| import gradio as gr
|
| import json
|
| import asyncio
|
| import os
|
| import sys
|
| import re
|
|
|
|
|
| FLOATING_CARD_JS = ''
|
|
|
|
|
|
|
| import time
|
|
|
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
|
| from dotenv import load_dotenv
|
| _project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
| load_dotenv(os.path.join(_project_root, ".env"))
|
|
|
| from backend.pipeline import ResearchPipeline
|
| from backend.tools.search_engine import search
|
| from backend.tools.graph_generator import generator as graph_generator
|
| from modules.graph_module import generate_interactive_graph
|
| from backend.synthesis import PROVIDERS
|
| from backend.prompts.profiles import AGENT_PROFILES
|
| from .utils import format_results_for_dataframe, format_error
|
|
|
| DEFAULT_MODEL = "mistral-small-2506"
|
|
|
|
|
| GROUPS = ["all", "latam", "global", "tesis", "iberoamerica", "peru", "brasil", "ecuador", "mexico", "ai_ml"]
|
|
|
|
|
| INDIVIDUAL_SOURCES = [
|
| "alicia", "renati", "lareferencia", "bdtd", "rraae",
|
| "semantic", "openalex", "pubmed", "arxiv", "crossref",
|
| "dblp", "scopus", "zenodo", "openaire", "doaj",
|
| "core", "redalyc", "serpapi"
|
| ]
|
|
|
| ALL_SOURCES = GROUPS + INDIVIDUAL_SOURCES
|
|
|
| _active_pipeline = None
|
|
|
|
|
| def _control_stop():
|
| """Stop the active pipeline"""
|
| global _active_pipeline
|
| if _active_pipeline:
|
| _active_pipeline.stop()
|
| return _build_status_html("error", "⛔ Detenido por el usuario")
|
| return _build_status_html("idle")
|
|
|
|
|
| def _control_pause():
|
| """Pause the active pipeline"""
|
| global _active_pipeline
|
| if _active_pipeline:
|
| _active_pipeline.pause()
|
| return _build_status_html("running", "⏸️ Pausado — haz clic en Reanudar")
|
| return _build_status_html("idle")
|
|
|
|
|
| def _control_resume():
|
| """Resume the active pipeline"""
|
| global _active_pipeline
|
| if _active_pipeline:
|
| _active_pipeline.resume()
|
| return _build_status_html("running", "▶️ Reanudado")
|
| return _build_status_html("idle")
|
|
|
|
|
| def _build_controls_html(state="idle"):
|
| """Build the control buttons bar matching Next.js AgentView"""
|
| if state == "idle":
|
| return '''
|
| <div style="display:flex; gap:8px; align-items:center; padding:8px 0;">
|
| <span style="font-size:12px; color:var(--text-muted, #9ca3af);">
|
| ⏹️ Pipeline inactivo
|
| </span>
|
| </div>'''
|
|
|
| if state == "paused":
|
| return '''
|
| <div style="
|
| display:flex; gap:8px; align-items:center; padding:10px 16px;
|
| background:rgba(245,158,11,0.08); border:1px solid rgba(245,158,11,0.3);
|
| border-radius:12px; animation:pulse 2s infinite;
|
| ">
|
| <span style="width:8px;height:8px;border-radius:50%;background:#f59e0b;box-shadow:0 0 8px rgba(245,158,11,0.5);"></span>
|
| <span style="font-size:13px; font-weight:600; color:#f59e0b;">⏸️ Pipeline pausado</span>
|
| <span style="font-size:11px; color:var(--text-muted, #9ca3af); margin-left:8px;">Haz clic en ▶ Reanudar para continuar</span>
|
| </div>'''
|
|
|
| if state == "stopped":
|
| return '''
|
| <div style="
|
| display:flex; gap:8px; align-items:center; padding:10px 16px;
|
| background:rgba(239,68,68,0.08); border:1px solid rgba(239,68,68,0.3);
|
| border-radius:12px;
|
| ">
|
| <span style="width:8px;height:8px;border-radius:50%;background:#ef4444;"></span>
|
| <span style="font-size:13px; font-weight:600; color:#ef4444;">⛔ Pipeline detenido</span>
|
| </div>'''
|
|
|
|
|
| return '''
|
| <div style="
|
| display:flex; gap:8px; align-items:center; padding:8px 0;
|
| ">
|
| <span style="font-size:12px; color:var(--text-muted, #9ca3af);">
|
| ⚡ Pipeline activo — usa los botones para controlar
|
| </span>
|
| </div>'''
|
|
|
|
|
| PHASES = [
|
| {"id": -1, "label": "Verificación de Fuentes", "icon": "🏥", "pct": 0, "color": "#6b7280"},
|
| {"id": 0, "label": "Optimización de Queries", "icon": "🧠", "pct": 5, "color": "#8b5cf6"},
|
| {"id": 1, "label": "Búsqueda Iterativa", "icon": "🔍", "pct": 15, "color": "#3b82f6"},
|
| {"id": 2, "label": "Detección de Vacíos", "icon": "🔎", "pct": 35, "color": "#06b6d4"},
|
| {"id": 3, "label": "Búsqueda de Rescate", "icon": "🚑", "pct": 45, "color": "#f59e0b"},
|
| {"id": 4, "label": "Plan Maestro", "icon": "📋", "pct": 55, "color": "#10b981"},
|
| {"id": 5, "label": "Redacción de Secciones", "icon": "✍️", "pct": 65, "color": "#a855f7"},
|
| {"id": 6, "label": "Validación y Corrección", "icon": "✅", "pct": 90, "color": "#22c55e"},
|
| {"id": 7, "label": "Completado", "icon": "🎉", "pct": 100,"color": "#10b981"},
|
| ]
|
|
|
|
|
| SOURCE_COLORS = {
|
| "pubmed": "#3b82f6", "semantic_scholar": "#8b5cf6", "openalex": "#06b6d4",
|
| "crossref": "#f59e0b", "arxiv": "#ef4444", "doaj": "#10b981",
|
| "zenodo": "#6366f1", "dblp": "#ec4899", "openaire": "#14b8a6",
|
| "core": "#f97316", "scielo": "#22c55e", "redalyc": "#a855f7",
|
| "latindex": "#0ea5e9", "dialnet": "#e11d48", "la_referencia": "#84cc16",
|
| }
|
|
|
| GRADE_COLORS = {
|
| "1A": "#10b981", "1B": "#22c55e", "2A": "#3b82f6", "2B": "#60a5fa",
|
| "3A": "#f59e0b", "3B": "#fbbf24", "4": "#f97316", "5": "#ef4444", "6": "#6b7280",
|
| }
|
|
|
|
|
| def update_models(prov_name):
|
| cfg = PROVIDERS.get(prov_name, PROVIDERS["mistral"])
|
| return gr.update(choices=cfg["models"], value=cfg["models"][0])
|
|
|
|
|
| def _build_progress_html(phase_id, extra=""):
|
| """Build a premium glassmorphic progress bar matching the search-popup style"""
|
| phase = next((p for p in PHASES if p["id"] == phase_id), PHASES[-1])
|
| pct = phase["pct"]
|
| label = phase["label"]
|
| icon = phase["icon"]
|
| color = phase["color"]
|
|
|
|
|
| dots_html = ""
|
| for p in PHASES:
|
| if p["id"] < 0:
|
| continue
|
| is_done = p["pct"] <= pct and pct > 0
|
| is_active = p["id"] == phase_id
|
| dot_color = p["color"] if is_done else "rgba(255,255,255,0.1)"
|
| dot_size = "10px" if is_active else "8px"
|
| glow = f"box-shadow:0 0 8px {p['color']}60;" if is_active else ""
|
| border = f"border:2px solid {p['color']};" if is_active else ""
|
| dots_html += f'''<div title="{p['icon']} {p['label']}" style="
|
| width:{dot_size}; height:{dot_size}; border-radius:50%;
|
| background:{dot_color}; {glow} {border}
|
| transition:all 0.3s ease; cursor:pointer;
|
| "></div>'''
|
|
|
| extra_html = f'''<div style="font-size:11px; color:var(--text-muted, #9ca3af); margin-top:6px;
|
| padding:4px 10px; border-radius:6px; background:rgba(139,92,246,0.06);
|
| border:1px solid rgba(139,92,246,0.15);
|
| ">{extra}</div>''' if extra else ""
|
|
|
| pulse_anim = "animation:pulse 2s infinite;" if pct < 100 and pct > 0 else ""
|
|
|
| return f'''
|
| <div style="
|
| background: var(--glass, rgba(17,24,39,0.6));
|
| backdrop-filter: blur(16px);
|
| border: 1px solid var(--glass-border, rgba(255,255,255,0.08));
|
| border-radius: 14px; padding: 16px 20px;
|
| font-family: Inter, system-ui, sans-serif;
