Update app.py

app.py

@@ -30,18 +30,13 @@ from openai import OpenAI  # Replaced Anthropic with OpenAI for Qwen
 # Configuración para HuggingFace
 os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 
-# --- Inicialización del Cliente Qwen ---
-# Asegúrate de que la variable de entorno NEBIUS_API_KEY esté configurada
-try:
-    client = OpenAI(
-        base_url="https://api.studio.nebius.com/v1/",
-        api_key=os.environ.get("NEBIUS_API_KEY")
-    )
-except Exception as e:
-    print(f"Error al inicializar el cliente de OpenAI: {e}")
-    client = None
+# Inicializar cliente Qwen
+client = OpenAI(
+    base_url="https://api.studio.nebius.com/v1/",
+    api_key=os.environ.get("NEBIUS_API_KEY")
+)
 
-# --- Sistema de Traducción ---
+# Sistema de traducción - Actualizado con nuevas entradas
 TRANSLATIONS = {
     'en': {
         'title': '🧬 Comparative Analyzer of Biotechnological Models',
@@ -63,7 +58,7 @@ TRANSLATIONS = {
         'light': 'Light',
         'dark': 'Dark',
         'best_for': 'Best for',
-        'loading': 'Analyzing... Please wait.',
+        'loading': 'Loading...',
         'error_no_api': 'Please configure NEBIUS_API_KEY in HuggingFace Space secrets',
         'error_no_files': 'Please upload fitting result files to analyze',
         'report_exported': 'Report exported successfully as',
@@ -94,7 +89,7 @@ TRANSLATIONS = {
         'light': 'Claro',
         'dark': 'Oscuro',
         'best_for': 'Mejor para',
-        'loading': 'Analizando... Por favor, espere.',
+        'loading': 'Cargando...',
         'error_no_api': 'Por favor configura NEBIUS_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',
@@ -105,10 +100,102 @@ TRANSLATIONS = {
         'additional_specs': '📝 Especificaciones adicionales para el análisis',
         'additional_specs_placeholder': 'Agregue cualquier requerimiento específico o áreas de enfoque para el análisis...'
     },
-    # Otras traducciones (fr, de, pt) se mantienen igual que en el original
+    '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 Qwen',
+        '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 NEBIUS_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': '🤖 Qwen 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 NEBIUS_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 Qwen',
+        '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 NEBIUS_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 de la Interfaz ---
+# Temas disponibles
 THEMES = {
     'light': gr.themes.Soft(),
     'dark': gr.themes.Base(
@@ -117,445 +204,1301 @@ THEMES = {
         neutral_hue="gray",
         font=["Arial", "sans-serif"]
     ).set(
+        body_background_fill="dark",
         body_background_fill_dark="*neutral_950",
-        block_background_fill_dark="*neutral_800",
-        block_label_text_color_dark="*neutral_200",
-        body_text_color_dark="*neutral_200",
+        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"
     )
 }
 
-# --- Clases de Estructura de Datos ---
+# 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 Qwen disponibles
+QWEN_MODELS = {
+    "Qwen/Qwen3-14B": {
+        "name": "Qwen 3 14B",
+        "description": "Modelo potente multilingüe de Alibaba",
+        "max_tokens": 4000,
+        "best_for": "Análisis complejos y detallados"
+    },
+    "Qwen/Qwen3-7B": {
+        "name": "Qwen 3 7B",
+        "description": "Modelo equilibrado para uso general",
+        "max_tokens": 4000,
+        "best_for": "Análisis rápidos y precisos"
+    },
+    "Qwen/Qwen1.5-14B": {
+        "name": "Qwen 1.5 14B",
+        "description": "Modelo avanzado para tareas complejas",
+        "max_tokens": 4000,
+        "best_for": "Análisis técnicos detallados"
+    }
+}
 
