app.py

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"<b>{text}</b>", 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
        )