|
| ">
|
| <div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:10px;">
|
| <div style="display:flex; align-items:center; gap:10px;">
|
| <span style="
|
| font-size:20px; width:36px; height:36px; display:flex; align-items:center; justify-content:center;
|
| background:linear-gradient(135deg, {color}20, {color}10);
|
| border:1px solid {color}40; border-radius:10px; {pulse_anim}
|
| ">{icon}</span>
|
| <div>
|
| <div style="font-size:14px; font-weight:700; color:var(--text, #fff);">
|
| {label}
|
| </div>
|
| <div style="font-size:11px; color:var(--text-muted, #9ca3af);">
|
| Fase {max(0, phase_id + 1)} de {len(PHASES) - 1}
|
| </div>
|
| </div>
|
| </div>
|
| <span style="
|
| font-size:13px; font-weight:700; color:{color};
|
| padding:4px 12px; border-radius:20px;
|
| background:{color}15; border:1px solid {color}30;
|
| ">{pct}%</span>
|
| </div>
|
| <div style="background:rgba(255,255,255,0.05); border-radius:8px; height:8px; overflow:hidden; margin-bottom:10px;">
|
| <div style="width:{pct}%; height:100%; border-radius:8px;
|
| background:linear-gradient(90deg, {color}, {color}cc);
|
| transition:width 0.8s cubic-bezier(0.23,1,0.32,1);
|
| box-shadow:0 0 12px {color}40;
|
| "></div>
|
| </div>
|
| <div style="display:flex; gap:6px; align-items:center; justify-content:center;">
|
| {dots_html}
|
| </div>
|
| {extra_html}
|
| </div>'''
|
|
|
|
|
| def _build_status_html(state="idle", extra=""):
|
| """Build a premium status indicator"""
|
| configs = {
|
| "idle": {"color": "#6b7280", "icon": "⏹️", "label": "Inactivo", "bg": "rgba(107,114,128,0.08)", "border": "rgba(107,114,128,0.2)"},
|
| "running": {"color": "#8b5cf6", "icon": "⚡", "label": "En ejecución...", "bg": "rgba(139,92,246,0.08)", "border": "rgba(139,92,246,0.3)"},
|
| "done": {"color": "#10b981", "icon": "✅", "label": "Completado", "bg": "rgba(16,185,129,0.08)", "border": "rgba(16,185,129,0.3)"},
|
| "error": {"color": "#ef4444", "icon": "❌", "label": "Error", "bg": "rgba(239,68,68,0.08)", "border": "rgba(239,68,68,0.3)"},
|
| }
|
| cfg = configs.get(state, configs["idle"])
|
| pulse = "animation:pulse 2s infinite;" if state == "running" else ""
|
| extra_html = f'<span style="color:var(--text-muted, #9ca3af); margin-left:8px; font-size:12px;">{extra}</span>' if extra else ""
|
|
|
| return f'''
|
| <div style="
|
| display:inline-flex; align-items:center; gap:10px;
|
| background:{cfg['bg']}; border:1px solid {cfg['border']};
|
| border-radius:10px; padding:8px 16px;
|
| backdrop-filter:blur(12px); {pulse}
|
| ">
|
| <span style="
|
| width:8px; height:8px; border-radius:50%;
|
| background:{cfg['color']}; box-shadow:0 0 8px {cfg['color']}60;
|
| "></span>
|
| <span style="font-size:13px; font-weight:600; color:{cfg['color']};">
|
| {cfg['icon']} {cfg['label']}
|
| </span>
|
| {extra_html}
|
| </div>'''
|
|
|
|
|
| def _parse_sections_from_report(report_md):
|
| if not report_md:
|
| return {}
|
| sections = {}
|
| current = None
|
| current_lines = []
|
| for line in report_md.split("\n"):
|
|
|
| m = re.match(r'^#{2,3}\s+(.+)', line)
|
|
|
| if not m:
|
| m = re.match(r'\\(?:sub)*section\{(.+?)\}', line)
|
| if m:
|
| if current:
|
| sections[current] = "\n".join(current_lines).strip()
|
| title = m.group(1).strip()
|
| title = re.sub(r'^[🔬📝📊🔎🚑📋✍️✅🎉🏥🧠🔍\s]+', '', title).strip()
|
| if not title:
|
| title = current or "Sin título"
|
| current = title
|
| current_lines = []
|
| else:
|
| current_lines.append(line)
|
| if current:
|
| sections[current] = "\n".join(current_lines).strip()
|
| return sections
|
|
|
|
|
|
|
| def _build_references_html(docs_df, report_md=""):
|
| if docs_df is None or docs_df.empty:
|
| return "_Sin referencias disponibles aún..._"
|
|
|
| import json as _json
|
| import re
|
| import math
|
| import base64
|
|
|
|
|
| cited_indices = set()
|
| if report_md:
|
| for match in re.finditer(r'\[(\d+)\]', report_md):
|
| cited_indices.add(int(match.group(1)))
|
|
|
| has_text_produced = bool(report_md.strip())
|
|
|
| html = '<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:16px; padding-bottom:12px; border-bottom:1px solid rgba(255,255,255,0.1);">'
|
| html += '<div id="refs-stats" style="font-size:13px; color:#9ca3af; font-weight:500;"></div>'
|
|
|
|
|
| html += '<div style="display:flex; gap:12px; align-items:center;">'
|
| if has_text_produced:
|
| html += '''
|
| <label style="display:flex; align-items:center; gap:6px; font-size:13px; color:#d1d5db; cursor:pointer;">
|
| <input type="checkbox" id="refs-filter-cited" onchange="document.getElementById('refs-container').setAttribute('data-page', '1'); initRefsPagination()" style="accent-color:var(--accent, #8b5cf6); width:16px; height:16px;">
|
| Solo citados en texto
|
| </label>
|
| '''
|
| else:
|
| html += '<input type="checkbox" id="refs-filter-cited" style="display:none;">'
|
|
|
| html += '</div></div>'
|
|
|
| html += '<div id="refs-container" data-page="1" style="display:flex; flex-direction:column; gap:12px; min-height:400px;">'
|
|
|
| for idx, row in docs_df.iterrows():
|
| num = idx + 1
|
| autores = str(row.get("Autores", ""))
|
| año = str(row.get("Año", ""))
|
| titulo = str(row.get("Título", ""))
|
| fuente = str(row.get("Fuente", ""))
|
| grade = str(row.get("GRADE", ""))
|
|
|
| parts = [a.strip() for a in autores.split(",")]
|
| surnames = [p.split()[-1] for p in parts if p and "..." not in p]
|
|
|
| if len(surnames) == 1:
|
| cite_text = f"{surnames[0]} ({año})"
|
| elif len(surnames) == 2:
|
| cite_text = f"{surnames[0]} y {surnames[1]} ({año})"
|
| elif len(surnames) > 2:
|
| cite_text = f"{surnames[0]} et al. ({año})"
|
| else:
|
| cite_text = f"Sin Autor ({año})"
|
|
|
| level_key = grade.split(" - ")[0].strip().upper() if grade else "UNKNOWN"
|
| color = GRADE_COLORS.get(level_key, "#6b7280")
|
|
|
| import math
|
| import base64
|
| found = {k: ("" if (isinstance(v, float) and math.isnan(v)) else v) for k, v in row.to_dict().items()}
|
| data_json = _json.dumps(found, ensure_ascii=False)
|
| data_b64 = base64.b64encode(data_json.encode('utf-8')).decode('utf-8')
|
|
|
| is_cited = str(num in cited_indices).lower()
|
| initial_display = "flex" if idx < 10 else "none"
|
|
|
| html += f'''
|
| <div class="ref-item" data-cited="{is_cited}" style="display:{initial_display}; padding:14px; border-radius:10px; background:var(--glass, rgba(17,24,39,0.4)); border:1px solid var(--glass-border, rgba(255,255,255,0.06)); gap:14px; align-items:flex-start; transition: all 0.2s;">
|
| <div style="font-weight:800; color:var(--accent); min-width:32px; font-size:16px;">[{num}]</div>
|
| <div style="flex-grow:1;">
|
| <div style="margin-bottom:6px; line-height:1.4;">
|
| <span class="cite-link" data-cite-b64="{data_b64}" onclick="showCiteCard(this, {idx})" style="font-weight:700; font-size:15px; cursor:pointer; color:var(--accent, #8b5cf6);">
|
| [{num}] {cite_text}.
