import gradio as gr import anthropic import PyPDF2 import pandas as pd import numpy as np import io import os import json import zipfile import tempfile from typing import Dict, List, Tuple, Union, Optional import re from pathlib import Path import openpyxl from dataclasses import dataclass from enum import Enum from docx import Document from docx.shared import Inches, Pt, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from reportlab.lib import colors from reportlab.lib.pagesizes import letter, A4 from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer, PageBreak from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch from reportlab.pdfbase import pdfmetrics from reportlab.pdfbase.ttfonts import TTFont import matplotlib.pyplot as plt from datetime import datetime # Configuración para HuggingFace os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False' # Inicializar cliente Anthropic client = anthropic.Anthropic() # Sistema de traducción - Actualizado con nuevas entradas TRANSLATIONS = { 'en': { 'title': '🧬 Comparative Analyzer of Biotechnological Models', 'subtitle': 'Specialized in comparative analysis of mathematical model fitting results', 'upload_files': '📁 Upload fitting results (CSV/Excel)', 'select_model': '🤖 Claude Model', 'select_language': '🌐 Language', 'select_theme': '🎨 Theme', 'detail_level': '📋 Analysis detail level', 'detailed': 'Detailed', 'summarized': 'Summarized', 'analyze_button': '🚀 Analyze and Compare Models', 'export_format': '📄 Export format', 'export_button': '💾 Export Report', 'comparative_analysis': '📊 Comparative Analysis', 'implementation_code': '💻 Implementation Code', 'data_format': '📋 Expected data format', 'examples': '📚 Analysis examples', 'light': 'Light', 'dark': 'Dark', 'best_for': 'Best for', 'loading': 'Loading...', 'error_no_api': 'Please configure ANTHROPIC_API_KEY in HuggingFace Space secrets', 'error_no_files': 'Please upload fitting result files to analyze', 'report_exported': 'Report exported successfully as', 'specialized_in': '🎯 Specialized in:', 'metrics_analyzed': '📊 Analyzed metrics:', 'what_analyzes': '🔍 What it specifically analyzes:', 'tips': '💡 Tips for better results:', 'additional_specs': '📝 Additional specifications for analysis', 'additional_specs_placeholder': 'Add any specific requirements or focus areas for the analysis...' }, 'es': { 'title': '🧬 Analizador Comparativo de Modelos Biotecnológicos', 'subtitle': 'Especializado en análisis comparativo de resultados de ajuste de modelos matemáticos', 'upload_files': '📁 Subir resultados de ajuste (CSV/Excel)', 'select_model': '🤖 Modelo Claude', 'select_language': '🌐 Idioma', 'select_theme': '🎨 Tema', 'detail_level': '📋 Nivel de detalle del análisis', 'detailed': 'Detallado', 'summarized': 'Resumido', 'analyze_button': '🚀 Analizar y Comparar Modelos', 'export_format': '📄 Formato de exportación', 'export_button': '💾 Exportar Reporte', 'comparative_analysis': '📊 Análisis Comparativo', 'implementation_code': '💻 Código de Implementación', 'data_format': '📋 Formato de datos esperado', 'examples': '📚 Ejemplos de análisis', 'light': 'Claro', 'dark': 'Oscuro', 'best_for': 'Mejor para', 'loading': 'Cargando...', 'error_no_api': 'Por favor configura ANTHROPIC_API_KEY en los secretos del Space', 'error_no_files': 'Por favor sube archivos con resultados de ajuste para analizar', 'report_exported': 'Reporte exportado exitosamente como', 'specialized_in': '🎯 Especializado en:', 'metrics_analyzed': '📊 Métricas analizadas:', 'what_analyzes': '🔍 Qué analiza específicamente:', 'tips': '💡 Tips para mejores resultados:', 'additional_specs': '📝 Especificaciones adicionales para el análisis', 'additional_specs_placeholder': 'Agregue cualquier requerimiento específico o áreas de enfoque para el análisis...' }, 'fr': { 'title': '🧬 Analyseur Comparatif de Modèles Biotechnologiques', 'subtitle': 'Spécialisé dans l\'analyse comparative des résultats d\'ajustement', 'upload_files': '📁 Télécharger les résultats (CSV/Excel)', 'select_model': '🤖 Modèle Claude', 'select_language': '🌐 Langue', 'select_theme': '🎨 Thème', 'detail_level': '📋 Niveau de détail', 'detailed': 'Détaillé', 'summarized': 'Résumé', 'analyze_button': '🚀 Analyser et Comparer', 'export_format': '📄 Format d\'export', 'export_button': '💾 Exporter le Rapport', 'comparative_analysis': '📊 Analyse Comparative', 'implementation_code': '💻 Code d\'Implémentation', 'data_format': '📋 Format de données attendu', 'examples': '📚 Exemples d\'analyse', 'light': 'Clair', 'dark': 'Sombre', 'best_for': 'Meilleur pour', 'loading': 'Chargement...', 'error_no_api': 'Veuillez configurer ANTHROPIC_API_KEY', 'error_no_files': 'Veuillez télécharger des fichiers à analyser', 'report_exported': 'Rapport exporté avec succès comme', 'specialized_in': '🎯 Spécialisé dans:', 'metrics_analyzed': '📊 Métriques analysées:', 'what_analyzes': '🔍 Ce qu\'il analyse spécifiquement:', 'tips': '💡 Conseils pour de meilleurs résultats:', 'additional_specs': '📝 Spécifications supplémentaires pour l\'analyse', 'additional_specs_placeholder': 'Ajoutez des exigences spécifiques ou des domaines d\'intérêt pour l\'analyse...' }, 'de': { 'title': '🧬 Vergleichender Analysator für Biotechnologische Modelle', 'subtitle': 'Spezialisiert auf vergleichende Analyse von Modellanpassungsergebnissen', 'upload_files': '📁 Ergebnisse hochladen (CSV/Excel)', 'select_model': '🤖 Claude Modell', 'select_language': '🌐 Sprache', 'select_theme': '🎨 Thema', 'detail_level': '📋 Detailgrad der Analyse', 'detailed': 'Detailliert', 'summarized': 'Zusammengefasst', 'analyze_button': '🚀 Analysieren und Vergleichen', 'export_format': '📄 Exportformat', 'export_button': '💾 Bericht Exportieren', 'comparative_analysis': '📊 Vergleichende Analyse', 'implementation_code': '💻 Implementierungscode', 'data_format': '📋 Erwartetes Datenformat', 'examples': '📚 Analysebeispiele', 'light': 'Hell', 'dark': 'Dunkel', 'best_for': 'Am besten für', 'loading': 'Laden...', 'error_no_api': 'Bitte