-# --- Clases de Procesamiento ---
 class FileProcessor:
-    """Clase para leer y procesar diferentes tipos de archivos de datos."""
+    """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(file_content: bytes) -> Optional[pd.DataFrame]:
+    def read_csv(csv_file) -> pd.DataFrame:
+        """Lee archivo CSV"""
         try:
-            return pd.read_csv(io.BytesIO(file_content))
+            return pd.read_csv(io.BytesIO(csv_file))
         except Exception as e:
-            print(f"Error reading CSV: {e}")
             return None
-
+    
     @staticmethod
-    def read_excel(file_content: bytes) -> Optional[pd.DataFrame]:
+    def read_excel(excel_file) -> pd.DataFrame:
+        """Lee archivo Excel"""
         try:
-            return pd.read_excel(io.BytesIO(file_content))
+            return pd.read_excel(io.BytesIO(excel_file))
         except Exception as e:
-            print(f"Error reading Excel: {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 (DOCX, PDF)."""
+    """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()
-        doc.add_heading('Biotechnology Model Analysis Report', 0)
-        # Lógica de formato simplificada para brevedad, la original es más compleja y se puede mantener
-        doc.add_paragraph(content)
+        
+        # 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()
-        story = [Paragraph("Biotechnology Model Analysis Report", styles['h1'])]
-        # Lógica de formato simplificada, la original es más compleja y se puede mantener
-        clean_content = re.sub(r'[^\x00-\x7F]+', '', content) # Remover caracteres no-ASCII para PDF básico
-        story.append(Paragraph(clean_content, styles['Normal']))
+        
+        # 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 central para interactuar con el modelo Qwen y realizar el análisis."""
-
-    def __init__(self, client_instance):
-        self.client = client_instance
-
+    """Clase para análisis con IA usando Qwen"""
+    
+    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.chat.completions.create(
+                model="Qwen/Qwen3-14B",
+                max_tokens=10,
+                temperature=0.0,
+                messages=[{"role": "user", "content": f"{prompt}\n\n{content[:1000]}"}]
+            )
+            
+            result = response.choices[0].message.content.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 Exception as e:
+            print(f"Error en detección de tipo: {str(e)}")
+            return AnalysisType.UNKNOWN
+    
     def get_language_prompt_prefix(self, language: str) -> str:
-        """Obtiene el prefijo del prompt según el idioma para guiar al modelo."""
+        """Obtiene el prefijo del prompt según el idioma"""
         prefixes = {
-            'es': "Por favor, responde en español.",
-            'en': "Please respond in English.",
-            'fr': "Veuillez répondre en français.",
-            'de': "Bitte antworten Sie auf Deutsch.",
-            'pt': "Por favor, responda em português."
+            '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, qwen_model: str, detail_level: str,
-                                  language: str, additional_specs: str) -> Dict:
-        """
-        Analiza los resultados de ajuste de modelos.
-        Convierte el DataFrame en texto y lo envía a la API de Qwen.
-        """
-        if self.client is None:
-            return {"error": TRANSLATIONS[language]['error_no_api']}
-
-        # **Paso Clave: Conversión del DataFrame (CSV/Excel) a Texto para la API**
-        # El LLM necesita ver todos los datos para hacer una comparación completa.
-        # to_string() es una forma efectiva de presentar datos tabulares en formato de texto.
-        dataframe_as_text = f"""
-        FITTING RESULTS DATASET:
-
+    
+    def analyze_fitting_results(self, data: pd.DataFrame, qwen_model: str, detail_level: str = "detailed", 
+                              language: str = "en", additional_specs: str = "") -> Dict:
+        """Analiza resultados de ajuste de modelos usando Qwen"""
+        
+        # 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)
-        user_specs_section = f"\nUSER ADDITIONAL SPECIFICATIONS:\n{additional_specs}\n" if additional_specs else ""
-
-        # Selección del prompt según el nivel de detalle
+        
+        # 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":
-            analysis_prompt = f"""
+            prompt = f"""
             {lang_prefix}
-            You are an expert in biotechnology and mathematical modeling. Analyze the provided model fitting results in detail.
+            
+            You are an expert in biotechnology and mathematical modeling. Analyze these kinetic/biotechnological model fitting results.
+            
             {user_specs_section}
-            TASK: Perform a comprehensive comparative analysis of the models based on the provided data.
-
-            1.  **Overall Summary:** Briefly describe the dataset (number of models, experiments, key metrics).