|
| </span> <span style="font-style:italic; font-size:15px; opacity:0.9;">{titulo}</span>
|
| </div>
|
| <div style="display:flex; gap:8px; margin-top:8px; align-items:center; flex-wrap:wrap;">
|
| <span style="font-size:11px; font-weight:600; padding:3px 10px; border-radius:12px; background:rgba(255,255,255,0.08);">{fuente}</span>
|
| <span style="font-size:11px; font-weight:600; padding:3px 10px; border-radius:12px; background:{color}15; border:1px solid {color}40; color:{color};">{grade}</span>
|
| </div>
|
| </div>
|
| </div>
|
| '''
|
|
|
| html += '</div>'
|
| html += '<div id="refs-pagination" style="display:flex; justify-content:center; align-items:center; margin-top:24px; padding-top:16px; border-top:1px solid rgba(255,255,255,0.05); gap:4px;"></div>'
|
|
|
| html += '<img src="data:image/gif;base64,R0lGODlhAQABAIAAAP///wAAACH5BAEAAAAALAAAAAABAAEAAAICRAEAOw==" onload="if(window.initRefsPagination) window.initRefsPagination();" style="display:none;">'
|
|
|
| html += FLOATING_CARD_JS
|
| return html
|
|
|
|
|
| def _build_stats_html(report_md, docs_df):
|
| """Build a premium stats dashboard matching the search-popup card style"""
|
| import pandas as pd
|
| total_docs = len(docs_df) if docs_df is not None and not docs_df.empty else 0
|
| sections = _parse_sections_from_report(report_md)
|
| total_sections = len(sections)
|
| word_count = len(report_md.split()) if report_md else 0
|
|
|
| grade_data = {}
|
| if docs_df is not None and not docs_df.empty and "GRADE" in docs_df.columns:
|
| grade_data = docs_df["GRADE"].value_counts().to_dict()
|
|
|
|
|
| stats = [
|
| ("📄", "Documentos", str(total_docs), "#3b82f6"),
|
| ("📑", "Secciones", str(total_sections), "#8b5cf6"),
|
| ("📝", "Palabras", f"{word_count:,}", "#10b981"),
|
| ]
|
|
|
| cards_html = ""
|
| for icon, label, val, color in stats:
|
| cards_html += f'''
|
| <div class="stat-card" style="
|
| position:relative; overflow:hidden;
|
| ">
|
| <div style="
|
| position:absolute; top:0; left:0; right:0; height:3px;
|
| background:linear-gradient(90deg, {color}, {color}60);
|
| "></div>
|
| <div style="font-size:24px; margin-bottom:6px; margin-top:4px;">{icon}</div>
|
| <div style="
|
| font-size:26px; font-weight:800; color:{color};
|
| letter-spacing:-0.5px; line-height:1;
|
| ">{val}</div>
|
| <div style="
|
| font-size:11px; color:var(--text-muted, #9ca3af); margin-top:6px;
|
| font-weight:500; text-transform:uppercase; letter-spacing:0.5px;
|
| ">{label}</div>
|
| </div>'''
|
|
|
|
|
| grade_html = ""
|
| if grade_data:
|
| grade_badges = ""
|
| for label, count in sorted(grade_data.items(), key=lambda x: -x[1]):
|
| level_key = label.split(" - ")[0].strip() if " - " in label else label
|
| color = GRADE_COLORS.get(level_key.upper(), "#6b7280")
|
| grade_badges += f'''<span style="
|
| display:inline-flex; align-items:center; gap:5px;
|
| padding:4px 10px; border-radius:20px; font-size:11px; font-weight:600;
|
| background:{color}15; border:1px solid {color}40; color:{color};
|
| ">
|
| <span style="width:6px;height:6px;border-radius:50%;background:{color};"></span>
|
| {label}: {count}
|
| </span>'''
|
|
|
| grade_html = f'''
|
| <div style="margin-top:12px; padding-top:12px; border-top:1px solid var(--glass-border, rgba(255,255,255,0.06));">
|
| <div style="font-size:12px; font-weight:600; color:var(--text-muted, #9ca3af); margin-bottom:8px;">
|
| 🏅 Distribución GRADE
|
| </div>
|
| <div style="display:flex; flex-wrap:wrap; gap:6px;">
|
| {grade_badges}
|
| </div>
|
| </div>'''
|
|
|
| return f'''
|
| <div style="
|
| background:var(--glass, rgba(17,24,39,0.6));
|
| backdrop-filter:blur(16px);
|
| border:1px solid var(--glass-border, rgba(255,255,255,0.08));
|
| border-radius:14px; padding:16px 20px;
|
| ">
|
| <div style="display:grid; grid-template-columns:repeat(auto-fit, minmax(120px, 1fr)); gap:12px;">
|
| {cards_html}
|
| </div>
|
| {grade_html}
|
| </div>'''
|
|
|
|
|
|
|
| def _generate_graph_from_df(df):
|
| return generate_interactive_graph(df)
|
|
|
|
|
| def _detect_phase(report_md):
|
| if not report_md:
|
| return 0
|
| text = report_md.lower()
|
| if ("completado" in text and ("secciones:" in text or "docs citados:" in text)) or "fase 8" in text:
|
| return 8
|
| if "reporte final" in text or "generando reporte" in text:
|
| return 7
|
| if "grade" in text or "clasificación grade" in text:
|
| return 6
|
| if ("validación" in text or "validate" in text or "ara+" in text) and "recuperación" not in text:
|
| return 6
|
| if "redactando" in text or "redacción" in text or "writing" in text:
|
| return 5
|
| if "plan maestro" in text or "master plan" in text or "fase 4" in text:
|
| return 4
|
| if "rescate" in text or "rescue" in text or "fase 3" in text:
|
| return 3
|
| if "detección de vacíos" in text or "gap detection" in text or "fase 2" in text:
|
| return 2
|
| if "ronda" in text or "buscando" in text or "búsqueda" in text:
|
| return 1
|
| if "optimiz" in text or "query" in text:
|
| return 0
|
| return 0
|
|
|
|
|
|
|
|
|
|
|
| SECTION_COLORS = [
|
| "#8b5cf6", "#3b82f6", "#06b6d4", "#10b981", "#f59e0b",
|
| "#ef4444", "#ec4899", "#6366f1", "#14b8a6", "#f97316",
|
| ]
|
|
|
| def _build_section_cards_html(sections_map, is_done=False):
|
| """Build glassmorphic expandable section cards"""
|
| if not sections_map:
|
| return '''<div style="
|
| text-align:center; padding:40px 20px; color:#6b7280;
|
| ">
|
| <div style="font-size:36px; margin-bottom:10px; opacity:0.5;">📑</div>
|
| <div style="font-size:13px;">Las secciones aparecerán aquí durante la ejecución...</div>
|
| </div>'''
|
|
|
| cards = ""
|
| for i, (title, content) in enumerate(sections_map.items()):
|
| color = SECTION_COLORS[i % len(SECTION_COLORS)]
|
| word_count = len(content.split()) if content else 0
|
| status_icon = "✅" if (is_done or word_count > 50) else "⏳"
|
| sec_id = f"sec_{i}"
|
|
|
|
|
| content_preview = content[:300].replace("<", "<").replace(">", ">") if content else ""
|
| content_full = content.replace("<", "<").replace(">", ">") if content else ""
|
|
|
|
|
| content_escaped = content.replace("'", "\\'").replace("\n", "\\n").replace('"', '"') if content else ""
|
|
|
| cards += f'''
|
| <div class="section-card" style="animation:slideIn 0.3s ease {i * 0.06}s both;">
|
| <!-- Color accent -->
|
| <div style="height:3px; background:linear-gradient(90deg, {color}, {color}80);"></div>
|
|
|
| <!-- Header (clickable to expand) -->
|
| <div class="section-card-header" onclick="
|
| var body=document.getElementById('{sec_id}_body');
|
| var arrow=document.getElementById('{sec_id}_arrow');
|
| if(body.style.display==='none'){{body.style.display='block';arrow.textContent='▲';}}
|
| else{{body.style.display='none';arrow.textContent='▼';}}
|
| ">
|
| <div style="display:flex; align-items:center; gap:10px;">
|
| <div style="
|
| width:28px; height:28px; border-radius:8px;
|
| background:linear-gradient(135deg, {color}25, {color}10);
|
| border:1px solid {color}40;
|
| display:flex; align-items:center; justify-content:center;
|
| font-size:12px; font-weight:700; color:{color};
|
| ">{i+1}</div>
|
| <div>
|
| <div style="font-size:13px; font-weight:600; color:var(--text, #fff);">{title}</div>
|
| <div style="font-size:11px; color:var(--text-muted, #9ca3af); margin-top:2px;">
|
| {status_icon} {word_count} palabras
|
| </div>
|
| </div>
|
| </div>
|
| <div style="display:flex; align-items:center; gap:8px;">
|
| <button onclick="
|
| event.stopPropagation();
|
| navigator.clipboard.writeText('{content_escaped}');
|
| this.textContent='✅ Copiado';
|
| var btn=this;
|
| setTimeout(function(){{btn.textContent='📋';}},1500);
|
| " style="
|
| background:rgba(139,92,246,0.08); border:1px solid rgba(139,92,246,0.2);
|
| color:#8b5cf6; border-radius:6px; padding:4px 8px;
|
| font-size:11px; cursor:pointer; transition:all 0.2s;
|
| " title="Copiar sección">📋</button>
|
| <span id="{sec_id}_arrow" style="color:var(--text-muted, #9ca3af); font-size:12px;">▼</span>
|
| </div>
|
| </div>
|
|
|
| <!-- Body (collapsed by default) -->
|
| <div id="{sec_id}_body" class="section-card-body" style="display:none;">
|
| <div style="
|
| font-size:13px; line-height:1.7; color:var(--text, #e5e7eb);
|
| padding-top:12px; white-space:pre-wrap;
|
| ">{content_full}</div>
|
| </div>
|
| </div>'''
|
|
|
| return f'''<div style="max-height:650px; overflow-y:auto; padding-right:4px;">
|
| {cards}
|
| </div>'''
|
|
|
|
|
|
|
|
|
|
|
|
|
| def _build_docs_index(docs_df):
|
| """Build a lookup dict: author_year_key -> paper details."""
|
| import pandas as pd
|
| index = {}
|
| if docs_df is None or (hasattr(docs_df, 'empty') and docs_df.empty):
|
| return index
|
|
|
| rows = docs_df.to_dict(orient="records") if hasattr(docs_df, 'to_dict') else []
|
| for row in rows:
|
| title = row.get("Título", row.get("title", ""))
|
| authors_raw = row.get("Autores", row.get("authors", ""))
|
| year = str(row.get("Año", row.get("year", "")))
|
| doi = row.get("DOI", row.get("doi", ""))
|
| source = row.get("Fuente", row.get("source", ""))
|
| grade = row.get("GRADE", row.get("grade", ""))
|
| pdf_url = row.get("PDF URL", row.get("pdf_url", ""))
|
|
|
|
|
| if isinstance(authors_raw, list):
|
| surnames = [a.split()[-1] for a in authors_raw[:3] if a]
|
| authors_display = ", ".join(authors_raw[:3])
|
| elif isinstance(authors_raw, str) and authors_raw:
|
| parts = [a.strip() for a in authors_raw.split(",")]
|
| surnames = [p.split()[-1] for p in parts[:3] if p]
|
| authors_display = authors_raw
|
| else:
|
| surnames = []
|
| authors_display = ""
|
|
|
|
|
| for s in surnames:
|
| key = f"{s.lower()}_{year}"
|
| if key not in index:
|
| index[key] = {
|
| "title": title, "authors": authors_display, "year": year,
|
| "doi": doi, "source": source, "grade": grade, "pdf_url": pdf_url,
|
| }
|
|
|
| if len(surnames) >= 2:
|
| combined = "_".join(s.lower() for s in surnames[:2]) + f"_{year}"
|
| index[combined] = {
|
| "title": title, "authors": authors_display, "year": year,
|
| "doi": doi, "source": source, "grade": grade, "pdf_url": pdf_url,
|
| }
|
|
|
| return index
|
|
|
|
|
| def _latex_to_html(text):
|
| """Convert common LaTeX commands to HTML for browser rendering."""