konfigurieren Sie ANTHROPIC_API_KEY', 'error_no_files': 'Bitte laden Sie Dateien zur Analyse hoch', 'report_exported': 'Bericht erfolgreich exportiert als', 'specialized_in': '🎯 Spezialisiert auf:', 'metrics_analyzed': '📊 Analysierte Metriken:', 'what_analyzes': '🔍 Was spezifisch analysiert wird:', 'tips': '💡 Tipps für bessere Ergebnisse:', 'additional_specs': '📝 Zusätzliche Spezifikationen für die Analyse', 'additional_specs_placeholder': 'Fügen Sie spezifische Anforderungen oder Schwerpunktbereiche für die Analyse hinzu...' }, 'pt': { 'title': '🧬 Analisador Comparativo de Modelos Biotecnológicos', 'subtitle': 'Especializado em análise comparativa de resultados de ajuste', 'upload_files': '📁 Carregar resultados (CSV/Excel)', 'select_model': '🤖 Modelo Claude', 'select_language': '🌐 Idioma', 'select_theme': '🎨 Tema', 'detail_level': '📋 Nível de detalhe', 'detailed': 'Detalhado', 'summarized': 'Resumido', 'analyze_button': '🚀 Analisar e Comparar', 'export_format': '📄 Formato de exportação', 'export_button': '💾 Exportar Relatório', 'comparative_analysis': '📊 Análise Comparativa', 'implementation_code': '💻 Código de Implementação', 'data_format': '📋 Formato de dados esperado', 'examples': '📚 Exemplos de análise', 'light': 'Claro', 'dark': 'Escuro', 'best_for': 'Melhor para', 'loading': 'Carregando...', 'error_no_api': 'Por favor configure ANTHROPIC_API_KEY', 'error_no_files': 'Por favor carregue arquivos para analisar', 'report_exported': 'Relatório exportado com sucesso como', 'specialized_in': '🎯 Especializado em:', 'metrics_analyzed': '📊 Métricas analisadas:', 'what_analyzes': '🔍 O que analisa especificamente:', 'tips': '💡 Dicas para melhores resultados:', 'additional_specs': '📝 Especificações adicionais para a análise', 'additional_specs_placeholder': 'Adicione requisitos específicos ou áreas de foco para a análise...' } } # Temas disponibles THEMES = { 'light': gr.themes.Soft(), 'dark': gr.themes.Base( primary_hue="blue", secondary_hue="gray", neutral_hue="gray", font=["Arial", "sans-serif"] ).set( body_background_fill="dark", body_background_fill_dark="*neutral_950", button_primary_background_fill="*primary_600", button_primary_background_fill_hover="*primary_500", button_primary_text_color="white", block_background_fill="*neutral_800", block_border_color="*neutral_700", block_label_text_color="*neutral_200", block_title_text_color="*neutral_100", checkbox_background_color="*neutral_700", checkbox_background_color_selected="*primary_600", input_background_fill="*neutral_700", input_border_color="*neutral_600", input_placeholder_color="*neutral_400" ) } # Enum para tipos de análisis class AnalysisType(Enum): MATHEMATICAL_MODEL = "mathematical_model" DATA_FITTING = "data_fitting" FITTING_RESULTS = "fitting_results" UNKNOWN = "unknown" # Estructura modular para modelos @dataclass class MathematicalModel: name: str equation: str parameters: List[str] application: str sources: List[str] category: str biological_meaning: str # Sistema de registro de modelos escalable class ModelRegistry: def __init__(self): self.models = {} self._initialize_default_models() def register_model(self, model: MathematicalModel): """Registra un nuevo modelo matemático""" if model.category not in self.models: self.models[model.category] = {} self.models[model.category][model.name] = model def get_model(self, category: str, name: str) -> MathematicalModel: """Obtiene un modelo específico""" return self.models.get(category, {}).get(name) def get_all_models(self) -> Dict: """Retorna todos los modelos registrados""" return self.models def _initialize_default_models(self): """Inicializa los modelos por defecto""" # Modelos de crecimiento self.register_model(MathematicalModel( name="Monod", equation="μ = μmax × (S / (Ks + S))", parameters=["μmax (h⁻¹)", "Ks (g/L)"], application="Crecimiento limitado por sustrato único", sources=["Cambridge", "MIT", "DTU"], category="crecimiento_biomasa", biological_meaning="Describe cómo la velocidad de crecimiento depende de la concentración de sustrato limitante" )) self.register_model(MathematicalModel( name="Logístico", equation="dX/dt = μmax × X × (1 - X/Xmax)", parameters=["μmax (h⁻¹)", "Xmax (g/L)"], application="Sistemas cerrados batch", sources=["Cranfield", "Swansea", "HAL Theses"], category="crecimiento_biomasa", biological_meaning="Modela crecimiento limitado por capacidad de carga del sistema" )) self.register_model(MathematicalModel( name="Gompertz", equation="X(t) = Xmax × exp(-exp((μmax × e / Xmax) × (λ - t) + 1))", parameters=["λ (h)", "μmax (h⁻¹)", "Xmax (g/L)"], application="Crecimiento con fase lag pronunciada", sources=["Lund University", "NC State"], category="crecimiento_biomasa", biological_meaning="Incluye fase de adaptación (lag) seguida de crecimiento exponencial y estacionario" )) # Instancia global del registro model_registry = ModelRegistry() # Modelos de Claude disponibles CLAUDE_MODELS = { "claude-opus-4-20250514": { "name": "Claude Opus 4 (Latest)", "description": "Modelo más potente para desafíos complejos", "max_tokens": 4000, "best_for": "Análisis muy detallados y complejos" }, "claude-sonnet-4-20250514": { "name": "Claude Sonnet 4 (Latest)", "description": "Modelo inteligente y eficiente para uso cotidiano", "max_tokens": 4000, "best_for": "Análisis general, recomendado para la mayoría de casos" }, "claude-3-5-haiku-20241022": { "name": "Claude 3.5 Haiku (Latest)", "description": "Modelo más rápido para tareas diarias", "max_tokens": 4000, "best_for": "Análisis rápidos y económicos" }, "claude-3-7-sonnet-20250219": { "name": "Claude 3.7 Sonnet", "description": "Modelo avanzado de la serie 3.7", "max_tokens": 4000, "best_for": "Análisis equilibrados con alta calidad" }, "claude-3-5-sonnet-20241022": { "name": "Claude 3.5 Sonnet (Oct 2024)", "description": "Excelente balance entre velocidad y capacidad", "max_tokens": 4000, "best_for": "Análisis rápidos y precisos" } } class FileProcessor: """Clase para procesar