| import os |
| import grad```as gr |
| import numpy as np |
| import torch |
| import asyn``` |
| import networkx``` nx |
| from transformers import```toModel, AutoTokenizer```om groq import```oq |
| from datetime import datetime |
| from enum import Enum |
| from dataclasses import dat```ass |
| from typing import```ct, List, Optional```ny |
| import plotly.express``` px |
| import matplotlib```plot as plt |
| from sklearn.```ifold import```NE |
| from sentence```ansformers import Sentence```nsformer |
|
|
| |
| NEXUS_VERSION = "1.0.0" |
| COLLECTIVE_INTELL```NCE_THRESHOLD = 0.78 |
| GROQ_MODEL```"llama-3.3-70b-versatile" |
| DISC_MODEL = SentenceTransformer```ll-MiniLM-L6-v2') |
| |
| # Tipos de personalidad DISC |
| class DISCTYPE```um): |
| DOMINANT =```" |
| INFL```TIAL = "I" |
| STEADY =```" |
| CONSCIENTIOUS = "C```@dataclass |
| class CognitiveProfile``` """Perfil```gnitivo basado``` DISC""" |
| primary: DIS```PE |
| secondary:```tional[DISCTYPE] = None |
| vector: np.nd```ay = None |
| traits: Dict[str, float] = None |
| |
| @dataclass |
| class CollectiveAgent: |
| """Agente aut贸n``` especializado``` |
| id: str |
| role: str |
| knowledge_base:```st[str] |
| last_activation:```tetime = field(default_factory=datetime.now```class EmergentSystem: |
| """Motor de inteligencia```lectiva""" |
| def __init__(self): |
| self.```nts = self._```t_agents() |
| self.knowledge_graph```nx.Graph() |
| self.cognitive_network```[] |
| self.g```_client = Groq```i_key=os.getenv("GROQ_API_KEY")) |
| |
| def _init_agents(self) -> Dict[str, CollectiveAgent]: |
| return { |
| "analyzer": Collective```nt("A1", "Analista", ["Pattern Recognition"]), |
| "```thesizer": Collective```nt("S1", "Sintetizador```["Cross-Domain Integration"]), |
| "innovator```CollectiveAgent("I1", "Innovador", ["Divergent Thinking"]), |
| "validator```CollectiveAgent("V1", "Validador",```Critical Analysis"]) |
| } |
| |
| def _```ate_knowledge_graph(self, concept:```r, connections:```st[str]): |
| self```owledge_graph.add_node(concept) |
| for connection in```nnections: |
| self.k```ledge_graph.add_edge(concept,```nnection) |
| |
| async```f collective_reason```(self, input_data```tr) -> Dict[str, Any]: |
| """Proceso```laborativo multi-agente""" |
| # Pro```amiento paralelo``` tasks =``` [ |
| self._agent_processing(agent, input_data) |
| for agent in self.agents.values() |
| ] |
| results```await asyncio.g```er(*tasks) |
| |
| # S铆ntesis emerg```e |
| collective_```ight = self```mergent_synthesis(results) |
| return { |
| "timestamp":```tetime.now().isoformat``` |
| "insights":```llective_insight, |
| "knowledge_graph": self```isualize_knowledge_graph() |
| } |
| |
| async def _agent_```cessing(self, agent: Collective```nt, input_data: str```> Dict: |
| """Procesamiento especial```do por agente""" |
| prompt = f"```o {agent.role} especializado en {', '.join(agent.knowledge_base)}, analiza: {input_data}" |
| |
| response = self.g```_client.chat.completions.create( |
| messages=[{"role": "user", "content": prompt}], |
| model=GROQ```DEL, |
| temperature```7, |
| max_tokens=1024 |
| ) |
| |
| agent.last_activation```datetime.now() |
| return``` "agent_id":```ent.id, |
| "```e": agent.role``` "analysis":```sponse.choices[0].message.content |
| } |
| |
| def _```rgent_synthesis(self, agent_output```List[Dict]) -> str: |
| """Generaci贸n``` conocimiento emergente""" |
| syntheses = [f"{output['role']}: {output['analysis']}" for output in agent_outputs] |
| synthesis```ompt = "Sintet``` las siguientes perspectivas en una```nclusi贸n unificada:\n" + "\n\n".join(syntheses) |
| |
| response```self.groq_client.chat```mpletions.create( |
| messages```[{"role": "user", "content": synthesis_prompt}], |
| model```OQ_MODEL, |
| temperature=```, |
| max_t```ns=2048 |
| ) |
| |
| return response```oices[0].message.content |
| |
| def _visualize_know```ge_graph(self): |
| """Generaci贸n```n谩mica de grafo de conocimiento``` |
| plt.figure```gsize=(12, 12)) |
| pos```nx.spring_layout(self.knowledge_graph, k```5) |
