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C2MV commited on Jun 10

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Update app.py

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@@ -18,309 +18,788 @@ 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 el cliente de OpenAI para Nebius AI (Qwen)
-client = OpenAI(
-    base_url="https://api.studio.nebius.com/v1/",
-    api_key=os.environ.get("NEBIUS_API_KEY")
-)
-# Sistema de traducción (sin cambios)
 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': '🤖 AI 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': '💻 Full Implementation Code (AI-Generated)',
-        'data_format': '📋 Expected data format',
-        'examples': '📚 Analysis examples',
-        'light': 'Light',
-        'dark': 'Dark',
-        'best_for': 'Best for',
-        'loading': 'Loading...',
-        'error_no_api': 'Please configure NEBIUS_API_KEY in your environment 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 de IA',
-        '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 Completo (Generado por IA)',
-        '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 NEBIUS_API_KEY en los secretos de tu entorno',
-        '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...'
-    },
 }
-# Temas y Clases de Estructura (sin cambios)
-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")}
-class AnalysisType(Enum): MATHEMATICAL_MODEL = "mathematical_model"; DATA_FITTING = "data_fitting"; FITTING_RESULTS = "fitting_results"; UNKNOWN = "unknown"
 @dataclass
-class MathematicalModel: name: str; equation: str; parameters: List[str]; application: str; sources: List[str]; category: str; biological_meaning: str
 class ModelRegistry:
-    def __init__(self): self.models = {}; self._initialize_default_models()
     def register_model(self, model: MathematicalModel):
-        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: return self.models.get(category, {}).get(name)
-    def get_all_models(self) -> Dict: return self.models
     def _initialize_default_models(self):
-        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"))
 model_registry = ModelRegistry()
-AI_MODELS = {"Qwen/Qwen3-14B": {"name": "Qwen 3 14B (Nebius)", "description": "Modelo potente de la serie Qwen, accedido vía Nebius AI.", "max_tokens": 8000, "best_for": "Análisis complejos y generación de código detallado."}}
 class FileProcessor:
     @staticmethod
-    def read_csv(csv_file) -> pd.DataFrame:
-        try: return pd.read_csv(io.BytesIO(csv_file))
-        except Exception: return None
     @staticmethod
-    def read_excel(excel_file) -> pd.DataFrame:
-        try: return pd.read_excel(io.BytesIO(excel_file))
-        except Exception: return None
 class ReportExporter:
     @staticmethod
     def export_to_docx(content: str, filename: str, language: str = 'en') -> str:
         doc = Document()
-        doc.add_heading(TRANSLATIONS[language]['title'], 0)
-        doc.add_paragraph(f"{TRANSLATIONS[language]['generated_on']}: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-        # ... (lógica de exportación completa)
         doc.save(filename)
         return filename
     @staticmethod
     def export_to_pdf(content: str, filename: str, language: str = 'en') -> str:
         doc = SimpleDocTemplate(filename, pagesize=letter)
-        # ... (lógica de exportación completa)
-        doc.build([])
         return filename
 class AIAnalyzer:
-    def __init__(self, client: OpenAI, model_registry: ModelRegistry):
         self.client = client
         self.model_registry = model_registry
     def get_language_prompt_prefix(self, language: str) -> str:
-        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, ai_model: str, detail_level: str = "detailed",
-                              language: str = "en", additional_specs: str = "") -> Dict:
-        data_summary = f"FITTING RESULTS DATA:\n\n{data.to_string()}"
-        lang_prefix = self.get_language_prompt_prefix(language)
-        user_specs_section = f"\n\nUSER ADDITIONAL SPECIFICATIONS:\n{additional_specs}" if additional_specs else ""
-        # Prompt para el análisis de texto (sin cambios)
-        analysis_prompt = f"{lang_prefix}\nYou are an expert in biotechnology and mathematical modeling. Analyze these model fitting results.\n{user_specs_section}\nDETAIL LEVEL: {detail_level.upper()}\n\nProvide a comprehensive comparative analysis based on the provided data. Structure your response clearly using Markdown. Identify the best models for each experimental condition and justify your choices with metrics like R² and RMSE. Conclude with overall recommendations.\n\n{data_summary}"
-        # --- CAMBIO 1: Prompt de generación de código mejorado y más exigente ---
-        code_prompt = f"""
-        {lang_prefix}
-        Based on the following data, generate a SINGLE, COMPLETE, and EXECUTABLE Python script.
-        **Requirements for the script:**
-        1.  **Self-Contained:** The script must be runnable as-is. It should NOT require any external CSV/Excel files.
-        2.  **Embed Data:** You MUST embed the provided data directly into the script, for example, by creating a pandas DataFrame from a dictionary or a multiline string.
-        3.  **Full Analysis:** The script should perform a complete analysis similar to the text report:
-            - Identify the best model (based on R²) for each 'Experiment' and 'Type' (Biomass, Substrate, Product).
-            - Print a clear summary table of the findings.
-        4.  **Visualization:** The script MUST generate at least one publication-quality plot using Matplotlib or Seaborn to visually compare the performance (e.g., R² values) of the best models across different experiments. The plot must be clearly labeled.
-        5.  **Code Quality:** Use clear variable names, comments, and functions or a class structure to organize the code logically.
-        6.  **No Placeholders:** Do not use placeholder comments like '# Add visualization here'. Implement the full functionality.
-        **Data to use:**
-        ```
-        {data.to_string()}
-        ```
-        Generate only the Python code, starting with `import pandas as pd`.
-        """
         try:
-            # Llamada para el análisis de texto
             analysis_response = self.client.chat.completions.create(
-                model=ai_model,
                 max_tokens=4000,
                 temperature=0.6,
-                messages=[{"role": "user", "content": analysis_prompt}]
             )
             analysis_text = analysis_response.choices[0].message.content
-            # Llamada para la generación de código
             code_response = self.client.chat.completions.create(
-                model=ai_model,
-                max_tokens=4000,
-                temperature=0.4, # Ligeramente más determinista para el código
-                messages=[{"role": "user", "content": code_prompt}]
             )
             code_text = code_response.choices[0].message.content
-            # Limpiar el código si viene envuelto en ```python ... ```
-            if code_text.strip().startswith("```python"):
-                code_text = code_text.strip()[9:]
-                if code_text.strip().endswith("```"):
-                    code_text = code_text.strip()[:-3]
             return {
                 "analisis_completo": analysis_text,
                 "codigo_implementacion": code_text,
             }
         except Exception as e:
-            return {"error": str(e)}
-def process_files(files, ai_model: str, detail_level: str = "detailed",
-                 language: str = "en", additional_specs: str = "") -> Tuple[str, str]:
-    processor = FileProcessor()
-    analyzer = AIAnalyzer(client, model_registry)
-    all_analysis = []
-    all_code = []
     if not files:
-        return TRANSLATIONS[language]['error_no_files'], ""
-    for file in files:
-        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']:
-            all_analysis.append(f"## 📊 {TRANSLATIONS[language]['comparative_analysis']}: {file_name}")
-            df = processor.read_csv(file_content) if file_ext == '.csv' else processor.read_excel(file_content)
             if df is not None:
-                result = analyzer.analyze_fitting_results(df, ai_model, detail_level, language, additional_specs)
                 if "error" in result:
-                    all_analysis.append(f"An error occurred: {result['error']}")
                 else:
-                    all_analysis.append(result.get("analisis_completo", "No analysis generated."))
-                    # --- CAMBIO 2: Usar siempre el código de la IA, sin fallback ---
-                    all_code.append(f"# Code generated for file: {file_name}\n" + result.get("codigo_implementacion", "# No code was generated for this file."))
             else:
-                all_analysis.append("Could not read the file content.")
-        all_analysis.append("\n---\n")
-    final_analysis = "\n".join(all_analysis)
-    final_code = "\n\n".join(all_code)
     return final_analysis, final_code
-# --- CAMBIO 3: La función `generate_implementation_code` ha sido eliminada por completo. ---
-# Estado de la aplicación y función de exportación (sin cambios)
 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]:
-    # ... (lógica de exportación sin cambios)
-    pass
 def create_interface():
     current_language = "en"
-    def update_interface_language(language):
         app_state.current_language = language
         t = TRANSLATIONS[language]
-        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'])]
     def process_and_store(files, model, detail, language, additional_specs):
         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['light']) as demo:
-        # Definición de la UI (sin cambios estructurales, solo etiquetas)
-        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):
-                language_selector = gr.Dropdown(choices=[("English", "en"), ("Español", "es")], 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"], type="filepath")
-                default_model = "Qwen/Qwen3-14B"
-                model_selector = gr.Dropdown(choices=list(AI_MODELS.keys()), value=default_model, label=TRANSLATIONS[current_language]['select_model'], info=f"{TRANSLATIONS[current_language]['best_for']}: {AI_MODELS[default_model]['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'])
-                additional_specs = gr.Textbox(label=TRANSLATIONS[current_language]['additional_specs'], placeholder=TRANSLATIONS[current_language]['additional_specs_placeholder'], lines=3, interactive=True)
-                analyze_btn = gr.Button(TRANSLATIONS[current_language]['analyze_button'], variant="primary", size="lg")
-                # ... (resto de botones de exportación)
-            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=25) # Más líneas para el código completo
-        # ... (resto de la UI y eventos)
-        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])
-        analyze_btn.click(fn=process_and_store, inputs=[files_input, model_selector, detail_level, language_selector, additional_specs], outputs=[analysis_output, code_output])
     return demo
 def main():
-    if not os.getenv("NEBIUS_API_KEY"):
-        print("⚠️ Configure NEBIUS_API_KEY in your environment secrets")
-        return gr.Interface(fn=lambda x: TRANSLATIONS['en']['error_no_api'], inputs=gr.Textbox(), outputs=gr.Textbox(), title="Configuration Error")
     return create_interface()
 if __name__ == "__main__":
     demo = main()
     if demo:
-        demo.launch(server_name="0.0.0.0", server_port=7860)