|
| if not text:
|
| return text
|
|
|
|
|
|
|
| text = re.sub(r'\\section\*?\{(.+?)\}', r'<h2>\1</h2>', text)
|
|
|
| text = re.sub(r'\\subsection\*?\{(.+?)\}', r'<h3>\1</h3>', text)
|
|
|
| text = re.sub(r'\\subsubsection\*?\{(.+?)\}', r'<h4>\1</h4>', text)
|
|
|
|
|
|
|
| text = re.sub(r'\\textbf\{(.+?)\}', r'<strong>\1</strong>', text)
|
|
|
| text = re.sub(r'\\textit\{(.+?)\}', r'<em>\1</em>', text)
|
|
|
| text = re.sub(r'\\emph\{(.+?)\}', r'<em>\1</em>', text)
|
|
|
| text = re.sub(r'\\underline\{(.+?)\}', r'<u>\1</u>', text)
|
|
|
|
|
|
|
| text = re.sub(r'(?<![\\\{])\{([^{}\n]+)\}(?!\})', r'*\1*', text)
|
|
|
|
|
|
|
| def fix_itemize_block(match):
|
| content = match.group(1).strip()
|
| lines = content.split('\n')
|
| fixed_lines = []
|
| for line in lines:
|
| line = line.strip()
|
| if not line:
|
| continue
|
| if line.startswith('-'):
|
| fixed_lines.append(f"\\item {line[1:].strip()}")
|
| elif not line.startswith('\\item'):
|
| fixed_lines.append(f"\\item {line}")
|
| else:
|
| fixed_lines.append(line)
|
| return "\\begin{itemize}\n" + "\n".join(fixed_lines) + "\n\\end{itemize}"
|
|
|
| text = re.sub(r'(?ims)^\s*itemize\s*$(.*?)(^\s*itemize\s*$)', fix_itemize_block, text)
|
|
|
|
|
| text = re.sub(r'\\begin\{itemize\}', '<ul>', text)
|
| text = re.sub(r'\\end\{itemize\}', '</ul>', text)
|
| text = re.sub(r'\\item\s*', '<li>', text)
|
|
|
|
|
| text = re.sub(r'(?im)^\s*itemize\s*$', '', text)
|
|
|
|
|
| text = text.replace('$$g/ml', 'µg/ml')
|
| text = text.replace('$$g', 'µg')
|
|
|
|
|
|
|
| text = re.sub(r'(?i)x\s*10\^\{([^}]+)\}', r'× 10<sup>\1</sup>', text)
|
| text = re.sub(r'(?i)x\s*10\^([0-9\-]+)', r'× 10<sup>\1</sup>', text)
|
|
|
|
|
|
|
| text = re.sub(r'([A-Z][a-z]?)_([0-9]+)', r'\1<sub>\2</sub>', text)
|
|
|
| text = re.sub(r'([A-Z][a-z]?)_\{([0-9]+)\}', r'\1<sub>\2</sub>', text)
|
|
|
|
|
| text = re.sub(r'([a-zA-Z]+)\^\{([0-9\-]+)\}', r'\1<sup>\2</sup>', text)
|
| text = re.sub(r'([a-zA-Z]+)\^([0-9\-]+)', r'\1<sup>\2</sup>', text)
|
|
|
|
|
| text = re.sub(r'\b([0-9]+)\s*[oO]C\b', r'\1 °C', text)
|
|
|
|
|
| text = text.replace('+/-', '±')
|
| text = text.replace('>=', '≥')
|
| text = text.replace('<=', '≤')
|
|
|
|
|
| text = re.sub(r'\bug/ml\b', 'µg/ml', text)
|
| text = re.sub(r'\bug/L\b', 'µg/L', text)
|
| text = re.sub(r'\bug\b', 'µg', text)
|
|
|
|
|
| text = re.sub(r'\\end\{enumerate\}', '</ol>', text)
|
| text = re.sub(r'\\item\s*', '<li>', text)
|
|
|
|
|
| text = text.replace(r'\%', '%')
|
| text = text.replace(r'\&', '&')
|
| text = text.replace(r'\#', '#')
|
| text = text.replace(r'\_', '_')
|
| text = text.replace(r'\$', '$')
|
|
|
|
|
| text = re.sub(r'\\begin\{document\}', '', text)
|
| text = re.sub(r'\\end\{document\}', '', text)
|
| text = re.sub(r'\\begin\{abstract\}', '', text)
|
| text = re.sub(r'\\end\{abstract\}', '', text)
|
| text = re.sub(r'\\maketitle', '', text)
|
| text = re.sub(r'\\documentclass\{[^}]*\}', '', text)
|
| text = re.sub(r'\\usepackage\{[^}]*\}', '', text)
|
| text = re.sub(r'\\title\{[^}]*\}', '', text)
|
| text = re.sub(r'\\author\{[^}]*\}', '', text)
|
| text = re.sub(r'\\date\{[^}]*\}', '', text)
|
|
|
|
|
| text = re.sub(r'\\cite\{([^}]+)\}', r'[\1]', text)
|
|
|
|
|
| text = re.sub(r'\n{2,}', '</p><p>', text)
|
|
|
|
|
|
|
| text = re.sub(r'\\(?:noindent|newpage|clearpage|vspace\{[^}]*\}|hspace\{[^}]*\}|par)\b', '', text)
|
|
|
| return text
|
|
|
|
|
| def _make_citations_interactive(report_md, docs_df):
|
| """Convert LaTeX/Markdown report to HTML with clickable [[n]] citations and MathJax math rendering."""
|
| import markdown as md_lib
|
| import json as _json
|
|
|
| if not report_md:
|
| return '<div style="color:#9ca3af; padding:20px;">Haz clic en el botón para ver el progreso en tiempo real...</div>'
|
|
|
|
|
| docs_index = _build_docs_index(docs_df)
|
|
|
|
|
| processed = _latex_to_html(report_md)
|
|
|
|
|
| try:
|
| html_body = md_lib.markdown(
|
| processed,
|
| extensions=['tables', 'fenced_code', 'nl2br'],
|
| )
|
| except Exception:
|
| html_body = processed.replace("\n\n", "</p><p>").replace("\n", "<br>")
|
| html_body = f"<p>{html_body}</p>"
|
|
|
| cite_id_counter = [0]
|
|
|
|
|
| bib_pattern = re.compile(r'(?:\[\[(\d+)\]\]\s*)?\{\{BIB:([\w\.\-/]+)\}\}')
|
| def replace_bib(match):
|
| idx_str = match.group(1)
|
| bib_id = match.group(2)
|
|
|
|
|
| if idx_str and docs_df is not None and not docs_df.empty:
|
| try:
|
| idx = int(idx_str) - 1
|
| if 0 <= idx < len(docs_df):
|
| row = docs_df.iloc[idx]
|
| autores = str(row.get("Autores", ""))
|
| año = str(row.get("Año", ""))
|
|
|
| parts = [a.strip() for a in autores.split(",")]
|
| surnames = [p.split()[-1] for p in parts if p and "..." not in p]
|
|
|
| if len(surnames) == 1:
|
| cite_text = f"[{idx+1}]"
|
| elif len(surnames) == 2:
|
| cite_text = f"[{idx+1}]"
|
| elif len(surnames) > 2:
|
| cite_text = f"[{idx+1}]"
|
| else:
|
| cite_text = f"[{idx+1}]"
|
|
|
|
|
| if len(surnames) >= 1:
|
| if len(surnames) == 1:
|
| tooltip = f"{surnames[0]} ({año})"
|
| elif len(surnames) == 2:
|
| tooltip = f"{surnames[0]} y {surnames[1]} ({año})"
|
| else:
|
| tooltip = f"{surnames[0]} et al. ({año})"
|
| else:
|
| tooltip = f"Fuente {idx+1} ({año})"
|
|
|
| cite_id_counter[0] += 1
|
| cid = cite_id_counter[0]
|
|
|
| import math
|
| import base64
|
| found = {k: ("" if (isinstance(v, float) and math.isnan(v)) else v) for k, v in row.to_dict().items()}
|
| data_json = _json.dumps(found, ensure_ascii=False)
|
| data_b64 = base64.b64encode(data_json.encode('utf-8')).decode('utf-8')
|
| return f'<span class="cite-link" data-cite-b64="{data_b64}" onclick="showCiteCard(this, {cid})" id="cite_{cid}" title="{tooltip}">{cite_text}</span>'
|
| except Exception:
|
| pass
|
|
|
|
|
| if idx_str:
|
| return f'<sup class="cite-inline">[{idx_str}]</sup>'
|
| return ""
|
|
|
| html_body = bib_pattern.sub(replace_bib, html_body)
|
|
|
|
|
| bare_bracket_pattern = re.compile(r'\[\[(\d+)\]\]')
|
| def replace_bare_bracket(match):
|
| idx_str = match.group(1)
|
| if docs_df is not None and not docs_df.empty:
|
| try:
|
| idx = int(idx_str) - 1
|
| if 0 <= idx < len(docs_df):
|
| row = docs_df.iloc[idx]
|
| autores = str(row.get("Autores", ""))
|
| año = str(row.get("Año", ""))
|
| parts = [a.strip() for a in autores.split(",")]
|
| surnames = [p.split()[-1] for p in parts if p and "..." not in p]
|
|
|
| if len(surnames) >= 1:
|
| if len(surnames) == 1:
|
| tooltip = f"{surnames[0]} ({año})"
|
| elif len(surnames) == 2:
|
| tooltip = f"{surnames[0]} y {surnames[1]} ({año})"
|
| else:
|
| tooltip = f"{surnames[0]} et al. ({año})"
|
| else:
|
| tooltip = f"Fuente {idx+1}"
|
|
|
| cite_id_counter[0] += 1
|
| cid = cite_id_counter[0]
|
| import math
|
| import base64
|
| found = {k: ("" if (isinstance(v, float) and math.isnan(v)) else v) for k, v in row.to_dict().items()}
|
| data_json = _json.dumps(found, ensure_ascii=False)
|
| data_b64 = base64.b64encode(data_json.encode('utf-8')).decode('utf-8')
|
| return f'<span class="cite-link" data-cite-b64="{data_b64}" onclick="showCiteCard(this, {cid})" id="cite_{cid}" title="{tooltip}">[{idx_str}]</span>'
|
| except Exception:
|
| pass
|
| return f'<sup>[{idx_str}]</sup>'
|
|
|
| html_body = bare_bracket_pattern.sub(replace_bare_bracket, html_body)
|
|
|
|
|
| citation_pattern = re.compile(
|
| r'\(([A-ZÁÉÍÓÚÑ][a-záéíóúñ]+(?:\s*(?:&|&|y|et\s+al\.?|,\s*[A-ZÁÉÍÓÚÑ][a-záéíóúñ]+))*)\s*,\s*(\d{4}|s\.f\.)\)'
|
| )
|
|
|
| def replace_citation(match):
|
| full_match = match.group(0)
|
| authors_part = match.group(1)
|
| year_part = match.group(2)
|
|
|
| author_names = re.split(r'\s*(?:&|&|y|,)\s*', authors_part)
|
| author_names = [a.strip().replace("et al.", "").strip() for a in author_names if a.strip()]
|
|
|
| found = None
|
| for a in author_names:
|
| surname = a.split()[-1].lower() if a else ""
|
| key = f"{surname}_{year_part}"
|
| if key in docs_index:
|
| found = docs_index[key]
|
| break
|
|
|
| if not found and len(author_names) >= 2:
|
| combined = "_".join(a.split()[-1].lower() for a in author_names[:2]) + f"_{year_part}"
|
| if combined in docs_index:
|
| found = docs_index[combined]
|
|
|
| if not found:
|
| return f'<span class="cite-inline">{full_match}</span>'
|
|
|
| cite_id_counter[0] += 1
|
| cid = cite_id_counter[0]
|
|
|
| import math
|
| import base64
|
| found_clean = {k: ("" if (isinstance(v, float) and math.isnan(v)) else v) for k, v in found.items()}
|
| data_json = _json.dumps(found_clean, ensure_ascii=False)
|
| data_b64 = base64.b64encode(data_json.encode('utf-8')).decode('utf-8')
|
| return f'<span class="cite-link" data-cite-b64="{data_b64}" onclick="showCiteCard(this, {cid})" id="cite_{cid}">{full_match}</span>'
|
|
|