-            2.  **Model Performance Ranking:** Create a master table ranking all models based on key metrics (R², RMSE, AIC, BIC). Sort from best to worst.
-            3.  **Analysis by Experiment/Condition:** If an 'Experiment' column exists, group the analysis by each experiment. For each experiment, identify the best performing model and its parameters.
-            4.  **Parameter Interpretation:** Discuss the biological meaning of the key parameters (like μmax, Ks) for the best models. Are the values realistic? How do they change across conditions?
-            5.  **Strengths and Weaknesses:** For the top 3 models, discuss their potential strengths and weaknesses based on the fitting results.
-            6.  **Final Recommendation:** Conclude with a clear recommendation for the best overall model for the described process, justifying your choice with data. If different models are better for different conditions, state that.
-
-            Format the response using Markdown for clarity.
+            
+            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
-            analysis_prompt = f"""
+        else:  # summarized
+            prompt = f"""
             {lang_prefix}
-            You are a biotechnology expert. Provide a concise summary of the provided model fitting results.
+            
+            You are an expert in biotechnology. Provide a CONCISE but COMPLETE analysis BY EXPERIMENT.
+            
             {user_specs_section}
-            TASK: Summarize the model comparison.
-
-            1.  **Best Model:** Identify the single best model overall based on R² and RMSE.
-            2.  **Top 3 Ranking:** List the top 3 models with their R² and RMSE values.
-            3.  **Key Finding:** State the most important conclusion from the data in one or two sentences.
-            4.  **Recommendation:** Briefly recommend which model to use.
-
-            Keep the response short and to the point.
+            
+            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:
-            # --- 1. Generar el Análisis Comparativo ---
-            analysis_response = self.client.chat.completions.create(
+            # Análisis principal
+            response = self.client.chat.completions.create(
                 model=qwen_model,
                 max_tokens=4000,
                 temperature=0.3,
                 messages=[{
-                    "role": "system", "content": analysis_prompt
-                }, {
-                    "role": "user", "content": dataframe_as_text
+                    "role": "user",
+                    "content": f"{prompt}\n\n{data_summary}"
                 }]
             )
-            analysis_result = analysis_response.choices[0].message.content
-
-            # --- 2. Generar el Código de Implementación ---
+            
+            analysis_result = response.choices[0].message.content
+            
+            # Generación de código
             code_prompt = f"""
             {lang_prefix}
-            Based on the provided data, generate a complete, executable Python script.
-            The script should perform a comparative analysis of the biotechnological models.
-
-            The Python script must include:
-            1.  Loading the data into a pandas DataFrame (embed the data directly in the script).
-            2.  A function to identify the best model for each 'Experiment' or condition present in the data.
-            3.  A function to determine the overall best model across all conditions.
-            4.  Use of Matplotlib or Seaborn to generate at least two relevant plots:
-                a) A bar chart comparing the R² values of all models.
-                b) A plot showing how a key parameter (e.g., 'mu_max') changes across different experiments.
-            5.  Print statements that clearly announce the results of the analysis (e.g., "The best model for Experiment 'pH 7.0' is 'Gompertz'").
-            6.  The code should be well-commented and easy to understand.
+            
+            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.chat.completions.create(
                 model=qwen_model,
                 max_tokens=3000,
                 temperature=0.1,
                 messages=[{
-                    "role": "system", "content": code_prompt
-                }, {
-                    "role": "user", "content": dataframe_as_text
+                    "role": "user",
+                    "content": code_prompt
                 }]
             )
+            
             code_result = code_response.choices[0].message.content
-
+            
             return {
+                "tipo": "Comparative Analysis of Mathematical Models",
                 "analisis_completo": analysis_result,
                 "codigo_implementacion": code_result,
+                "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:
-            print(f"Error during AI analysis: {e}")
+            print(f"Error en análisis: {str(e)}")
             return {"error": str(e)}
 