| nx.draw(self.knowledge_graph, pos,```th_labels=True, node_size```00, font_size```) |
| plt```tle("Evoluci贸n del Conocimiento Co```tivo") |
| return plt``` Evaluaci贸n DISC avanz``` |
| class DISC```filer: |
| """Sistema de perfil```ento cognitivo""" |
| def __init__(self): |
| self.questions```self._load```estions() |
| self```ait_vectors = self._init_t```t_vectors() |
| |
| def _load_questions(self) -> List[Dict]: |
| return [ |
| {"id": 1, "text": "En situaciones de presi贸n, tiendo a:", "options": { |
| "D": "Tomar el control y dirigir acciones", |
| "I": "Motivar al equipo con energ铆a", |
| "S": "Mantener la calma y buscar consenso", |
| "C": "Analizar sistem谩ticamente las opciones" |
| }}, |
| # 11 preguntas adicionales con dise帽os psicom茅tricos |
| ] |
| |
| def _init_trait```ctors(self) -> Dict[str, np.ndarray]: |
| return { |
| "D```np.array([0.9, 0.1, 0.2, 0.3]), |
| "``` np.array([0.1, 0.9, 0.4, 0.2]), |
| "``` np.array([0.3, 0.3, 0.9, 0.1]), |
| "C": np```ray([0.2, 0.1, 0.1, 0.9]) |
| } |
| |
| def assess_profile(self, responses```ict[int, str]) -> CognitiveProfile``` """Calcula```rfil cognitivo con```bedding sem```ico""" |
| response_text =```".join([self.questions[q]["options"][r] for q, r in responses```ems()]) |
| semantic```ctor = DISC_MODEL```code([response_text])[0] |
| |
| #```peo a espacio```SC |
| disc_s```es = {dtype: np.dot(semantic_vector, vec) for dtype, vec in```lf.trait_vectors```ems()} |
| primary```max(disc_scores```ey=disc_scores```t) |
| secondary```sorted(disc_scores```ey=disc_scores```t, reverse=True```] if disc_scores[primary] > 0.7 else None |
| |
| return Cognitive```file( |
| primary=```CTYPE(primary), |
| secondary=DISCT```(secondary) if secondary```se None, |
| vector```mantic_vector, |
| traits=disc_scores``` ) |
| |
| def visualize_profile(self, profile: CognitiveProfile): |
| """Visualizaci贸n interactiva```l perfil""" |
| traits = list(profile.traits.keys()) |
| scores = [profile.traits[t] for t in traits] |
| |
| fig```go.Figure(data=[go.Bar(x=traits, y=scores)]) |
| fig.update_layout( |
| title="Per``` DISC Cognit```", |
| yaxis```tle="Intensidad del```sgo", |
| template```lotly_white" |
| ) |
| return fig |
| |
| # Interfaz adaptativa```f create_adaptive_```erface(): |
| """Interf```que se modifica```g煤n perfil```SC""" |
| with gr```ocks(theme=gr.themes.Soft(primary_hue="pur```")) as interface: |
| #```cci贸n de evaluaci贸n DISC |
| with```.Row(): |
| ```sc_responses```{} |
| for question``` DISCProfiler().questions: |
| ```th gr.Column(): |
| gr.Mark```n(f"**{question['text']}**") |
| for opt_key```pt_text in question["options"].items(): |
| btn =```.Button(opt_text, variant="secondary") |
| disc_res```ses[question["id"]] = btn.click( |
| fn=lambda```question["id"], v=opt_key: {k: v}, |
| outputs=None``` ) |
| |
| # Proces```ento colectivo |
| with gr.Row``` |
| input_text = gr```xtbox(label="Consulta Co```tiva", lines``` |
| submit_``` = gr.Button("Engendrar```teligencia", variant```rimary") |
| |
| # Visualizaci贸n```aptativa |
| with```.Row(): |
| profile```z = gr.Plot() |
| knowledge_graph```gr.Plot() |
| ```sight_output = gr.M```down() |
| |
| # Eventos``` submit_btn.click``` fn=```cess_collective_inquiry``` inputs=[input_text, disc_responses], |
| ```tputs=[insight_output, knowledge_graph, profile_viz] |
| ) |
| |
| return interface```sync def process_collective```quiry(query: str, responses: dict): |
| """Flujo```mpleto de procesamiento""" |
| profiler = DISCProf```r() |
| profile =```ofiler.assess```ofile(responses) |
| profile```z = profiler.```ualize_profile(profile) |
| |
| system = Emergent```tem() |
| collective_```ight = await```stem.collective_reason```(query) |
| knowledge```aph = system._```ualize_knowledge_graph() |
| |
| return collective_```ight["insights"], knowledge_graph, profile```z |
| |
| # Punto de entrada |
| if __name```== "__main```: |
| nexus_interface```create_adaptive_interface``` nexus_```erface.launch( |
| server_name="0.0.0.0", |
| server_port=7860, |
| share```ue |
| ) |