| html_body = citation_pattern.sub(replace_citation, html_body)
|
|
|
|
|
| floating_card_js = FLOATING_CARD_JS
|
|
|
| return f'''<div class="report-interactive" style="
|
| font-family:'Inter',sans-serif; font-size:14px; line-height:1.75;
|
| color:var(--text, #e5e7eb); max-height:700px; overflow-y:auto; padding:4px 8px 4px 4px;
|
| ">
|
| <style>
|
| .report-interactive h1 {{ font-size:1.5rem; font-weight:700; margin:1.2em 0 0.6em; color:#f3f4f6; border-bottom:1px solid rgba(255,255,255,0.08); padding-bottom:8px; }}
|
| .report-interactive h2 {{ font-size:1.25rem; font-weight:600; margin:1em 0 0.5em; color:#e5e7eb; }}
|
| .report-interactive h3 {{ font-size:1.1rem; font-weight:600; margin:0.8em 0 0.4em; color:#d1d5db; }}
|
| .report-interactive h4 {{ font-size:1rem; font-weight:500; margin:0.6em 0 0.3em; color:#c084fc; }}
|
| .report-interactive p {{ margin:0.5em 0; text-align:justify; font-size: 15px; }}
|
| .report-interactive hr {{ border:none; border-top:1px solid rgba(255,255,255,0.06); margin:1.5em 0; }}
|
| .report-interactive em {{ color:#c084fc; font-style: italic; }}
|
| .report-interactive strong {{ color:#f3f4f6; }}
|
| .report-interactive a {{ color:#818cf8; text-decoration:underline; }}
|
| .report-interactive ul, .report-interactive ol {{ padding-left:1.5em; margin:0.5em 0; font-size: 15px; }}
|
| .report-interactive li {{ margin:0.3em 0; }}
|
| .report-interactive blockquote {{ margin:1em 0; padding:8px 16px; color:#9ca3af; font-style: italic; border-left: 3px solid rgba(255,255,255,0.2); }}
|
| .cite-link {{
|
| color:#a78bfa; cursor:pointer; font-weight:600;
|
| border-bottom:1px dashed rgba(167,139,250,0.4);
|
| transition:all 0.15s ease; padding:0 2px; border-radius:2px;
|
| font-size:0.85em;
|
| }}
|
| .cite-link:hover {{
|
| background:rgba(139,92,246,0.15); color:#c4b5fd;
|
| border-bottom-color:rgba(167,139,250,0.7);
|
| box-shadow:0 0 8px rgba(139,92,246,0.2);
|
| }}
|
| .cite-inline {{
|
| color:#9ca3af; font-style:italic; font-size:0.85em;
|
| }}
|
| /* MathJax rendered equations */
|
| .MathJax {{ font-size:1.05em !important; }}
|
| </style>
|
| {html_body}
|
| {floating_card_js}
|
| </div>'''
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| async def research_handler(
|
| query, provider, search_model, synthesis_model, translation_model,
|
| profile, depth, iterations, include_validation, sources,
|
| enable_dme=True, synthesis_strategy="auto",
|
| year_start="", year_end="", university="",
|
| infinite_output=True, max_continuation=5,
|
| grade_mode="original", geo_context="Automático" |
| ):
|
| import pandas as pd
|
|
|
| empty_df = pd.DataFrame(columns=["Título", "Autores", "Año", "DOI", "Fuente", "GRADE", "PDF URL"])
|
| ref_md = "_Sin referencias disponibles aún..._"
|
| stats_html = _build_stats_html("", empty_df)
|
|
|
| if not query or not query.strip():
|
| gr.Warning("Ingrese un tema de investigación")
|
| yield _build_status_html("error", "Sin consulta"), _build_progress_html(-1), \
|
| "**Error:** Ingrese un tema de investigación.", empty_df, \
|
| "", ref_md, stats_html, "" |
| return
|
|
|
| api_key = os.getenv(PROVIDERS.get(provider, {}).get("env_key", ""), "")
|
| if not api_key:
|
| env_key = PROVIDERS.get(provider, {}).get("env_key", "?")
|
| gr.Warning(f"No hay API key para {provider}. Configure {env_key} en .env")
|
| yield _build_status_html("error", "API key faltante"), _build_progress_html(-1), \
|
| f"**Error:** No hay API key para {provider}. Configure `{env_key}` en .env", \
|
| empty_df, "", ref_md, stats_html, ""
|
| return
|
|
|
|
|
| from backend.database.models import SessionLocal, User, Project, ResearchJob
|
| db_job = None
|
| db = SessionLocal()
|
| user = db.query(User).filter(User.username == "admin").first()
|
| if user:
|
| project = Project(title=f"Investigación: {query[:50]}", owner_id=user.id)
|
| db.add(project)
|
| db.commit()
|
| db_job = ResearchJob(project_id=project.id, query=query, status="running")
|
| db.add(db_job)
|
| db.commit()
|
| db.refresh(db_job)
|
| db.close()
|
|
|
| search_sources = sources if sources else ["all"]
|
| pipeline = ResearchPipeline(
|
| provider=provider, search_model=search_model,
|
| synthesis_model=synthesis_model, translation_model=translation_model,
|
| api_key=api_key,
|
| )
|
|
|
| global _active_pipeline
|
| _active_pipeline = pipeline
|
|
|
| accumulated_report = ""
|
| accumulated_df = empty_df
|
| current_phase = -1
|
|
|
| try:
|
| async for report_md, docs_df in pipeline.run(
|
| query=query.strip(), sources=search_sources, profile=profile,
|
| depth=int(depth), iterations=int(iterations),
|
| include_validation=include_validation,
|
| enable_dme=enable_dme, synthesis_strategy=synthesis_strategy,
|
| year_start=year_start or None, year_end=year_end or None,
|
| university=university or None, grade_mode=grade_mode,
|
| geo_context=geo_context,
|
| infinite_output=infinite_output,
|
| max_continuation_passes=int(max_continuation),
|
| ):
|
| accumulated_report = report_md
|
| if docs_df is not None and not docs_df.empty:
|
| accumulated_df = docs_df
|
|
|
| detected_phase = _detect_phase(report_md)
|
| current_phase = detected_phase
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| last_key = list(sections_map.keys())[-1] if sections_map else ""
|
| extra = f"{len(accumulated_df)} docs" if len(accumulated_df) else ""
|
| if current_phase == 5 and last_key:
|
| extra = f"Redactando: {last_key}"
|
|
|
| progress_html = _build_progress_html(current_phase, extra)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
| sections_content = _build_section_cards_html(sections_map)
|
|
|
| paused_label = " ⏸️" if pipeline.is_paused else ""
|
| yield ( |
| _build_status_html("running", f"Fase {current_phase}{paused_label}"), |
| progress_html, _make_citations_interactive(accumulated_report, accumulated_df), accumulated_df, |
| sections_content, ref_md, stats_html, accumulated_report, |
| ) |
|
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map, is_done=True)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
|
|
| yield ( |
| _build_status_html("done", f"{len(accumulated_df)} docs | {len(sections_map)} secciones"), |
| _build_progress_html(7), _make_citations_interactive(accumulated_report, accumulated_df), accumulated_df, |
| sections_content, ref_md, stats_html, accumulated_report, |
| ) |
|
|
| if db_job:
|
| from datetime import datetime
|
| db = SessionLocal()
|
| job = db.query(ResearchJob).get(db_job.id)
|
| if job:
|
| job.status = "completed"
|
| job.report_md = accumulated_report
|
| job.completed_at = datetime.utcnow()
|
| db.commit()
|
| db.close()
|
|
|
| except (StopAsyncIteration, asyncio.CancelledError):
|
|
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map, is_done=True)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
| yield (
|
| _build_status_html("error", "⛔ Detenido por el usuario"),
|
| _build_progress_html(current_phase, "Detenido"),
|
| _make_citations_interactive(accumulated_report + "\n\n---\n⛔ **Pipeline detenido por el usuario**", accumulated_df),
|
| accumulated_df, sections_content, ref_md, stats_html,
|
| accumulated_report
|
| )
|
| except Exception as e:
|
| if db_job:
|
| db = SessionLocal()
|
| job = db.query(ResearchJob).get(db_job.id)
|
| if job:
|
| job.status = "error"
|
| db.commit()
|
| db.close()
|
| yield (
|
| _build_status_html("error", str(e)[:60]),
|
| _build_progress_html(current_phase),
|
| _make_citations_interactive(f"**Error:** {str(e)}", accumulated_df), accumulated_df, "", ref_md, stats_html,
|
| accumulated_report
|
| )
|
| finally:
|
| _active_pipeline = None
|
| await pipeline.close()
|
|
|
|
|
|
|
|
|
|
|
|
|
| async def super_research_handler(
|
| query, provider, search_model, synthesis_model, translation_model,
|
| profile, depth, rounds, include_validation, sources,
|
| enable_dme=True, synthesis_strategy="auto",
|
| year_start="", year_end="", university="",
|
| infinite_output=True, max_continuation=5,
|
| grade_mode="original", geo_context="Automático" |
| ):
|
| import pandas as pd
|
|
|
| empty_df = pd.DataFrame(columns=["Título", "Autores", "Año", "DOI", "Fuente", "GRADE", "PDF URL"])
|
| ref_md = "_Sin referencias disponibles aún..._"
|
| stats_html = _build_stats_html("", empty_df)
|
|
|
| if not query or not query.strip():
|
| gr.Warning("Ingrese un tema de investigación")
|
| yield _build_status_html("error", "Sin consulta"), _build_progress_html(-1), \
|
| "**Error:** Ingrese un tema de investigación.", empty_df, \
|
| "", ref_md, stats_html, "" |
| return
|
|
|
| api_key = os.getenv(PROVIDERS.get(provider, {}).get("env_key", ""), "")
|
| if not api_key:
|
| env_key = PROVIDERS.get(provider, {}).get("env_key", "?")
