:
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tipo_fr = "casas-y-apartamentos-y-apartaestudios"
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else:
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tipo_fr = tipo_slug + "s"
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# --- URL FINCA RAÍZ MULTIFILTRO ---
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url_fr_base = f"https://www.fincaraiz.com.co/{op_slug}/{tipo_fr}/{b_slug}/{c_slug}/{int(hab)}-o-mas-habitaciones/{int(ban)}-o-mas-banos/{slug_park}/{slug_ant}/m2-desde-{int(m2_min)}/m2-hasta-{int(m2_max)}"
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# Extras condicionales
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if ascensor: url_fr_base += "/con-ascensor"
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if piscina: url_fr_base += "/con-piscina"
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url_fr = url_fr_base
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# --- URL METROCUADRADO ---
|
| 55 |
+
# MC usa un formato más simple, le pasamos la operación dinámica
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| 56 |
url_mc = f"https://www.metrocuadrado.com/{tipo_slug}-casa-oficina/{op_slug}/{c_slug}/{b_slug}/{int(ban)}-banos-{int(hab)}-habitaciones/?search=form"
|
| 57 |
|
| 58 |
+
return url_fr, url_mc
|
| 59 |
|
| 60 |
+
# --- 2. EXTRACTOR DE PRECIO DINÁMICO ---
|
| 61 |
+
def extraer_precio_regex(texto, operacion):
|
| 62 |
patron = r'\$\s?(\d{1,3}(?:[.,]\d{3})*)'
|
| 63 |
coincidencias = re.findall(patron, texto)
|
| 64 |
if coincidencias:
|
| 65 |
precios = [int(p.replace('.', '').replace(',', '')) for p in coincidencias]
|
| 66 |
+
|
| 67 |
+
# Filtro de cordura según si es Venta o Arriendo
|
| 68 |
+
if operacion == "Arriendo":
|
| 69 |
+
precios_validos = [p for p in precios if 600000 <= p <= 40000000] # Ignora ventas y admón
|
| 70 |
+
else: # Venta
|
| 71 |
+
precios_validos = [p for p in precios if p >= 40000000] # Ignora arriendos cruzados
|
| 72 |
+
|
| 73 |
+
if precios_validos:
|
| 74 |
+
return precios_validos[0]
|
| 75 |
return 0
|
| 76 |
|
| 77 |
+
# --- 3. MOTOR DE EXTRACCIÓN ---
|
| 78 |
+
def motor_tramitia_final(operacion, barrio, ciudad, area, m2_min, m2_max, tipo, hab, ban, park, antiguedad, ascensor, piscina):
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| 79 |
resultados = []
|
| 80 |
+
url_fr, url_mc = construir_urls_final(operacion, barrio, ciudad, tipo, hab, ban, park, antiguedad, m2_min, m2_max, ascensor, piscina)
|
| 81 |
|
| 82 |
+
log_visible = f"✅ URLs GENERADAS (AUDITORÍA):\nFR: {url_fr}\nMC: {url_mc}\n\n"
|
| 83 |
+
ua = UserAgent()
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|
| 84 |
|
| 85 |
+
with sync_playwright() as p:
|
| 86 |
+
browser = p.chromium.launch(
|
| 87 |
+
headless=True,
|
| 88 |
+
args=[
|
| 89 |
+
'--disable-blink-features=AutomationControlled',
|
| 90 |
+
'--no-sandbox',
|
| 91 |
+
'--disable-infobars',
|
| 92 |
+
'--window-position=0,0',
|
| 93 |
+
f'--user-agent={ua.random}'
|
| 94 |
+
]
|
| 95 |
+
)
|
| 96 |
+
context = browser.new_context(viewport={'width': 1366, 'height': 768})
|
| 97 |
|
| 98 |
+
# --- FINCA RAÍZ ---
|
| 99 |
+
try:
|
| 100 |
+
page = context.new_page()
|
| 101 |
+
log_visible += f"🔄 Escaneando Finca Raíz ({operacion})...\n"
|
| 102 |
+
page.goto(url_fr, wait_until="domcontentloaded", timeout=60000)
|
| 103 |
+
|
| 104 |
+
page.mouse.move(random.randint(100, 500), random.randint(100, 500))
|
| 105 |
+
for _ in range(4):
|
| 106 |
+