diferentes tipos de archivos""" @staticmethod def extract_text_from_pdf(pdf_file) -> str: """Extrae texto de un archivo PDF""" try: pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file)) text = "" for page in pdf_reader.pages: text += page.extract_text() + "\n" return text except Exception as e: return f"Error reading PDF: {str(e)}" @staticmethod def read_csv(csv_file) -> pd.DataFrame: """Lee archivo CSV""" try: return pd.read_csv(io.BytesIO(csv_file)) except Exception as e: return None @staticmethod def read_excel(excel_file) -> pd.DataFrame: """Lee archivo Excel""" try: return pd.read_excel(io.BytesIO(excel_file)) except Exception as e: return None @staticmethod def extract_from_zip(zip_file) -> List[Tuple[str, bytes]]: """Extrae archivos de un ZIP""" files = [] try: with zipfile.ZipFile(io.BytesIO(zip_file), 'r') as zip_ref: for file_name in zip_ref.namelist(): if not file_name.startswith('__MACOSX'): file_data = zip_ref.read(file_name) files.append((file_name, file_data)) except Exception as e: print(f"Error processing ZIP: {e}") return files class ReportExporter: """Clase para exportar reportes a diferentes formatos""" @staticmethod def export_to_docx(content: str, filename: str, language: str = 'en') -> str: """Exporta el contenido a un archivo DOCX""" doc = Document() # Configurar estilos title_style = doc.styles['Title'] title_style.font.size = Pt(24) title_style.font.bold = True heading_style = doc.styles['Heading 1'] heading_style.font.size = Pt(18) heading_style.font.bold = True # Título title_text = { 'en': 'Comparative Analysis Report - Biotechnological Models', 'es': 'Informe de Análisis Comparativo - Modelos Biotecnológicos', 'fr': 'Rapport d\'Analyse Comparative - Modèles Biotechnologiques', 'de': 'Vergleichsanalysebericht - Biotechnologische Modelle', 'pt': 'Relatório de Análise Comparativa - Modelos Biotecnológicos' } doc.add_heading(title_text.get(language, title_text['en']), 0) # Fecha date_text = { 'en': 'Generated on', 'es': 'Generado el', 'fr': 'Généré le', 'de': 'Erstellt am', 'pt': 'Gerado em' } doc.add_paragraph(f"{date_text.get(language, date_text['en'])}: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") doc.add_paragraph() # Procesar contenido lines = content.split('\n') current_paragraph = None for line in lines: line = line.strip() if line.startswith('###'): doc.add_heading(line.replace('###', '').strip(), level=2) elif line.startswith('##'): doc.add_heading(line.replace('##', '').strip(), level=1) elif line.startswith('#'): doc.add_heading(line.replace('#', '').strip(), level=0) elif line.startswith('**') and line.endswith('**'): # Texto en negrita p = doc.add_paragraph() run = p.add_run(line.replace('**', '')) run.bold = True elif line.startswith('- ') or line.startswith('* '): # Lista doc.add_paragraph(line[2:], style='List Bullet') elif line.startswith(tuple('0123456789')): # Lista numerada doc.add_paragraph(line, style='List Number') elif line == '---' or line.startswith('==='): # Separador doc.add_paragraph('_' * 50) elif line: # Párrafo normal doc.add_paragraph(line) # Guardar documento doc.save(filename) return filename @staticmethod def export_to_pdf(content: str, filename: str, language: str = 'en') -> str: """Exporta el contenido a un archivo PDF""" # Crear documento PDF doc = SimpleDocTemplate(filename, pagesize=letter) story = [] styles = getSampleStyleSheet() # Estilos personalizados title_style = ParagraphStyle( 'CustomTitle', parent=styles['Title'], fontSize=24, textColor=colors.HexColor('#1f4788'), spaceAfter=30 ) heading_style = ParagraphStyle( 'CustomHeading', parent=styles['Heading1'], fontSize=16, textColor=colors.HexColor('#2e5090'), spaceAfter=12 ) # Título title_text = { 'en': 'Comparative Analysis Report - Biotechnological Models', 'es': 'Informe de Análisis Comparativo - Modelos Biotecnológicos', 'fr': 'Rapport d\'Analyse Comparative - Modèles Biotechnologiques', 'de': 'Vergleichsanalysebericht - Biotechnologische Modelle', 'pt': 'Relatório de Análise Comparativa - Modelos Biotecnológicos' } story.append(Paragraph(title_text.get(language, title_text['en']), title_style)) # Fecha date_text = { 'en': 'Generated on', 'es': 'Generado el', 'fr': 'Généré le', 'de': 'Erstellt am', 'pt': 'Gerado em' } story.append(Paragraph(f"{date_text.get(language, date_text['en'])}: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", styles['Normal'])) story.append(Spacer(1, 0.5*inch)) # Procesar contenido lines = content.split('\n') for line in lines: line = line.strip() if not line: story.append(Spacer(1, 0.2*inch)) elif line.startswith('###'): story.append(Paragraph(line.replace('###', '').strip(), styles['Heading3'])) elif line.startswith('##'): story.append(Paragraph(line.replace('##', '').strip(), styles['Heading2'])) elif line.startswith('#'): story.append(Paragraph(line.replace('#', '').strip(), heading_style)) elif line.startswith('**') and line.endswith('**'): text = line.replace('**', '') story.append(Paragraph(f"{text}", styles['Normal'])) elif line.startswith('- ') or line.startswith('* '): story.append(Paragraph(f"• {line[2:]}", styles['Normal'])) elif line == '---' or line.startswith('==='): story.append(Spacer(1, 0.3*inch)) story.append(Paragraph("_" * 70, styles['Normal'])) story.append(Spacer(1, 0.3*inch)) else: # Limpiar caracteres especiales para PDF clean_line = line.replace('📊', '[GRAPH]').replace('🎯', '[TARGET]').replace('🔍', '[SEARCH]').replace('💡', '[TIP]') story.append(Paragraph(clean_line, styles['Normal'])) # Construir PDF doc.build(story) return filename class AIAnalyzer: """Clase para análisis con IA""" def __init__(self, client, model_registry): self.client = client self.model_registry = model_registry def detect_analysis_type(self, content: Union[str, pd.DataFrame]) -> AnalysisType: """Detecta el tipo de análisis necesario""" if isinstance(content, pd.DataFrame): columns = [col.lower() for col in content.columns] fitting_indicators = [ 'r2', 'r_squared', 'rmse', 'mse', 