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import gradio as gr
import json
import pandas as pd
import spacy
import subprocess
import sys
import logging
from pathlib import Path
from seo_analyzer import SEOSpaceAnalyzer
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def setup_spacy_model():
"""Carga o descarga el modelo spaCy necesario."""
try:
spacy.load("es_core_news_lg")
except OSError:
logger.info("Descargando spaCy model es_core_news_lg...")
subprocess.run([sys.executable, "-m", "spacy", "download", "es_core_news_lg"], check=True)
def create_interface() -> gr.Blocks:
analyzer = SEOSpaceAnalyzer()
with gr.Blocks(title="SEO Analyzer Pro", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🧠 SEO Analyzer Pro
Este espacio analiza contenido web orientado a normativa bancaria y genera:
- Temas inferidos automáticamente
- Títulos y meta descripciones SEO
- Alertas por lenguaje de riesgo
""")
with gr.Row():
sitemap_input = gr.Textbox(label="📍 URL del Sitemap", placeholder="https://ejemplo.com/sitemap.xml")
analyze_btn = gr.Button("🔍 Analizar")
clear_btn = gr.Button("🧹 Limpiar")
download_json_btn = gr.Button("📥 Descargar JSON")
download_csv_btn = gr.Button("📤 Descargar CSV")
status_output = gr.Textbox(label="Estado del análisis")
with gr.Tabs():
with gr.Tab("📊 Resumen"):
stats_output = gr.JSON(label="Estadísticas")
recommendations_output = gr.JSON(label="Recomendaciones SEO")
with gr.Tab("📝 Contenido"):
content_output = gr.JSON(label="Análisis de contenido")
with gr.Tab("🔗 Enlaces"):
links_output = gr.JSON(label="Análisis de enlaces")
links_plot = gr.Plot(label="Visualización de enlaces internos")
with gr.Tab("📄 Detalles"):
details_output = gr.JSON(label="Detalles por página")
with gr.Tab("🧠 SEO y Temas"):
seo_tags_output = gr.JSON(label="Metadatos SEO generados")
topics_output = gr.JSON(label="Temas inferidos")
flags_output = gr.JSON(label="Términos prohibidos detectados")
with gr.Tab("🔗 Similitud"):
similarity_output = gr.JSON(label="Similitud entre URLs")
def analyze_with_status(sitemap_url):
try:
result = analyzer.analyze_sitemap(sitemap_url)
return (*result, "✅ Análisis completado")
except Exception as e:
return [None]*8 + [f"❌ Error: {e}"]
def export_json():
if analyzer.current_analysis:
path = Path("content_storage/seo_report.json")
with open(path, "w", encoding="utf-8") as f:
json.dump(analyzer.current_analysis, f, ensure_ascii=False, indent=2)
return str(path)
return ""
def export_csv():
if not analyzer.current_analysis:
return ""
path = Path("content_storage/seo_summary.csv")
data = []
for url, seo in analyzer.current_analysis.get("seo_tags", {}).items():
data.append({
"url": url,
"title": seo.get("title", ""),
"meta_description": seo.get("meta_description", ""),
"flags": ", ".join(seo.get("flags", [])),
"topics": ", ".join(analyzer.current_analysis.get("topics", {}).get(url, [])),
"summary": analyzer.current_analysis.get("summaries", {}).get(url, "")
})
pd.DataFrame(data).to_csv(path, index=False)
return str(path)
analyze_btn.click(
fn=analyze_with_status,
inputs=sitemap_input,
outputs=[
stats_output, recommendations_output, content_output,
links_output, details_output, similarity_output,
seo_tags_output, status_output
]
)
clear_btn.click(fn=lambda: [None]*8, outputs=[
stats_output, recommendations_output, content_output,
links_output, details_output, similarity_output,
seo_tags_output, status_output
])
download_json_btn.click(fn=export_json, outputs=status_output)
download_csv_btn.click(fn=export_csv, outputs=status_output)
links_output.change(fn=analyzer.plot_internal_links, inputs=links_output, outputs=links_plot)
seo_tags_output.change(fn=lambda: analyzer.current_analysis.get("topics", {}), outputs=topics_output)
seo_tags_output.change(fn=lambda: analyzer.current_analysis.get("flags", {}), outputs=flags_output)
return demo
if __name__ == "__main__":
setup_spacy_model()
app = create_interface()
app.launch(server_name="0.0.0.0", server_port=7860)
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