page.mouse.wheel(0, 1000)
|
| 107 |
+
time.sleep(1.5)
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|
| 108 |
|
| 109 |
+
elementos = page.query_selector_all("a")
|
| 110 |
+
cont_fr = 0
|
| 111 |
+
|
| 112 |
+
for el in elementos:
|
| 113 |
+
if cont_fr >= 15: break
|
| 114 |
+
txt = el.inner_text()
|
| 115 |
|
| 116 |
+
if "$" in txt:
|
| 117 |
+
precio = extraer_precio_regex(txt, operacion) # Pasamos la operación
|
| 118 |
+
if precio > 0:
|
| 119 |
+
href = el.get_attribute("href")
|
| 120 |
+
full_url = f"https://www.fincaraiz.com.co{href}" if href and href.startswith("/") else href
|
| 121 |
+
|
| 122 |
+
if full_url:
|
| 123 |
+
resultados.append({
|
| 124 |
+
"Portal": "Finca Raiz",
|
| 125 |
+
"Precio": precio,
|
| 126 |
+
"Precio_M2": precio / area,
|
| 127 |
+
"Descripcion": txt.replace('\n', ' ')[:90] + "...",
|
| 128 |
+
"URL": full_url
|
| 129 |
+
})
|
| 130 |
+
cont_fr += 1
|
| 131 |
+
log_visible += f"✅ FR: Encontrados {cont_fr} candidatos brutos.\n"
|
| 132 |
+
page.close()
|
| 133 |
+
except Exception as e: log_visible += f"⚠️ Error FR: {e}\n"
|
| 134 |
+
|
| 135 |
+
# --- METROCUADRADO ---
|
| 136 |
+
try:
|
| 137 |
+
page = context.new_page()
|
| 138 |
+
log_visible += f"🔄 Escaneando Metrocuadrado ({operacion})...\n"
|
| 139 |
+
page.goto(url_mc, wait_until="domcontentloaded", timeout=60000)
|
| 140 |
+
|
| 141 |
+
for _ in range(5):
|
| 142 |
+
page.mouse.wheel(0, 1000)
|
| 143 |
+
time.sleep(1.5)
|
| 144 |
+
|
| 145 |
+
cards = page.query_selector_all("li, div[class*='card']")
|
| 146 |
+
cont_mc = 0
|
| 147 |
+
|
| 148 |
+
for card in cards:
|
| 149 |
+
if cont_mc >= 15: break
|
| 150 |
+
txt = card.inner_text()
|
| 151 |
|
| 152 |
+
if "$" in txt:
|
| 153 |
+
precio = extraer_precio_regex(txt, operacion)
|
| 154 |
+
if precio > 0:
|
| 155 |
+
enlace = card.query_selector("a")
|
| 156 |
+
if enlace:
|
| 157 |
+
href = enlace.get_attribute("href")
|
| 158 |
+
full_url = f"https://www.metrocuadrado.com{href}" if href and href.startswith("/") else href
|
|
|
|
| 159 |
|
| 160 |
+
if full_url:
|
|
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|
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|
|
|
|
|
| 161 |
resultados.append({
|
| 162 |
"Portal": "Metrocuadrado",
|
| 163 |
"Precio": precio,
|
| 164 |
"Precio_M2": precio / area,
|
| 165 |
+
"Descripcion": txt.replace('\n', ' ')[:90] + "...",
|
| 166 |
+
"URL": full_url
|
|
|
|
|
|
|
| 167 |
})
|
| 168 |
+
cont_mc += 1
|
| 169 |
+
log_visible += f"✅ MC: Encontrados {cont_mc} candidatos brutos.\n"
|
| 170 |
+
page.close()
|
| 171 |
+
except Exception as e: log_visible += f"⚠️ Error MC: {e}\n"
|
|
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|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 172 |
|
| 173 |
+
browser.close()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 174 |
|
| 175 |
+
if not resultados:
|
| 176 |
+
return f"{log_visible}\n❌ NO SE ENCONTRARON DATOS VÁLIDOS.", None, None, "---"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 177 |
|
| 178 |
+
# --- LIMPIEZA Y SELECCIÓN ---
|
| 179 |
+
df_crudo = pd.DataFrame(resultados)
|
| 180 |
+
df_limpio = df_crudo.drop_duplicates(subset=['URL'])
|
| 181 |
+
|
| 182 |
+
df_fr = df_limpio[df_limpio['Portal'] == 'Finca Raiz'].head(6)
|
| 183 |
+
df_mc = df_limpio[df_limpio['Portal'] == 'Metrocuadrado'].head(6)
|
| 184 |
+
|
| 185 |
+
df_final = pd.concat([df_fr, df_mc]).reset_index(drop=True)
|
| 186 |
+
|
| 187 |
+
log_visible += f"\n✨ PROCESADO FINAL: {len(df_final)} inmuebles únicos seleccionados."