'aic', 'bic', 'parameter', 'param', 'coefficient', 'fit', 'model', 'equation', 'goodness', 'chi_square', 'p_value', 'confidence', 'standard_error', 'se' ] has_fitting_results = any(indicator in ' '.join(columns) for indicator in fitting_indicators) if has_fitting_results: return AnalysisType.FITTING_RESULTS else: return AnalysisType.DATA_FITTING prompt = """ Analyze this content and determine if it is: 1. A scientific article describing biotechnological mathematical models 2. Experimental data for parameter fitting 3. Model fitting results (with parameters, R², RMSE, etc.) Reply only with: "MODEL", "DATA" or "RESULTS" """ try: response = self.client.messages.create( model="claude-3-haiku-20240307", max_tokens=10, messages=[{"role": "user", "content": f"{prompt}\n\n{content[:1000]}"}] ) result = response.content[0].text.strip().upper() if "MODEL" in result: return AnalysisType.MATHEMATICAL_MODEL elif "RESULTS" in result: return AnalysisType.FITTING_RESULTS elif "DATA" in result: return AnalysisType.DATA_FITTING else: return AnalysisType.UNKNOWN except: return AnalysisType.UNKNOWN def get_language_prompt_prefix(self, language: str) -> str: """Obtiene el prefijo del prompt según el idioma""" prefixes = { 'en': "Please respond in English. ", 'es': "Por favor responde en español. ", 'fr': "Veuillez répondre en français. ", 'de': "Bitte antworten Sie auf Deutsch. ", 'pt': "Por favor responda em português. " } return prefixes.get(language, prefixes['en']) def analyze_fitting_results(self, data: pd.DataFrame, claude_model: str, detail_level: str = "detailed", language: str = "en", additional_specs: str = "") -> Dict: """Analiza resultados de ajuste de modelos con soporte multiidioma y especificaciones adicionales""" # Preparar resumen completo de los datos data_summary = f""" FITTING RESULTS DATA: Data structure: - Columns: {list(data.columns)} - Number of models evaluated: {len(data)} Complete data: {data.to_string()} Descriptive statistics: {data.describe().to_string()} """ # Extraer valores para usar en el código data_dict = data.to_dict('records') # Obtener prefijo de idioma lang_prefix = self.get_language_prompt_prefix(language) # Agregar especificaciones adicionales del usuario si existen user_specs_section = f""" USER ADDITIONAL SPECIFICATIONS: {additional_specs} Please ensure to address these specific requirements in your analysis. """ if additional_specs else "" # Prompt mejorado con instrucciones específicas para cada nivel if detail_level == "detailed": prompt = f""" {lang_prefix} You are an expert in biotechnology and mathematical modeling. Analyze these kinetic/biotechnological model fitting results. {user_specs_section} DETAIL LEVEL: DETAILED - Provide comprehensive analysis BY EXPERIMENT PERFORM A COMPREHENSIVE COMPARATIVE ANALYSIS PER EXPERIMENT: 1. **EXPERIMENT IDENTIFICATION AND OVERVIEW** - List ALL experiments/conditions tested (e.g., pH levels, temperatures, time points) - For EACH experiment, identify: * Experimental conditions * Number of models tested * Variables measured (biomass, substrate, product) 2. **MODEL IDENTIFICATION AND CLASSIFICATION BY EXPERIMENT** For EACH EXPERIMENT separately: - Identify ALL fitted mathematical models BY NAME - Classify them: biomass growth, substrate consumption, product formation - Show the mathematical equation of each model - List parameter values obtained for that specific experiment 3. **COMPARATIVE ANALYSIS PER EXPERIMENT** Create a section for EACH EXPERIMENT showing: **EXPERIMENT [Name/Condition]:** a) **BIOMASS MODELS** (if applicable): - Best model: [Name] with R²=[value], RMSE=[value] - Parameters: μmax=[value], Xmax=[value], etc. - Ranking of all biomass models tested b) **SUBSTRATE MODELS** (if applicable): - Best model: [Name] with R²=[value], RMSE=[value] - Parameters: Ks=[value], Yxs=[value], etc. - Ranking of all substrate models tested c) **PRODUCT MODELS** (if applicable): - Best model: [Name] with R²=[value], RMSE=[value] - Parameters: α=[value], β=[value], etc. - Ranking of all product models tested 4. **DETAILED COMPARATIVE TABLES** **Table 1: Summary by Experiment and Variable Type** | Experiment | Variable | Best Model | R² | RMSE | Key Parameters | Ranking | |------------|----------|------------|-------|------|----------------|---------| | Exp1 | Biomass | [Name] | [val] | [val]| μmax=X | 1 | | Exp1 | Substrate| [Name] | [val] | [val]| Ks=Y | 1 | | Exp1 | Product | [Name] | [val] | [val]| α=Z | 1 | | Exp2 | Biomass | [Name] | [val] | [val]| μmax=X2 | 1 | **Table 2: Complete Model Comparison Across All Experiments** | Model Name | Type | Exp1_R² | Exp1_RMSE | Exp2_R² | Exp2_RMSE | Avg_R² | Best_For | 5. **PARAMETER ANALYSIS ACROSS EXPERIMENTS** - Compare how parameters change between experiments - Identify trends (e.g., μmax increases with temperature) - Calculate average parameters and variability - Suggest optimal conditions based on parameters 6. **BIOLOGICAL INTERPRETATION BY EXPERIMENT** For each experiment, explain: - What the parameter values mean biologically - Whether values are realistic for the conditions - Key differences between experiments - Critical control parameters identified 7. **OVERALL BEST MODELS DETERMINATION** - **BEST BIOMASS MODEL OVERALL**: [Name] - performs best in [X] out of [Y] experiments - **BEST SUBSTRATE MODEL OVERALL**: [Name] - average R²=[value] - **BEST PRODUCT MODEL OVERALL**: [Name] - most consistent across conditions Justify with numerical evidence from multiple experiments. 