|
| gr.Warning(f"No hay API key para {provider}. Configure {env_key} en .env")
|
| yield _build_status_html("error", "API key faltante"), _build_progress_html(-1), \
|
| f"**Error:** No hay API key para {provider}. Configure `{env_key}` en .env", \
|
| empty_df, "", ref_md, stats_html, "" |
| return
|
|
|
| from backend.database.models import SessionLocal, User, Project, ResearchJob
|
| db_job = None
|
| db = SessionLocal()
|
| user = db.query(User).filter(User.username == "admin").first()
|
| if user:
|
| project = Project(title=f"Super Inv: {query[:50]}", owner_id=user.id)
|
| db.add(project)
|
| db.commit()
|
| db_job = ResearchJob(project_id=project.id, query=query, status="running")
|
| db.add(db_job)
|
| db.commit()
|
| db.refresh(db_job)
|
| db.close()
|
|
|
| search_sources = sources if sources else ["all"]
|
| pipeline = ResearchPipeline(
|
| provider=provider, search_model=search_model,
|
| synthesis_model=synthesis_model, translation_model=translation_model,
|
| api_key=api_key,
|
| )
|
|
|
| global _active_pipeline
|
| _active_pipeline = pipeline
|
|
|
| accumulated_report = ""
|
| accumulated_df = empty_df
|
| current_phase = -1
|
|
|
| try:
|
| async for report_md, docs_df in pipeline.run(
|
| query=query.strip(), sources=search_sources, profile=profile,
|
| depth=int(depth), iterations=int(rounds),
|
| include_validation=include_validation,
|
| enable_dme=enable_dme, synthesis_strategy=synthesis_strategy,
|
| year_start=year_start or None, year_end=year_end or None,
|
| university=university or None, grade_mode=grade_mode,
|
| geo_context=geo_context,
|
| infinite_output=infinite_output,
|
| max_continuation_passes=int(max_continuation),
|
| ):
|
| accumulated_report = report_md
|
| if docs_df is not None and not docs_df.empty:
|
| accumulated_df = docs_df
|
|
|
| detected_phase = _detect_phase(report_md)
|
| current_phase = detected_phase
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| last_key = list(sections_map.keys())[-1] if sections_map else ""
|
| extra = f"{len(accumulated_df)} docs" if len(accumulated_df) else ""
|
| if current_phase == 5 and last_key:
|
| extra = f"Redactando: {last_key}"
|
|
|
| progress_html = _build_progress_html(current_phase, extra)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
| sections_content = _build_section_cards_html(sections_map)
|
|
|
| paused_label = " ⏸️" if pipeline.is_paused else ""
|
| yield (
|
| _build_status_html("running", f"Fase {current_phase}{paused_label}"),
|
| progress_html, _make_citations_interactive(accumulated_report, accumulated_df), accumulated_df,
|
| sections_content, ref_md, stats_html, accumulated_report |
| )
|
|
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map, is_done=True)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
|
|
| yield (
|
| _build_status_html("done", f"{len(accumulated_df)} docs | {len(sections_map)} secciones"),
|
| _build_progress_html(7), _make_citations_interactive(accumulated_report, accumulated_df), accumulated_df,
|
| sections_content, ref_md, stats_html, accumulated_report |
| )
|
|
|
| if db_job:
|
| from datetime import datetime
|
| db = SessionLocal()
|
| job = db.query(ResearchJob).get(db_job.id)
|
| if job:
|
| job.status = "completed"
|
| job.report_md = accumulated_report
|
| job.completed_at = datetime.utcnow()
|
| db.commit()
|
| db.close()
|
|
|
| except (StopAsyncIteration, asyncio.CancelledError):
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map, is_done=True)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, accumulated_df)
|
| yield (
|
| _build_status_html("error", "⛔ Detenido por el usuario"),
|
| _build_progress_html(current_phase, "Detenido"),
|
| _make_citations_interactive(accumulated_report + "\n\n---\n⛔ **Pipeline detenido por el usuario**", accumulated_df),
|
| accumulated_df, sections_content, ref_md, stats_html,
|
| accumulated_report
|
| )
|
| except Exception as e:
|
| if db_job:
|
| db = SessionLocal()
|
| job = db.query(ResearchJob).get(db_job.id)
|
| if job:
|
| job.status = "error"
|
| db.commit()
|
| db.close()
|
| yield (
|
| _build_status_html("error", str(e)[:60]),
|
| _build_progress_html(current_phase),
|
| _make_citations_interactive(f"**Error:** {str(e)}", accumulated_df), accumulated_df, "", ref_md, stats_html,
|
| accumulated_report
|
| )
|
| finally:
|
| _active_pipeline = None
|
| await pipeline.close()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| async def synthesis_handler(
|
| query, docs_text, provider, search_model, synthesis_model,
|
| translation_model, profile, include_validation,
|
| enable_dme=True, synthesis_strategy="auto",
|
| grade_mode="original", geo_context="Automático", |
| ):
|
| import pandas as pd
|
|
|
| empty_df = pd.DataFrame(columns=["Título", "Autores", "Año", "DOI", "Fuente", "GRADE", "PDF URL"])
|
| ref_md = "_Sin referencias disponibles aún..._"
|
| stats_html = _build_stats_html("", empty_df)
|
|
|
| if not query or not query.strip():
|
| gr.Warning("Ingrese un tema/título")
|
| yield _build_status_html("error", "Sin consulta"), _build_progress_html(-1), \
|
| "**Error:** Ingrese un tema o título para la síntesis.", empty_df, \
|
| "", ref_md, stats_html, ""
|
| return
|
|
|
| if not docs_text or not docs_text.strip():
|
| gr.Warning("Ingrese al menos 5 documentos")
|
| yield _build_status_html("error", "Sin documentos"), _build_progress_html(-1), \
|
| "**Error:** Pegue la lista de documentos en el campo de texto.", empty_df, \
|
| "", ref_md, stats_html, ""
|
| return
|
|
|
| api_key = os.getenv(PROVIDERS.get(provider, {}).get("env_key", ""), "")
|
| if not api_key:
|
| env_key = PROVIDERS.get(provider, {}).get("env_key", "?")