-# --- Lógica Principal de la Aplicación ---
-
-# Estado global para almacenar los últimos resultados
-app_state = {
-    "current_analysis": "",
-    "current_code": ""
-}
-
-def process_uploaded_files(files: List, qwen_model: str, detail_level: str,
-                           language: str, additional_specs: str, progress=gr.Progress()) -> Tuple[str, str]:
-    """
-    Función principal que orquesta el procesamiento de archivos y el análisis.
-    """
-    if not files:
-        error_msg = TRANSLATIONS[language]['error_no_files']
-        return error_msg, ""
-
-    if client is None:
-        return TRANSLATIONS[language]['error_no_api'], ""
-
-    progress(0, desc=TRANSLATIONS[language]['loading'])
-    
+def process_files(files, qwen_model: str, detail_level: str = "detailed", 
+                 language: str = "en", additional_specs: str = "") -> Tuple[str, str]:
+    """Procesa múltiples archivos usando Qwen"""
     processor = FileProcessor()
-    analyzer = AIAnalyzer(client)
+    analyzer = AIAnalyzer(client, model_registry)
+    results = []
+    all_code = []
     
-    all_analyses = []
-    all_codes = []
-
-    for i, file in enumerate(files):
-        file_path = Path(file.name)
-        file_ext = file_path.suffix.lower()
+    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()
         
-        progress((i + 1) / len(files), desc=f"Processing {file_path.name}...")
-
-        try:
-            with open(file_path, 'rb') as f:
-                file_content = f.read()
-
-            df = None
+        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)
-            elif file_ext in ['.xlsx', '.xls']:
-                df = processor.read_excel(file_content)
             else:
-                all_analyses.append(f"## ⚠️ Unsupported file: {file_path.name}\nOnly CSV and Excel files are supported for analysis.")
-                continue
-
+                df = processor.read_excel(file_content)
+            
             if df is not None:
-                # El DataFrame se ha leído correctamente, ahora se pasa al analizador
-                analysis_result = analyzer.analyze_fitting_results(
-                    data=df,
-                    qwen_model=qwen_model,
-                    detail_level=detail_level,
-                    language=language,
-                    additional_specs=additional_specs
-                )
-
-                if "error" in analysis_result:
-                    all_analyses.append(f"## ❌ Error analyzing {file_path.name}\n\n{analysis_result['error']}")
-                else:
-                    header = TRANSLATIONS[language]['comparative_analysis']
-                    all_analyses.append(f"## {header}: {file_path.name}\n\n{analysis_result.get('analisis_completo', '')}")
-                    all_codes.append(f"# Code generated for: {file_path.name}\n{analysis_result.get('codigo_implementacion', '')}")
-            else:
-                all_analyses.append(f"## ❌ Could not read file: {file_path.name}")
+                analysis_type = analyzer.detect_analysis_type(df)
+                
+                if analysis_type == AnalysisType.FITTING_RESULTS:
+                    result = analyzer.analyze_fitting_results(
+                        df, qwen_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
 
-        except Exception as e:
-            all_analyses.append(f"## ❌ Critical error processing {file_path.name}: {e}")
+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
 
-    # Unir todos los resultados
-    final_analysis = "\n\n---\n\n".join(all_analyses)
-    final_code = "\n\n# " + "="*70 + "\n\n".join(all_codes)
+# Visualization configuration
+plt.style.use('seaborn-v0_8-darkgrid')
+sns.set_palette("husl")
 
-    # Almacenar en el estado de la app para exportación
-    app_state["current_analysis"] = final_analysis
-    app_state["current_code"] = final_code
+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
 
-    return final_analysis, final_code
+# 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"
 
-def export_report_action(export_format: str, language: str) -> Tuple[str, str]:
-    """Exporta el reporte al formato seleccionado usando el estado guardado."""
-    if not app_state["current_analysis"]:
-        error_msg = TRANSLATIONS[language].get('error_no_files', 'No analysis available to export')
-        return gr.update(value=error_msg, visible=True), gr.update(visible=False)
+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")
     
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as tmpfile:
-        filename_base = f"biotech_analysis_report_{timestamp}"
+    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)
         
-        try:
-            if export_format == "DOCX":
-                report_filename = f"{filename_base}.docx"
-                ReportExporter.export_to_docx(app_state["current_analysis"], report_filename, language)
-                file_to_download = report_filename
-            else: # PDF
-                report_filename = f"{filename_base}.pdf"
-                ReportExporter.export_to_pdf(app_state["current_analysis"], report_filename, language)
-                file_to_download = report_filename
-            
-            success_msg = f"{TRANSLATIONS[language]['report_exported']} {file_to_download}"
-            return gr.update(value=success_msg, visible=True), gr.update(value=file_to_download, visible=True)
+        success_msg = TRANSLATIONS[language]['report_exported']
+        return f"{success_msg} {filename}", filename
+    except Exception as e:
+        return f"Error: {str(e)}", ""
 