8. **CONCLUSIONS AND RECOMMENDATIONS** - Which models are most robust across different conditions - Specific models to use for each experimental condition - Confidence intervals and prediction reliability - Scale-up recommendations with specific values Use Markdown format with clear structure. Include ALL numerical values from the data. Create clear sections for EACH EXPERIMENT. """ else: # summarized prompt = f""" {lang_prefix} You are an expert in biotechnology. Provide a CONCISE but COMPLETE analysis BY EXPERIMENT. {user_specs_section} DETAIL LEVEL: SUMMARIZED - Be concise but include all experiments and essential information PROVIDE A FOCUSED COMPARATIVE ANALYSIS: 1. **EXPERIMENTS OVERVIEW** - Total experiments analyzed: [number] - Conditions tested: [list] - Variables measured: biomass/substrate/product 2. **BEST MODELS BY EXPERIMENT - QUICK SUMMARY** 📊 **EXPERIMENT 1 [Name/Condition]:** - Biomass: [Model] (R²=[value]) - Substrate: [Model] (R²=[value]) - Product: [Model] (R²=[value]) 📊 **EXPERIMENT 2 [Name/Condition]:** - Biomass: [Model] (R²=[value]) - Substrate: [Model] (R²=[value]) - Product: [Model] (R²=[value]) [Continue for all experiments...] 3. **OVERALL WINNERS ACROSS ALL EXPERIMENTS** 🏆 **Best Models Overall:** - **Biomass**: [Model] - Best in [X]/[Y] experiments - **Substrate**: [Model] - Average R²=[value] - **Product**: [Model] - Most consistent performance 4. **QUICK COMPARISON TABLE** | Experiment | Best Biomass | Best Substrate | Best Product | Overall R² | |------------|--------------|----------------|--------------|------------| | Exp1 | [Model] | [Model] | [Model] | [avg] | | Exp2 | [Model] | [Model] | [Model] | [avg] | 5. **KEY FINDINGS** - Parameter ranges across experiments: μmax=[min-max], Ks=[min-max] - Best conditions identified: [specific values] - Most robust models: [list with reasons] 6. **PRACTICAL RECOMMENDATIONS** - For biomass prediction: Use [Model] - For substrate monitoring: Use [Model] - For product estimation: Use [Model] - Critical parameters: [list with values] Keep it concise but include ALL experiments and model names with their key metrics. """ try: response = self.client.messages.create( model=claude_model, max_tokens=4000, messages=[{ "role": "user", "content": f"{prompt}\n\n{data_summary}" }] ) # Análisis adicional para generar código con valores numéricos reales code_prompt = f""" {lang_prefix} Based on the analysis and this actual data: {data.to_string()} Generate Python code that: 1. Creates a complete analysis system with the ACTUAL NUMERICAL VALUES from the data 2. Implements analysis BY EXPERIMENT showing: - Best models for each experiment - Comparison across experiments - Parameter evolution between conditions 3. Includes visualization functions that: - Show results PER EXPERIMENT - Compare models across experiments - Display parameter trends 4. Shows the best model for biomass, substrate, and product separately The code must include: - Data loading with experiment identification - Model comparison by experiment and variable type - Visualization showing results per experiment - Overall best model selection with justification - Functions to predict using the best models for each category Make sure to include comments indicating which model won for each variable type and why. Format: Complete, executable Python code with actual data values embedded. """ code_response = self.client.messages.create( model=claude_model, max_tokens=3000, messages=[{ "role": "user", "content": code_prompt }] ) return { "tipo": "Comparative Analysis of Mathematical Models", "analisis_completo": response.content[0].text, "codigo_implementacion": code_response.content[0].text, "resumen_datos": { "n_modelos": len(data), "columnas": list(data.columns), "metricas_disponibles": [col for col in data.columns if any(metric in col.lower() for metric in ['r2', 'rmse', 'aic', 'bic', 'mse'])], "mejor_r2": data['R2'].max() if 'R2' in data.columns else None, "mejor_modelo_r2": data.loc[data['R2'].idxmax()]['Model'] if 'R2' in data.columns and 'Model' in data.columns else None, "datos_completos": data_dict # Incluir todos los datos para el código } } except Exception as e: return {"error": str(e)} def process_files(files, claude_model: str, detail_level: str = "detailed", language: str = "en", additional_specs: str = "") -> Tuple[str, str]: """Procesa múltiples archivos con soporte de idioma y especificaciones adicionales""" processor = FileProcessor() analyzer = AIAnalyzer(client, model_registry) results = [] all_code = [] for file in files: if file is None: continue file_name = file.name if hasattr(file, 'name') else "archivo" file_ext = Path(file_name).suffix.lower() with open(file.name, 'rb') as f: file_content = f.read() if file_ext in ['.csv', '.xlsx', '.xls']: if language == 'es': results.append(f"## 📊 Análisis de Resultados: {file_name}") else: results.append(f"## 📊 Results Analysis: {file_name}") if file_ext == '.csv': df = processor.read_csv(file_content) else: df = processor.read_excel(file_content) if df is not None: analysis_type = analyzer.detect_analysis_type(df) if analysis_type == AnalysisType.FITTING_RESULTS: result = analyzer.analyze_fitting_results( df, claude_model, detail_level, language, additional_specs ) if language == 'es': results.append("### 🎯 ANÁLISIS COMPARATIVO DE MODELOS MATEMÁTICOS") else: results.append("### 🎯 COMPARATIVE ANALYSIS OF MATHEMATICAL MODELS") results.append(result.get("analisis_completo", "")) if "codigo_implementacion" in result: all_code.append(result["codigo_implementacion"]) results.append("\n---\n") analysis_text = "\n".join(results) code_text = "\n\n# " + "="*50 + "\n\n".join(all_code) if all_code else generate_implementation_code(analysis_text) return analysis_text, code_text def generate_implementation_code(analysis_results: str) -> str: """Genera código de implementación con análisis por experimento""" code = """ import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.integrate import odeint from scipy.optimize import curve_fit, differential_evolution from sklearn.metrics import r2_score, mean_squared_error import seaborn as sns from typing import Dict, List, Tuple, Optional # Visualization configuration plt.style.use('seaborn-v0_8-darkgrid') sns.set_palette("husl") class ExperimentalModelAnalyzer: \"\"\" Class for comparative analysis of biotechnological models across multiple experiments. Analyzes biomass, substrate and product models