|
| gr.Warning(f"No hay API key para {provider}. Configure {env_key} en .env")
|
| yield _build_status_html("error", "API key faltante"), _build_progress_html(-1), \
|
| f"**Error:** No hay API key para {provider}. Configure `{env_key}` en .env", \
|
| empty_df, "", ref_md, stats_html, ""
|
| return
|
|
|
| pipeline = ResearchPipeline(
|
| provider=provider, search_model=search_model,
|
| synthesis_model=synthesis_model, translation_model=translation_model,
|
| api_key=api_key,
|
| )
|
|
|
| accumulated_report = ""
|
| current_phase = 0
|
|
|
| try:
|
| async for report_md, docs_df in pipeline.run(
|
| query=query.strip(), sources=[], profile=profile,
|
| iterations=0, include_validation=include_validation,
|
| docs_text=docs_text, enable_dme=enable_dme,
|
| synthesis_strategy=synthesis_strategy,
|
| grade_mode=grade_mode, geo_context=geo_context,
|
| ):
|
| accumulated_report = report_md
|
| detected_phase = _detect_phase(report_md)
|
| if detected_phase != current_phase:
|
| current_phase = detected_phase
|
|
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, empty_df)
|
|
|
| yield (
|
| _build_status_html("running", "Sintetizando"),
|
| _build_progress_html(current_phase), accumulated_report, empty_df,
|
| sections_content, ref_md, stats_html, accumulated_report
|
| )
|
|
|
| sections_map = _parse_sections_from_report(accumulated_report)
|
| sections_content = _build_section_cards_html(sections_map, is_done=True)
|
| ref_md = _build_references_html(docs_df, accumulated_report)
|
| stats_html = _build_stats_html(accumulated_report, empty_df)
|
|
|
| yield (
|
| _build_status_html("done", "Síntesis completada"),
|
| _build_progress_html(7), accumulated_report, empty_df,
|
| sections_content, ref_md, stats_html, accumulated_report
|
| )
|
|
|
| except Exception as e:
|
| yield (
|
| _build_status_html("error", str(e)[:60]),
|
| _build_progress_html(current_phase),
|
| f"**Error:** {str(e)}", empty_df, "", ref_md, stats_html, |
| )
|
| finally:
|
| await pipeline.close()
|
|
|
|
|
|
|
|
|
|
|
|
|
| def _build_research_panel(prefix, title, subtitle, btn_label, handler_fn, is_super=False):
|
| """Build a unified premium research panel for Research/Super/Synthesis tabs"""
|
|
|
|
|
| gr.HTML(f'''
|
| <div style="
|
| display:flex; justify-content:space-between; align-items:center;
|
| padding:14px 20px; margin-bottom:12px;
|
| background:linear-gradient(135deg, rgba(139,92,246,0.08), rgba(99,102,241,0.04));
|
| border:1px solid rgba(139,92,246,0.2); border-radius:14px;
|
| ">
|
| <div style="display:flex; align-items:center; gap:12px;">
|
| <div style="
|
| width:40px; height:40px; border-radius:12px;
|
| background:linear-gradient(135deg, #8b5cf6, #6366f1);
|
| display:flex; align-items:center; justify-content:center;
|
| font-size:20px; box-shadow:0 4px 15px rgba(139,92,246,0.3);
|
| ">{"🚀" if is_super else "🔬"}</div>
|
| <div>
|
| <div style="font-size:16px; font-weight:700; color:var(--text, #fff);">
|
| {title}
|
| </div>
|
| <div style="font-size:11px; color:var(--text-muted, #9ca3af);">
|
| {subtitle}
|
| </div>
|
| </div>
|
| </div>
|
| <div style="display:flex; gap:8px;">
|
| <span style="
|
| display:inline-flex; align-items:center; gap:5px;
|
| padding:4px 12px; border-radius:20px; font-size:11px; font-weight:600;
|
| background:rgba(139,92,246,0.1); border:1px solid rgba(139,92,246,0.3); color:#8b5cf6;
|
| ">Pipeline v2.0</span>
|
| </div>
|
| </div>
|
| ''')
|
|
|
| with gr.Row():
|
|
|
| with gr.Column(scale=2):
|
| status = gr.HTML(_build_status_html("idle"))
|
| progress = gr.HTML(_build_progress_html(-1, "Esperando consulta..."))
|
|
|
| gr.HTML('''<div class="section-header">💬 Consulta de investigación</div>''')
|
| query = gr.Textbox(
|
| label="",
|
| placeholder="Ej: Impacto de la IA en la educación superior en Perú",
|
| lines=3, show_label=False,
|
| elem_classes=["glass-input-wrapper"]
|
| )
|
|
|
| with gr.Row():
|
| prov = gr.Dropdown(
|
| choices=list(PROVIDERS.keys()), value="mistral",
|
| label="⚡ Proveedor IA", scale=1,
|
| )
|
|
|
| with gr.Accordion("🤖 Modelos por Rol", open=False):
|
| search_m = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="🔍 Búsqueda",
|
| )
|
| synth_m = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="📝 Síntesis",
|
| )
|
| trans_m = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="🌐 Traducción",
|
| )
|
| prov.change(
|
| fn=update_models, inputs=[prov],
|
| outputs=[search_m, synth_m, trans_m],
|
| )
|
|
|
| with gr.Accordion("📚 Parámetros de Búsqueda", open=False):
|
| src = gr.CheckboxGroup(
|
| choices=ALL_SOURCES, value=ALL_SOURCES, label="Fuentes", show_label=False,
|
| )
|
| gr.HTML('''
|
| <div style="display:flex; gap:6px; flex-wrap:wrap; margin:6px 0;">
|
| <span style="font-size:10px; padding:2px 8px; border-radius:6px; background:rgba(59,130,246,0.08); border:1px solid rgba(59,130,246,0.2); color:#3b82f6;">all = todas</span>
|
| <span style="font-size:10px; padding:2px 8px; border-radius:6px; background:rgba(34,197,94,0.08); border:1px solid rgba(34,197,94,0.2); color:#22c55e;">latam = Latinoamérica</span>
|
| <span style="font-size:10px; padding:2px 8px; border-radius:6px; background:rgba(168,85,247,0.08); border:1px solid rgba(168,85,247,0.2); color:#a855f7;">global = PubMed+ArXiv+OpenAlex</span>
|
| </div>
|
| ''')
|
| with gr.Row():
|
| prof = gr.Dropdown(
|
| choices=list(AGENT_PROFILES.keys()),
|
| value="auto", label="🎭 Perfil",
|
| )
|
| dep = gr.Slider(minimum=1, maximum=5, value=3, step=1, label="📏 Profundidad")
|
|
|
| if is_super:
|
| iters = gr.Slider(minimum=2, maximum=5, value=3, step=1, label="🔄 Rondas")
|
| else:
|
| iters = gr.Slider(minimum=1, maximum=5, value=1, step=1, label="🔄 Iteraciones")
|
|
|
| with gr.Accordion("🔧 Opciones Avanzadas", open=False):
|
| geo = gr.Textbox(value="Automático", label="📍 Contexto Geográfico (País/Universidad)", placeholder="Ej: Perú, Universidad Nacional del Santa")
|
| val = gr.Checkbox(value=True, label="🔬 Validación de citas (ARA+)")
|
| dme = gr.Checkbox(value=True, label="🔧 DME: Reparación + Enriquecimiento")
|
| strat = gr.Radio(
|
| choices=["lineal", "jerárquica", "auto"],
|
| value="jerárquica", label="📐 Estrategia de Síntesis",
|
| )
|
| grade_mode = gr.Radio(
|
| choices=["original", "keywords", "llm", "oxford", "hybrid"], |
| value="original", label="📊 Algoritmo GRADE", |
| info="original: Beta SX | keywords: Rápido | llm: IA Preciso | oxford: CEBM | hybrid: Mixto", |
| )
|
| with gr.Row():
|
| yr_s = gr.Textbox(label="📅 Año inicio", placeholder="2020")
|
| yr_e = gr.Textbox(label="📅 Año fin", placeholder="2025")
|
| uni = gr.Textbox(label="🏛️ Universidad", placeholder="Ej: UNMSM")
|
| inf_out = gr.Checkbox(value=True, label="♾️ Output Infinito")
|
| max_cont = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="🔁 Max Continuaciones")
|
|
|
| btn = gr.Button(
|
| btn_label, variant="primary", size="lg",
|
| elem_classes=["ejecutar-btn"]
|
| )
|
|
|
|
|
| with gr.Row():
|
| pause_btn = gr.Button(
|
| "⏸️ Pausar", size="sm", variant="secondary",
|
| elem_classes=["control-btn-pause"]
|
| )
|
| resume_btn = gr.Button(
|
| "▶️ Reanudar", size="sm", variant="secondary",
|
| elem_classes=["control-btn-resume"]
|
| )
|
| stop_btn = gr.Button(
|
| "⛔ Detener", size="sm", variant="stop",
|
| elem_classes=["control-btn-stop"]
|
| )
|
|
|
|
|
| with gr.Column(scale=3):
|
| with gr.Tabs():
|
| with gr.TabItem("📄 Informe"):
|
| report = gr.HTML(_make_citations_interactive("", None))
|
| with gr.TabItem("📚 Referencias"):
|
| refs = gr.HTML("_Las referencias aparecerán durante la ejecución..._")
|
| with gr.TabItem("📑 Secciones"):
|
| sections = gr.HTML(_build_section_cards_html({}))
|
| with gr.TabItem("📊 Estadísticas"):
|
| stats = gr.HTML(_build_stats_html("", None))
|
| with gr.TabItem("📋 Documentos"):
|
| docs = gr.Dataframe(
|
| headers=["Título", "Autores", "Año", "DOI", "Fuente", "GRADE", "PDF URL"],
|
| label="Documentos Encontrados", wrap=True,
|
| )
|
| with gr.TabItem("🌐 Grafo"):
|
| graph_btn = gr.Button("🌐 Generar Grafo de Relaciones", size="sm", elem_classes=["ejecutar-btn"])
|
| graph_html = gr.HTML('''<div style="text-align:center; padding:30px; color:#6b7280;">
|
| <div style="font-size:36px; margin-bottom:8px;">🌐</div>
|
| <div style="font-size:13px;">Haz clic en el botón para generar el grafo.</div>
