Update app.py
Browse files
app.py
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import gradio as gr
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import os
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from openai import OpenAI
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import random
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# ✅ Configuración de la API (tu endpoint
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client = OpenAI(
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base_url="https://router.huggingface.co/v1",
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api_key=os.environ.get("Tzim_tzum", ""),
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def tzimtzum_process(concept: str, intensity: int = 3):
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"""Motor Tzim Tzum Hermenéutico:
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if not concept.strip():
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return "Por favor, ingresa un concepto para transformar."
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# 🌱 FASE 1: EXPANSIÓN INICIAL
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expansion_prompt = f"""
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Eres un motor de expansión conceptual puro. Toma el concepto '{concept}' y genera exactamente {intensity} variaciones RADICALMENTE DIFERENTES entre sí, explorando estos ángulos:
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except Exception as e:
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return f"Error en expansión: {str(e)}"
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# 🎯 SELECCIONA LA PRIMERA VARIACIÓN
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current_concept = expanded_concepts.split("\n\n")[0] if "\n\n" in expanded_concepts else expanded_concepts
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# 🌀 FASE 2: CONTRACCIÓN HERMENÉUTICA —
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transformation_log = []
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for filter_step in TZIMTZUM_FILTERS:
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filter_title = filter_step["title"]
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filter_prompt = filter_step["prompt"]
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contraction_prompt = f"""
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Eres un transformador conceptual hermenéutico. Aplica el filtro "{filter_title}" ({filter_name}) a la siguiente idea:
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@@ -114,27 +117,58 @@ Eres un transformador conceptual hermenéutico. Aplica el filtro "{filter_title}
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Instrucción específica del filtro:
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{filter_prompt}
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Transforma activamente la idea según esta instrucción.
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"""
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try:
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contraction_response = client.chat.completions.create(
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model="Qwen/Qwen3-Next-80B-A3B-Instruct",
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messages=[{"role": "user", "content": contraction_prompt}],
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max_tokens=
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temperature=0.5
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)
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new_concept = contraction_response.choices[0].message.content
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transformation_log.append({
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"filter": filter_name,
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"title": filter_title,
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"result": new_concept
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})
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current_concept = new_concept
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except Exception as e:
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# ✅ FASE 3: VALIDACIÓN FINAL (breve y estructurada)
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validation_prompt = f"""
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Evalúa esta idea final tras pasar por los 7 filtros hermenéuticos:
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"originalidad": número del 1-10,
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"coherencia": número del 1-10,
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"profundidad": número del 1-10,
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"resultado_final": "versión refinada y mejorada"
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}}
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"""
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# 📄 Genera el reporte final
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filters_applied = " → ".join([f["title"] for f in transformation_log])
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return f"""
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## RESULTADO TZIM TZUM HERMENÉUTICO
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**Concepto original:** {concept}
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{filters_applied}
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### 🧩 Evolución Detallada:
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+ "\n\n".join([
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f"**{step['title']}**:\n{step['result']}\n---"
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for step in transformation_log
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]) + f"""
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### 📊 Validación Final:
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{validation_response.choices[0].message.content}
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"""
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except Exception as e:
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# 🖥️ Interfaz Gradio (minimalista y funcional)
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def respond(message, history, intensity):
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import gradio as gr
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import os
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from openai import OpenAI
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# ✅ Configuración de la API (tu endpoint y token personalizados de HF)
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client = OpenAI(
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base_url="https://router.huggingface.co/v1",
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api_key=os.environ.get("Tzim_tzum", ""),
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]
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def tzimtzum_process(concept: str, intensity: int = 3):
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"""Motor Tzim Tzum Hermenéutico ROBUSTO: con gestión de tokens, truncamiento y recuperación parcial."""
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if not concept.strip():
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return "Por favor, ingresa un concepto para transformar."