separately for each experimental condition. \"\"\" def __init__(self): self.results_df = None self.experiments = {} self.best_models_by_experiment = {} self.overall_best_models = { 'biomass': None, 'substrate': None, 'product': None } def load_results(self, file_path: str = None, data_dict: dict = None) -> pd.DataFrame: \"\"\"Load fitting results from CSV/Excel file or dictionary\"\"\" if data_dict: self.results_df = pd.DataFrame(data_dict) elif file_path: if file_path.endswith('.csv'): self.results_df = pd.read_csv(file_path) else: self.results_df = pd.read_excel(file_path) print(f"✅ Data loaded: {len(self.results_df)} models") print(f"📊 Available columns: {list(self.results_df.columns)}") # Identify experiments if 'Experiment' in self.results_df.columns: self.experiments = self.results_df.groupby('Experiment').groups print(f"🧪 Experiments found: {list(self.experiments.keys())}") return self.results_df def analyze_by_experiment(self, experiment_col: str = 'Experiment', model_col: str = 'Model', type_col: str = 'Type', r2_col: str = 'R2', rmse_col: str = 'RMSE') -> Dict: \"\"\" Analyze models by experiment and variable type. Identifies best models for biomass, substrate, and product in each experiment. \"\"\" if self.results_df is None: raise ValueError("First load data with load_results()") results_by_exp = {} # Get unique experiments if experiment_col in self.results_df.columns: experiments = self.results_df[experiment_col].unique() else: experiments = ['All_Data'] self.results_df[experiment_col] = 'All_Data' print("\\n" + "="*80) print("📊 ANALYSIS BY EXPERIMENT AND VARIABLE TYPE") print("="*80) for exp in experiments: print(f"\\n🧪 EXPERIMENT: {exp}") print("-"*50) exp_data = self.results_df[self.results_df[experiment_col] == exp] results_by_exp[exp] = {} # Analyze by variable type if available if type_col in exp_data.columns: var_types = exp_data[type_col].unique() for var_type in var_types: var_data = exp_data[exp_data[type_col] == var_type] if not var_data.empty: # Find best model for this variable type best_idx = var_data[r2_col].idxmax() best_model = var_data.loc[best_idx] results_by_exp[exp][var_type] = { 'best_model': best_model[model_col], 'r2': best_model[r2_col], 'rmse': best_model[rmse_col], 'all_models': var_data[[model_col, r2_col, rmse_col]].to_dict('records') } print(f"\\n 📈 {var_type.upper()}:") print(f" Best Model: {best_model[model_col]}") print(f" R² = {best_model[r2_col]:.4f}") print(f" RMSE = {best_model[rmse_col]:.4f}") # Show all models for this variable print(f"\\n All {var_type} models tested:") for _, row in var_data.iterrows(): print(f" - {row[model_col]}: R²={row[r2_col]:.4f}, RMSE={row[rmse_col]:.4f}") else: # If no type column, analyze all models together best_idx = exp_data[r2_col].idxmax() best_model = exp_data.loc[best_idx] results_by_exp[exp]['all'] = { 'best_model': best_model[model_col], 'r2': best_model[r2_col], 'rmse': best_model[rmse_col], 'all_models': exp_data[[model_col, r2_col, rmse_col]].to_dict('records') } self.best_models_by_experiment = results_by_exp # Determine overall best models self._determine_overall_best_models() return results_by_exp def _determine_overall_best_models(self): \"\"\"Determine the best models across all experiments\"\"\" print("\\n" + "="*80) print("🏆 OVERALL BEST MODELS ACROSS ALL EXPERIMENTS") print("="*80) # Aggregate performance by model and type model_performance = {} for exp, exp_results in self.best_models_by_experiment.items(): for var_type, var_results in exp_results.items(): if var_type not in model_performance: model_performance[var_type] = {} for model_data in var_results['all_models']: model_name = model_data['Model'] if model_name not in model_performance[var_type]: model_performance[var_type][model_name] = { 'r2_values': [], 'rmse_values': [], 'experiments': [] } model_performance[var_type][model_name]['r2_values'].append(model_data['R2']) model_performance[var_type][model_name]['rmse_values'].append(model_data['RMSE']) model_performance[var_type][model_name]['experiments'].append(exp) # Calculate average performance and select best for var_type, models in model_performance.items(): best_avg_r2 = -1 best_model = None print(f"\\n📊 {var_type.upper()} MODELS:") for model_name, perf_data in models.items(): avg_r2 = np.mean(perf_data['r2_values']) avg_rmse = np.mean(perf_data['rmse_values']) n_exp = len(perf_data['experiments']) print(f" {model_name}:") print(f" Average R² = {avg_r2:.4f}") print(f" Average RMSE = {avg_rmse:.4f}") print(f" Tested in {n_exp} experiments") if avg_r2 > best_avg_r2: best_avg_r2 = avg_r2 best_model = { 'name': model_name, 'avg_r2': avg_r2, 'avg_rmse': avg_rmse, 'n_experiments': n_exp } if var_type.lower() in ['biomass', 'substrate', 'product']: self.overall_best_models[var_type.lower()] = best_model print(f"\\n 🏆 BEST {var_type.upper()} MODEL: {best_model['name']} (Avg R²={best_model['avg_r2']:.4f})") def create_comparison_visualizations(self): \"\"\"Create visualizations comparing models across experiments\"\"\" if not self.best_models_by_experiment: raise ValueError("First run analyze_by_experiment()") # Prepare data for visualization experiments = [] biomass_r2 = [] substrate_r2 = [] product_r2 = [] for exp, results in self.best_models_by_experiment.items(): experiments.append(exp) biomass_r2.append(results.get('Biomass', {}).get('r2', 0)) substrate_r2.append(results.get('Substrate', {}).get('r2', 0)) product_r2.append(results.get('Product', {}).get('r2', 0)) # Create figure with subplots fig, axes = plt.subplots(2, 2, figsize=(15, 12)) fig.suptitle('Model Performance Comparison Across Experiments', fontsize=16) # 1. R² comparison by experiment and variable type ax1 = axes[0, 0] x = np.arange(len(experiments)) width = 0.25 ax1.bar(x - width, biomass_r2, width, label='Biomass', color='green', alpha=0.8) ax1.bar(x, substrate_r2, width, label='Substrate', color='blue', alpha=0.8) ax1.bar(x + width, product_r2, width, label='Product', color='red', alpha=0.8) ax1.set_xlabel('Experiment') ax1.set_ylabel('R²') ax1.set_title('Best Model R² by Experiment and Variable Type') ax1.set_xticks(x) ax1.set_xticklabels(experiments, rotation=45, ha='right') ax1.legend() ax1.grid(True, alpha=0.3) # Add value labels for i, (b, s, p) in enumerate(zip(biomass_r2, substrate_r2, product_r2)): if b > 0: ax1.text(i - width, b + 0.01, f'{b:.3f}', ha='center', va='bottom', fontsize=8) if s > 0: ax1.text(i, s + 0.01, f'{s:.3f}', ha='center', va='bottom', fontsize=8) if p > 0: ax1.text(i + width, p + 0.01, f'{p:.3f}', ha='center', va='bottom', fontsize=8) # 2. Model frequency heatmap ax2 = axes[0, 1] # This would show which models appear most frequently as best # Implementation depends on actual data structure ax2.text(0.5, 0.5, 'Model Frequency Analysis\\n(Most Used Models)', ha='center', va='center', transform=ax2.transAxes) ax2.set_title('Most Frequently Selected Models') # 3. Parameter evolution across experiments ax3 = axes[1, 0] ax3.text(0.5, 0.5, 'Parameter Evolution\\nAcross Experiments', ha='center', va='center', transform=ax3.transAxes) ax3.set_title('Parameter Trends') # 4. Overall best models summary ax4 = axes[1, 1] ax4.axis('off') summary_text = "🏆 OVERALL BEST MODELS\\n\\n" for var_type, model_info in self.overall_best_models.items(): if model_info: summary_text += f"{var_type.upper()}:\\n" summary_text += f" Model: {model_info['name']}\\n" summary_text += f" Avg R²: {model_info['avg_r2']:.4f}\\n" summary_text += f" Tested in: {model_info['n_experiments']} experiments\\n\\n" ax4.text(0.1, 0.9, summary_text, transform=ax4.transAxes, fontsize=12, verticalalignment='top', fontfamily='monospace') ax4.set_title('Overall Best Models Summary') plt.tight_layout() plt.show() def generate_summary_table(self) -> pd.DataFrame: \"\"\"Generate a summary table of best models by experiment and type\"\"\" summary_data = [] for exp, results in self.best_models_by_experiment.items(): for var_type, var_results in results.items(): summary_data.append({ 'Experiment': exp, 'Variable_Type': var_type, 'Best_Model': var_results['best_model'], 'R2': var_results['r2'], 'RMSE': var_results['rmse'] }) summary_df = pd.DataFrame(summary_data) print("\\n📋 SUMMARY TABLE: BEST MODELS BY EXPERIMENT AND VARIABLE TYPE") print("="*80) print(summary_df.to_string(index=False)) return summary_df # Example usage if __name__ == "__main__": print("🧬 Experimental Model Comparison System") print("="*60) # Example data structure with experiments example_data = { 'Experiment': ['pH_7.0', 'pH_7.0', 'pH_7.0', 'pH_7.5', 'pH_7.5', 'pH_7.5', 'pH_7.0', 'pH_7.0', 'pH_7.5', 'pH_7.5', 'pH_7.0', 'pH_7.0', 'pH_7.5', 'pH_7.5'], 'Model': ['Monod', 'Logistic', 'Gompertz', 'Monod', 'Logistic', 'Gompertz', 'First_Order', 'Monod_Substrate', 'First_Order', 'Monod_Substrate', 'Luedeking_Piret', 'Linear', 'Luedeking_Piret', 'Linear'], 'Type': ['Biomass', 'Biomass', 'Biomass', 'Biomass', 'Biomass', 'Biomass', 'Substrate', 'Substrate', 'Substrate', 'Substrate', 'Product', 'Product', 'Product', 'Product'], 'R2': [0.9845, 0.9912, 0.9956, 0.9789, 0.9834, 0.9901, 0.9723, 0.9856, 0.9698, 0.9812, 0.9634, 0.9512, 0.9687, 0.9423], 'RMSE': [0.0234, 0.0189, 0.0145, 0.0267, 0.0223, 0.0178, 0.0312, 0.0245, 0.0334, 0.0289, 0.0412, 0.0523, 0.0389, 0.0567], 'mu_max': [0.45, 0.48, 0.52, 0.42, 0.44, 0.49, None, None, None, None, None, None, None, None], 'Ks': [None, None, None, None, None, None, 2.1, 1.8, 2.3, 1.9, None, None, None, None] } # Create analyzer analyzer = ExperimentalModelAnalyzer() # Load data analyzer.load_results(data_dict=example_data) # Analyze by experiment results = analyzer.analyze_by_experiment() # Create visualizations analyzer.create_comparison_visualizations() # Generate summary table summary = analyzer.generate_summary_table() print("\\n✨ Analysis complete! Best models identified for each experiment and variable type.") """ return code # Estado global para almacenar resultados class AppState: def __init__(self): self.current_analysis = "" self.current_code = "" self.current_language = "en" app_state = AppState() def export_report(export_format: str, language: str) -> Tuple[str, str]: """Exporta el reporte al formato seleccionado""" if not app_state.current_analysis: error_msg = { 'en': "No analysis available to export", 'es': "No hay análisis disponible para exportar", 'fr': "Aucune analyse disponible pour exporter", 'de': "Keine Analyse zum Exportieren verfügbar", 'pt': "Nenhuma análise disponível para exportar" } return error_msg.get(language, error_msg['en']), "" timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") try: if export_format == "DOCX": filename = f"biotech_analysis_report_{timestamp}.docx" ReportExporter.export_to_docx(app_state.current_analysis, filename, language) else: # PDF filename = f"biotech_analysis_report_{timestamp}.pdf" ReportExporter.export_to_pdf(app_state.current_analysis, filename, language) success_msg = TRANSLATIONS[language]['report_exported'] return f"{success_msg} {filename}", filename except Exception as e: return f"Error: {str(e)}", "" # Interfaz Gradio con soporte multiidioma y temas def create_interface(): # Estado inicial current_theme = "light" current_language = "en" def update_interface_language(language): """Actualiza el idioma de la interfaz""" app_state.current_language = language t = TRANSLATIONS[language] return [ gr.update(value=f"# {t['title']}"), # title_text gr.update(value=t['subtitle']), # subtitle_text gr.update(label=t['upload_files']), # files_input gr.update(label=t['select_model']), # model_selector gr.update(label=t['select_language']), # language_selector gr.update(label=t['select_theme']), # theme_selector gr.update(label=t['detail_level']), # detail_level gr.update(label=t['additional_specs'], placeholder=t['additional_specs_placeholder']), # additional_specs gr.update(value=t['analyze_button']), # analyze_btn gr.update(label=t['export_format']), # export_format gr.update(value=t['export_button']), # export_btn