-        except Exception as e:
-            return gr.update(value=f"Error exporting report: {e}", visible=True), gr.update(visible=False)
-
-# --- Interfaz de Gradio ---
+# Interfaz Gradio con soporte multiidioma y temas
 def create_interface():
-    current_lang = "en" # Default language
-
-    def update_ui_language(language):
-        """Actualiza todos los textos de la UI al cambiar de idioma."""
-        nonlocal current_lang
-        current_lang = language
-        t = TRANSLATIONS.get(language, TRANSLATIONS['en'])
-        
-        return (
-            gr.update(value=f"# {t['title']}"),
-            gr.update(value=t['subtitle']),
-            gr.update(label=t['upload_files']),
-            gr.update(label=t['select_model']),
-            gr.update(label=t['select_language']),
-            gr.update(label=t['select_theme']),
-            gr.update(label=t['detail_level']),
-            gr.update(label=t['additional_specs'], placeholder=t['additional_specs_placeholder']),
-            gr.update(value=t['analyze_button']),
-            gr.update(label=t['export_format']),
-            gr.update(value=t['export_button']),
-            gr.update(label=t['comparative_analysis']),
-            gr.update(label=t['implementation_code']),
-            gr.update(label=t['data_format']),
-            gr.update(label=t['examples']),
-        )
-
-    with gr.Blocks(theme=THEMES['light']) as demo:
-        # Definición de componentes de la UI
-        title_text = gr.Markdown(f"# {TRANSLATIONS[current_lang]['title']}")
-        subtitle_text = gr.Markdown(TRANSLATIONS[current_lang]['subtitle'])
+    # 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):
-                # Columna de Controles
                 files_input = gr.File(
-                    label=TRANSLATIONS[current_lang]['upload_files'],
+                    label=TRANSLATIONS[current_language]['upload_files'],
                     file_count="multiple",
-                    file_types=[".csv", ".xlsx", ".xls"],
+                    file_types=[".csv", ".xlsx", ".xls", ".pdf", ".zip"],
                     type="filepath"
                 )
                 
                 model_selector = gr.Dropdown(
-                    choices=["Qwen/Qwen3-14B", "Qwen/Qwen3-7B", "Qwen/Qwen1.5-14B"],
+                    choices=list(QWEN_MODELS.keys()),
                     value="Qwen/Qwen3-14B",
-                    label=TRANSLATIONS[current_lang]['select_model']
+                    label=TRANSLATIONS[current_language]['select_model'],
+                    info=f"{TRANSLATIONS[current_language]['best_for']}: {QWEN_MODELS['Qwen/Qwen3-14B']['best_for']}"
                 )
                 
                 detail_level = gr.Radio(
-                    choices=[("Detailed", "detailed"), ("Summarized", "summarized")],
+                    choices=[
+                        (TRANSLATIONS[current_language]['detailed'], "detailed"),
+                        (TRANSLATIONS[current_language]['summarized'], "summarized")
+                    ],
                     value="detailed",
-                    label=TRANSLATIONS[current_lang]['detail_level']
+                    label=TRANSLATIONS[current_language]['detail_level']
                 )
-
+                
+                # Nueva entrada para especificaciones adicionales
                 additional_specs = gr.Textbox(
-                    label=TRANSLATIONS[current_lang]['additional_specs'],
-                    placeholder=TRANSLATIONS[current_lang]['additional_specs_placeholder'],
-                    lines=3
+                    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_lang]['analyze_button'], variant="primary")
+                analyze_btn = gr.Button(
+                    TRANSLATIONS[current_language]['analyze_button'],
+                    variant="primary",
+                    size="lg"
+                )
                 
-                with gr.Accordion(label=TRANSLATIONS[current_lang]['data_format'], open=False) as data_format_accordion:
-                    gr.Markdown("""
-                    ### Expected CSV/Excel Structure:
-                    The file should contain columns representing different models and their fitting metrics.
-                    | Experiment | Model       | Type      | R2    | RMSE   | AIC     | mu_max | Ks   |
-                    |------------|-------------|-----------|-------|--------|---------|--------|------|
-                    | pH_7.0     | Monod       | Biomass   | 0.985 | 0.023  | -45.2   | 0.45   | 2.1  |
-                    | pH_7.0     | Logistic    | Biomass   | 0.976 | 0.031  | -42.1   | 0.42   | -    |
-                    | pH_7.5     | Monod       | Biomass   | 0.978 | 0.027  | -44.1   | 0.43   | 2.2  |
-                    
-                    **Key Columns:** `Model`, `R2`, `RMSE`. Optional: `Experiment`, `Type`, and parameter columns.
-                    """)
+                gr.Markdown("---")
                 