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# 🌱 FASE 1: EXPANSIÓN INICIAL
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expansion_prompt = f"""
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Eres un motor de expansión conceptual puro. Toma el concepto '{concept}' y genera exactamente {intensity} variaciones RADICALMENTE DIFERENTES entre sí, explorando estos ángulos:
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except Exception as e:
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return f"Error en expansión: {str(e)}"
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# 🎯 SELECCIONA LA PRIMERA VARIACIÓN
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current_concept = expanded_concepts.split("\n\n")[0] if "\n\n" in expanded_concepts else expanded_concepts
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# 🌀 FASE 2: CONTRACCIÓN HERMENÉUTICA — CON GESTIÓN DE TOKENS
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transformation_log = []
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for filter_step in TZIMTZUM_FILTERS:
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filter_title = filter_step["title"]
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filter_prompt = filter_step["prompt"]
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# 🧩 Trunca la entrada actual si es demasiado larga
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if len(current_concept) > 1500:
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current_concept = current_concept[:1500] + "... [truncado para proceso]"
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contraction_prompt = f"""
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Eres un transformador conceptual hermenéutico. Aplica el filtro "{filter_title}" ({filter_name}) a la siguiente idea:
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Instrucción específica del filtro:
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{filter_prompt}
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Transforma activamente la idea según esta instrucción. Sé conciso. Máximo 300 palabras.
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"""
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try:
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contraction_response = client.chat.completions.create(
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model="Qwen/Qwen3-Next-80B-A3B-Instruct",
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messages=[{"role": "user", "content": contraction_prompt}],
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max_tokens=500,
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temperature=0.5
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)
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new_concept = contraction_response.choices[0].message.content
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# ✂️ Truncamos la salida para no saturar el siguiente paso
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if len(new_concept) > 1500:
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new_concept = new_concept[:1500] + " [...]"
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transformation_log.append({
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"filter": filter_name,
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"title": filter_title,
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"result": new_concept
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})
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current_concept = new_concept
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except Exception as e:
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# ❗ Si falla, guardamos lo que tenemos y devolvemos estado parcial
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partial_result = "\n\n".join([
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f"**{step['title']}**:\n{step['result']}\n---"
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for step in transformation_log
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])
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return f"""
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## ⚠️ PROCESO INTERRUMPIDO EN: {filter_title}
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**Concepto original:** {concept}
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### 🔄 Transformación hasta ahora:
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{' → '.join([f['title'] for f in transformation_log])}
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### 🧩 Evolución Detallada (parcial):
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{partial_result}
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### ❌ Error:
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{str(e)}
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---
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💡 *El sistema truncó respuestas largas para evitar saturación. Si necesitas más detalle, reduce la intensidad o procesa en etapas.*
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"""
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# ✅ FASE 3: VALIDACIÓN FINAL (con truncamiento también)
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if len(current_concept) > 1500:
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current_concept = current_concept[:1500] + " [...]"
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validation_prompt = f"""
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Evalúa esta idea final tras pasar por los 7 filtros hermenéuticos:
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"originalidad": número del 1-10,
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"coherencia": número del 1-10,
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"profundidad": número del 1-10,
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"resultado_final": "versión refinada y mejorada (máx. 200 palabras)"
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}}
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"""
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# 📄 Genera el reporte final
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filters_applied = " → ".join([f["title"] for f in transformation_log])
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detailed_evolution = "\n\n".join([
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f"**{step['title']}**:\n{step['result']}\n---"
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for step in transformation_log
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])
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return f"""
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## ✅ RESULTADO TZIM TZUM HERMENÉUTICO
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**Concepto original:** {concept}
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{filters_applied}
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### 🧩 Evolución Detallada:
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{detailed_evolution}
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### 📊 Validación Final:
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{validation_response.choices[0].message.content}
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"""
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except Exception as e:
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# Si falla la validación, devolvemos al menos la transformación completa
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detailed_evolution = "\n\n".join([
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f"**{step['title']}**:\n{step['result']}\n---"
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for step in transformation_log
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])
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return f"""
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## ✅ TRANSFORMACIÓN COMPLETA (validación fallida)
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**Concepto original:** {concept}
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### 🔄 Transformación Secuencial:
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{filters_applied}
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### 🧩 Evolución Detallada:
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{detailed_evolution}
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### ❌ Error en validación:
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{str(e)}
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"""
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# 🖥️ Interfaz Gradio (minimalista y funcional)
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def respond(message, history, intensity):
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