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 188 |
|
| 189 |
+
# --- PDF ---
|
| 190 |
+
pdf_path = f"Reporte_{int(time.time())}.pdf"
|
| 191 |
+
pdf = FPDF()
|
| 192 |
+
pdf.add_page()
|
| 193 |
+
pdf.set_font("Arial", 'B', 14)
|
| 194 |
+
pdf.cell(0, 10, f"ESTUDIO DE MERCADO: {barrio.upper()} ({operacion.upper()})", ln=True)
|
| 195 |
+
pdf.ln(5)
|
| 196 |
+
|
| 197 |
+
for _, r in df_final.iterrows():
|
| 198 |
+
pdf.set_fill_color(240, 240, 240)
|
| 199 |
+
pdf.set_font("Arial", 'B', 10)
|
| 200 |
+
pdf.cell(0, 8, f"{r['Portal']} - ${r['Precio']:,.0f}", ln=True, fill=True)
|
| 201 |
+
pdf.set_font("Arial", '', 9)
|
| 202 |
+
pdf.multi_cell(0, 5, f"{r['Descripcion']}")
|
| 203 |
+
pdf.set_font("Arial", 'U', 8); pdf.set_text_color(0,0,255)
|
| 204 |
+
pdf.cell(0, 6, "Ver Publicacion Original", link=r['URL'], ln=True)
|
| 205 |
+
pdf.set_text_color(0,0,0); pdf.ln(3)
|
| 206 |
+
pdf.output(pdf_path)
|
| 207 |
+
|
| 208 |
+
# --- CÁLCULOS ---
|
| 209 |
+
if not df_final.empty:
|
| 210 |
promedio = df_final['Precio_M2'].mean() * area
|
| 211 |
+
minimo = df_final['Precio'].min()
|
| 212 |
+
maximo = df_final['Precio'].max()
|
| 213 |
+
|
| 214 |
resumen = (
|
| 215 |
f"💰 **ESTIMACIÓN DE {operacion.upper()}**\n"
|
| 216 |
f"🔹 **Valor Sugerido:** ${promedio:,.0f}\n"
|
| 217 |
+
f"📉 Mínimo Zona: ${minimo:,.0f}\n"
|
| 218 |
+
f"📈 Máximo Zona: ${maximo:,.0f}"
|
| 219 |
)
|
| 220 |
+
else:
|
| 221 |
+
resumen = "⚠️ No hay suficientes datos para calcular."
|
| 222 |
+
|
| 223 |
+
return f"{log_visible}\n✅ Tarea Completada.", df_final, pdf_path, resumen
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 224 |
|
| 225 |
# --- INTERFAZ GRÁFICA ---
|
| 226 |
with gr.Blocks(theme=gr.themes.Soft()) as demo:
|
| 227 |
+
gr.Markdown("## 🤖 TramitIA Pro: Tasador Inmobiliario Integral")
|
| 228 |
|
| 229 |
with gr.Row():
|
| 230 |
with gr.Column(scale=1):
|
| 231 |
op = gr.Radio(["Arriendo", "Venta"], label="¿Qué deseas hacer?", value="Arriendo")
|
| 232 |
+
|
| 233 |
c = gr.Textbox(label="Ciudad", value="Barranquilla")
|
| 234 |
b = gr.Textbox(label="Barrio (Ej: La Concepcion)", value="La Concepcion")
|
| 235 |
|
| 236 |
with gr.Row():
|
| 237 |
t = gr.Dropdown(["Apartamento", "Casa", "Bodega", "Lote", "Oficina"], label="Tipo", value="Apartamento")
|
| 238 |
+
a = gr.Number(label="Área M2 (Tu Cliente)", value=70)
|
| 239 |
|
| 240 |
+
gr.Markdown("### Filtros de Búsqueda")
|
| 241 |
with gr.Row():
|
| 242 |
m2_min = gr.Number(label="M2 Mínimo", value=10)
|
| 243 |
m2_max = gr.Number(label="M2 Máximo", value=200)
|
|
|
|
| 247 |
piscina = gr.Checkbox(label="Con Piscina")
|
| 248 |
|
| 249 |
with gr.Row():
|
| 250 |
+
h = gr.Number(label="Habitaciones", value=3)
|
| 251 |
ban = gr.Number(label="Baños", value=2)
|
| 252 |
+
p = gr.Number(label="Parqueaderos", value=1)
|
| 253 |
|
| 254 |
+
e = gr.Dropdown(
|
| 255 |
+
["Menos de 1 año", "1 a 8 años", "9 a 15 años", "16 a 30 años", "Más de 30 años"],
|
| 256 |
+
label="Antigüedad", value="1 a 8 años"
|
| 257 |
+
)
|
| 258 |
+
btn = gr.Button("EJECUTAR ANÁLISIS COMERCIAL", variant="primary")
|
| 259 |
|
| 260 |
with gr.Column(scale=2):
|
| 261 |
res_fin = gr.Markdown("### 💰 El resultado aparecerá aquí...")
|
| 262 |
with gr.Tabs():
|
| 263 |
+
with gr.TabItem("Auditoría"): msg = gr.Textbox(lines=10, label="Log del Sistema")
|
| 264 |
+
with gr.TabItem("Tabla de Resultados"): out_df = gr.Dataframe()
|
| 265 |
with gr.TabItem("Descargar PDF"): out_pdf = gr.File()
|
|
|
|
|
|
|
| 266 |
|
| 267 |
+
btn.click(motor_tramitia_final,
|
| 268 |
+
[op, b, c, a, m2_min, m2_max, t, h, ban, p, e, ascensor, piscina],
|
| 269 |
+
[msg, out_df, out_pdf, res_fin])
|
| 270 |
|
| 271 |
demo.launch()
|