|
| </div>''')
|
| graph_btn.click(fn=_generate_graph_from_df, inputs=[docs], outputs=[graph_html])
|
|
|
| report_md_state = gr.State("")
|
|
|
| with gr.TabItem("📥 Exportar"):
|
| gr.HTML('''<div style="padding:12px; background:rgba(99,102,241,0.06); border:1px solid rgba(99,102,241,0.2); border-radius:12px; margin-bottom:12px;">
|
| <div style="font-size:14px; font-weight:600; color:#818cf8; margin-bottom:4px;">📥 Exportar Resultados</div>
|
| <div style="font-size:11px; color:#9ca3af;">Descarga el informe y los documentos en distintos formatos.</div>
|
| </div>''')
|
| with gr.Row():
|
| export_md_btn = gr.Button("📄 Markdown (.md)", size="sm", variant="secondary")
|
| export_bib_btn = gr.Button("📚 BibTeX (.bib)", size="sm", variant="secondary")
|
| with gr.Row():
|
| export_docx_btn = gr.Button("📝 Word (.docx)", size="sm", variant="secondary")
|
| export_zip_btn = gr.Button("📦 ZIP (Workspace)", size="sm", variant="primary")
|
| export_file = gr.File(label="Archivo generado", visible=True)
|
|
|
| from backend.tools.export_utils import export_markdown, export_bibtex, export_zip, export_docx
|
|
|
| def _do_export_md(report_state, q):
|
| if not report_state: return gr.update(value=None)
|
| return export_markdown(report_state, q or "research")
|
|
|
| def _do_export_bib(docs_df, q):
|
| if docs_df is None or docs_df.empty: return gr.update(value=None)
|
| return export_bibtex(docs_df, q or "references")
|
|
|
| def _do_export_docx(report_state, q):
|
| if not report_state: return gr.update(value=None)
|
| path = export_docx(report_state, q or "research")
|
| return path if path else gr.update(value=None)
|
|
|
| def _do_export_zip(report_state, docs_df, q):
|
| if not report_state: return gr.update(value=None)
|
| import pandas as pd
|
| if docs_df is None:
|
| docs_df = pd.DataFrame()
|
| return export_zip(report_state, docs_df, q or "research")
|
|
|
| export_md_btn.click(fn=_do_export_md, inputs=[report_md_state, query], outputs=[export_file])
|
| export_bib_btn.click(fn=_do_export_bib, inputs=[docs, query], outputs=[export_file])
|
| export_docx_btn.click(fn=_do_export_docx, inputs=[report_md_state, query], outputs=[export_file])
|
| export_zip_btn.click(fn=_do_export_zip, inputs=[report_md_state, docs, query], outputs=[export_file])
|
|
|
|
|
| from modules.chat_tab import create_chat_tabs
|
| create_chat_tabs(report_md_state, docs, prov, synth_m)
|
|
|
|
|
| stop_btn.click(fn=_control_stop, outputs=[status])
|
| pause_btn.click(fn=_control_pause, outputs=[status])
|
| resume_btn.click(fn=_control_resume, outputs=[status])
|
|
|
|
|
| return (btn, query, prov, search_m, synth_m, trans_m, prof, dep, iters,
|
| val, src, dme, strat, yr_s, yr_e, uni, inf_out, max_cont, grade_mode, geo,
|
| status, progress, report, docs, sections, refs, stats, report_md_state)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| def create_research_tab():
|
| with gr.Tab("🔬 Research", id="research"):
|
| gr.HTML('''<style>
|
| @keyframes pulse { 0%,100%{opacity:1} 50%{opacity:.6} }
|
| @keyframes slideIn { from{opacity:0;transform:translateY(8px)} to{opacity:1;transform:translateY(0)} }
|
| @keyframes fadeIn { from{opacity:0} to{opacity:1} }
|
| </style>''')
|
|
|
| with gr.Tabs():
|
|
|
| with gr.TabItem("🔬 Research"):
|
| r = _build_research_panel(
|
| "r", "Research Pipeline",
|
| "Búsqueda iterativa + síntesis con IA en tiempo real",
|
| "🚀 Ejecutar Research", research_handler, is_super=False
|
| )
|
| r[0].click(
|
| fn=research_handler,
|
| inputs=list(r[1:20]),
|
| outputs=list(r[20:28]),
|
| )
|
|
|
|
|
| with gr.TabItem("🚀 Super Research"):
|
| s = _build_research_panel(
|
| "s", "Super Research Pipeline",
|
| "Investigación profunda multi-ronda con validación cruzada",
|
| "⚡ Ejecutar Super Research", super_research_handler, is_super=True
|
| )
|
| s[0].click(
|
| fn=super_research_handler,
|
| inputs=list(s[1:20]),
|
| outputs=list(s[20:28]),
|
| )
|
|
|
|
|
| with gr.TabItem("📝 Síntesis"):
|
| gr.HTML('''
|
| <div style="
|
| display:flex; justify-content:space-between; align-items:center;
|
| padding:14px 20px; margin-bottom:12px;
|
| background:linear-gradient(135deg, rgba(16,185,129,0.08), rgba(6,182,212,0.04));
|
| border:1px solid rgba(16,185,129,0.2); border-radius:14px;
|
| ">
|
| <div style="display:flex; align-items:center; gap:12px;">
|
| <div style="
|
| width:40px; height:40px; border-radius:12px;
|
| background:linear-gradient(135deg, #10b981, #06b6d4);
|
| display:flex; align-items:center; justify-content:center;
|
| font-size:20px; box-shadow:0 4px 15px rgba(16,185,129,0.3);
|
| ">📝</div>
|
| <div>
|
| <div style="font-size:16px; font-weight:700; color:var(--text, #fff);">
|
| Síntesis de Documentos
|
| </div>
|
| <div style="font-size:11px; color:var(--text-muted, #9ca3af);">
|
| Generar informe a partir de documentos proporcionados
|
| </div>
|
| </div>
|
| </div>
|
| </div>
|
| ''')
|
|
|
| with gr.Row():
|
| with gr.Column(scale=2):
|
| y_status = gr.HTML(_build_status_html("idle"))
|
| y_progress = gr.HTML(_build_progress_html(-1, "Esperando consulta..."))
|
|
|
| gr.HTML('''<div class="section-header">💬 Tema / Título</div>''')
|
| y_query = gr.Textbox(
|
| label="", show_label=False,
|
| placeholder="Ej: Marco teórico sobre gestión del conocimiento",
|
| lines=2, elem_classes=["glass-input-wrapper"]
|
| )
|
|
|
| gr.HTML('''<div class="section-header" style="margin-top:8px;">📄 Documentos</div>''')
|
| y_docs = gr.Textbox(
|
| label="", show_label=False,
|
| placeholder="[1] García (2023) - Gestión del conocimiento en Perú\n[2] Smith (2022) - Knowledge management systems\n[3] López (2024) - Bases de datos académicas",
|
| lines=8, elem_classes=["glass-input-wrapper"]
|
| )
|
|
|
| y_provider = gr.Dropdown(
|
| choices=list(PROVIDERS.keys()), value="mistral",
|
| label="⚡ Proveedor IA",
|
| )
|
|
|
| with gr.Accordion("🤖 Modelos por Rol", open=False):
|
| y_search_model = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="🔍 Búsqueda",
|
| )
|
| y_synthesis_model = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="📝 Síntesis",
|
| )
|
| y_translation_model = gr.Dropdown(
|
| choices=PROVIDERS["mistral"]["models"],
|
| value=DEFAULT_MODEL, label="🌐 Traducción",
|
| )
|
| y_provider.change(
|
| fn=update_models, inputs=[y_provider],
|
| outputs=[y_search_model, y_synthesis_model, y_translation_model],
|
| )
|
|
|
| with gr.Accordion("🔧 Opciones Avanzadas", open=False):
|
| with gr.Row():
|
| y_profile = gr.Dropdown(
|
| choices=list(AGENT_PROFILES.keys()),
|
| value="auto", label="🎭 Perfil",
|
| )
|
| y_validation = gr.Checkbox(value=True, label="🔬 Validación ARA+")
|
| y_geo = gr.Textbox(value="Automático", label="📍 Contexto Geográfico (País/Universidad)", placeholder="Ej: Perú, Universidad Nacional del Santa")
|
| y_enable_dme = gr.Checkbox(value=True, label="🔧 DME")
|
| y_synthesis_strategy = gr.Radio(
|
| choices=["lineal", "jerárquica", "auto"],
|
| value="jerárquica", label="📐 Estrategia",
|
| )
|
| y_grade_mode = gr.Radio(
|
| choices=["original", "keywords", "llm", "oxford", "hybrid"], |
| value="original", label="📊 Algoritmo GRADE", |
| )
|
|
|
| y_btn = gr.Button(
|
| "📝 Ejecutar Síntesis", variant="primary", size="lg",
|
| elem_classes=["ejecutar-btn"]
|
| )
|
|
|
| with gr.Column(scale=3):
|
| with gr.Tabs():
|
| with gr.TabItem("📄 Informe"):
|
| y_report = gr.HTML(_make_citations_interactive("", None))
|
| with gr.TabItem("📚 Referencias"):
|
| y_refs = gr.Markdown("_Las referencias aparecerán aquí..._")
|
| with gr.TabItem("📑 Secciones"):
|
| y_sections = gr.HTML(_build_section_cards_html({}))
|
| with gr.TabItem("📊 Estadísticas"):
|
| y_stats = gr.HTML(_build_stats_html("", None))
|
| with gr.TabItem("📋 Documentos"):
|
| y_docs_out = gr.Dataframe(
|
| headers=["Título", "Autores", "Año", "DOI", "Fuente", "GRADE", "PDF URL"],
|
| label="Documentos", wrap=True,
|
| )
|
|
|
| y_report_md_state = gr.State("") |
|
|
| y_btn.click( |
| fn=synthesis_handler,
|
| inputs=[
|
| y_query, y_docs, y_provider, y_search_model,
|
| y_synthesis_model, y_translation_model, y_profile,
|
| y_validation, y_enable_dme, y_synthesis_strategy,
|
| y_grade_mode, y_geo,
|
| ],
|
| outputs=[ |
| y_status, y_progress, y_report, y_docs_out, |
| y_sections, y_refs, y_stats, y_report_md_state, |
| ], |
| ) |
|
|
|
|