gr.update(label=t['comparative_analysis']), # analysis_output gr.update(label=t['implementation_code']), # code_output gr.update(label=t['data_format']) # data_format_accordion ] def process_and_store(files, model, detail, language, additional_specs): """Procesa archivos y almacena resultados""" if not files: error_msg = TRANSLATIONS[language]['error_no_files'] return error_msg, "" analysis, code = process_files(files, model, detail, language, additional_specs) app_state.current_analysis = analysis app_state.current_code = code return analysis, code with gr.Blocks(theme=THEMES[current_theme]) as demo: # Componentes de UI with gr.Row(): with gr.Column(scale=3): title_text = gr.Markdown(f"# {TRANSLATIONS[current_language]['title']}") subtitle_text = gr.Markdown(TRANSLATIONS[current_language]['subtitle']) with gr.Column(scale=1): with gr.Row(): language_selector = gr.Dropdown( choices=[("English", "en"), ("Español", "es"), ("Français", "fr"), ("Deutsch", "de"), ("Português", "pt")], value="en", label=TRANSLATIONS[current_language]['select_language'], interactive=True ) theme_selector = gr.Dropdown( choices=[("Light", "light"), ("Dark", "dark")], value="light", label=TRANSLATIONS[current_language]['select_theme'], interactive=True ) with gr.Row(): with gr.Column(scale=1): files_input = gr.File( label=TRANSLATIONS[current_language]['upload_files'], file_count="multiple", file_types=[".csv", ".xlsx", ".xls", ".pdf", ".zip"], type="filepath" ) model_selector = gr.Dropdown( choices=list(CLAUDE_MODELS.keys()), value="claude-3-5-sonnet-20241022", label=TRANSLATIONS[current_language]['select_model'], info=f"{TRANSLATIONS[current_language]['best_for']}: {CLAUDE_MODELS['claude-3-5-sonnet-20241022']['best_for']}" ) detail_level = gr.Radio( choices=[ (TRANSLATIONS[current_language]['detailed'], "detailed"), (TRANSLATIONS[current_language]['summarized'], "summarized") ], value="detailed", label=TRANSLATIONS[current_language]['detail_level'] ) # Nueva entrada para especificaciones adicionales additional_specs = gr.Textbox( label=TRANSLATIONS[current_language]['additional_specs'], placeholder=TRANSLATIONS[current_language]['additional_specs_placeholder'], lines=3, max_lines=5, interactive=True ) analyze_btn = gr.Button( TRANSLATIONS[current_language]['analyze_button'], variant="primary", size="lg" ) gr.Markdown("---") export_format = gr.Radio( choices=["DOCX", "PDF"], value="PDF", label=TRANSLATIONS[current_language]['export_format'] ) export_btn = gr.Button( TRANSLATIONS[current_language]['export_button'], variant="secondary" ) export_status = gr.Textbox( label="Export Status", interactive=False, visible=False ) export_file = gr.File( label="Download Report", visible=False ) with gr.Column(scale=2): analysis_output = gr.Markdown( label=TRANSLATIONS[current_language]['comparative_analysis'] ) code_output = gr.Code( label=TRANSLATIONS[current_language]['implementation_code'], language="python", interactive=True, lines=20 ) data_format_accordion = gr.Accordion( label=TRANSLATIONS[current_language]['data_format'], open=False ) with data_format_accordion: gr.Markdown(""" ### Expected CSV/Excel structure: | Experiment | Model | Type | R2 | RMSE | AIC | BIC | mu_max | Ks | Parameters | |------------|-------|------|-----|------|-----|-----|--------|-------|------------| | pH_7.0 | Monod | Biomass | 0.985 | 0.023 | -45.2 | -42.1 | 0.45 | 2.1 | {...} | | pH_7.0 | Logistic | Biomass | 0.976 | 0.031 | -42.1 | -39.5 | 0.42 | - | {...} | | pH_7.0 | First_Order | Substrate | 0.992 | 0.018 | -48.5 | -45.2 | - | 1.8 | {...} | | pH_7.5 | Monod | Biomass | 0.978 | 0.027 | -44.1 | -41.2 | 0.43 | 2.2 | {...} | **Important columns:** - **Experiment**: Experimental condition identifier - **Model**: Model name - **Type**: Variable type (Biomass/Substrate/Product) - **R2, RMSE**: Fit quality metrics - **Parameters**: Model-specific parameters """) # Definir ejemplos examples = gr.Examples( examples=[ [["examples/biomass_models_comparison.csv"], "claude-3-5-sonnet-20241022", "detailed", ""], [["examples/substrate_kinetics_results.xlsx"], "claude-3-5-sonnet-20241022", "summarized", "Focus on temperature effects"] ], inputs=[files_input, model_selector, detail_level, additional_specs], label=TRANSLATIONS[current_language]['examples'] ) # Eventos - Actualizado para incluir additional_specs language_selector.change( update_interface_language, inputs=[language_selector], outputs=[ title_text, subtitle_text, files_input, model_selector, language_selector, theme_selector, detail_level, additional_specs, analyze_btn, export_format, export_btn, analysis_output, code_output, data_format_accordion ] ) def change_theme(theme_name): """Cambia el tema de la interfaz""" # Nota: En Gradio actual, cambiar el tema dinámicamente requiere recargar # Esta es una limitación conocida return gr.Info("Theme will be applied on next page load") theme_selector.change( change_theme, inputs=[theme_selector], outputs=[] ) analyze_btn.click( fn=process_and_store, inputs=[files_input, model_selector, detail_level, language_selector, additional_specs], outputs=[analysis_output, code_output] ) def handle_export(format, language): status, file = export_report(format, language) if file: return gr.update(value=status, visible=True), gr.update(value=file, visible=True) else: return gr.update(value=status, visible=True), gr.update(visible=False) export_btn.click( fn=handle_export, inputs=[export_format, language_selector], outputs=[export_status, export_file] ) return demo # Función principal def main(): if not os.getenv("ANTHROPIC_API_KEY"): print("⚠️ Configure ANTHROPIC_API_KEY in HuggingFace Space secrets") return gr.Interface( fn=lambda x: TRANSLATIONS['en']['error_no_api'], inputs=gr.Textbox(), outputs=gr.Textbox(), title="Configuration Error" ) return create_interface() # Para ejecución local if __name__ == "__main__": demo = main() if demo: demo.launch( server_name="0.0.0.0", server_port=7860, share=False )