-                examples_accordion = gr.Accordion(label=TRANSLATIONS[current_lang]['examples'], open=True)
-                # La funcionalidad de `gr.Examples` requiere archivos en el servidor.
-                # Se puede agregar si se alojan archivos de ejemplo.
-
-            with gr.Column(scale=2):
-                # Columna de Resultados
-                analysis_output = gr.Markdown(label=TRANSLATIONS[current_lang]['comparative_analysis'])
-                code_output = gr.Code(label=TRANSLATIONS[current_lang]['implementation_code'], language="python")
+                export_format = gr.Radio(
+                    choices=["DOCX", "PDF"],
+                    value="PDF",
+                    label=TRANSLATIONS[current_language]['export_format']
+                )
                 
-                gr.Markdown("---")
+                export_btn = gr.Button(
+                    TRANSLATIONS[current_language]['export_button'],
+                    variant="secondary"
+                )
                 
-                with gr.Row():
-                    export_format = gr.Radio(["PDF", "DOCX"], value="PDF", label=TRANSLATIONS[current_lang]['export_format'])
-                    export_btn = gr.Button(TRANSLATIONS[current_lang]['export_button'])
+                export_status = gr.Textbox(
+                    label="Export Status",
+                    interactive=False,
+                    visible=False
+                )
                 
-                export_status = gr.Textbox(label="Export Status", interactive=False, visible=False)
-                export_file = gr.File(label="Download Report", visible=False, interactive=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
+                )
         
-        with gr.Row():
-            language_selector = gr.Dropdown(
-                choices=[("English", "en"), ("Español", "es"), ("Français", "fr"), ("Deutsch", "de"), ("Português", "pt")],
-                value="en", label="Language", interactive=True
-            )
-            theme_selector = gr.Dropdown(
-                choices=[("Light", "light"), ("Dark", "dark")],
-                value="light", label="Theme", interactive=True
-            )
-
-        # Lógica de Eventos
-        analyze_btn.click(
-            fn=process_uploaded_files,
-            inputs=[files_input, model_selector, detail_level, language_selector, additional_specs],
-            outputs=[analysis_output, code_output]
+        data_format_accordion = gr.Accordion(
+            label=TRANSLATIONS[current_language]['data_format'],
+            open=False
         )
-
-        export_btn.click(
-            fn=export_report_action,
-            inputs=[export_format, language_selector],
-            outputs=[export_status, export_file]
+        
+        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"], "Qwen/Qwen3-14B", "detailed", ""],
+                [["examples/substrate_kinetics_results.xlsx"], "Qwen/Qwen3-14B", "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(
-            fn=update_ui_language,
-            inputs=language_selector,
+            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, examples_accordion
+                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
             ]
         )
         
-        # El cambio de tema en Gradio 4+ se maneja a nivel de Blocks, requiere recarga.
-        # Esta es una forma de notificar al usuario.
-        theme_selector.change(None, theme_selector, js="""
-        (theme) => {
-            if (theme == 'dark') {
-                document.body.classList.add('dark');
-            } else {
-                document.body.classList.remove('dark');
-            }
-            return theme;
-        }
-        """)
-
+        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
 
-# --- Punto de Entrada Principal ---
-if __name__ == "__main__":
+# Función principal
+def main():
     if not os.getenv("NEBIUS_API_KEY"):
-        print("CRITICAL ERROR: NEBIUS_API_KEY environment variable not set.")
-        # Interfaz de error si no hay clave API
-        with gr.Blocks() as error_demo:
-            gr.Markdown("# Configuration Error")
-            gr.Markdown(TRANSLATIONS['en']['error_no_api'])
-        error_demo.launch()
-    else:
-        app_interface = create_interface()
-        app_interface.launch()
+        print("⚠️ Configure NEBIUS_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
+        )