 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
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
 from reportlab.lib.units import inch
 import matplotlib.pyplot as plt
 from datetime import datetime
+# --- Configuración para la API de Qwen ---
+# Configuración de Gradio
 os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+# Inicializar cliente OpenAI para apuntar a la API de Nebius (Qwen)
+try:
+    if "NEBIUS_API_KEY" not in os.environ:
+        print("⚠️ ADVERTENCIA: La variable de entorno NEBIUS_API_KEY no está configurada. El analizador no funcionará.")
+        client = None
+    else:
+        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 OpenAI para Nebius: {e}")
+    client = None
+# --- Sistema de Traducción Completo ---
 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': '🤖 Qwen 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 NEBIUS_API_KEY in the environment variables or 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 Qwen',
+'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 NEBIUS_API_KEY en las variables de entorno o 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 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 ---
+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"
+)
 }
+# --- Clases de Datos y Estructuras ---
+class AnalysisType(Enum):
+    MATHEMATICAL_MODEL = "mathematical_model"
+    DATA_FITTING = "data_fitting"
+    FITTING_RESULTS = "fitting_results"
+    UNKNOWN = "unknown"
 @dataclass
+class MathematicalModel:
+    name: str
+    equation: str
+    parameters: List[str]
+    application: str
+    sources: List[str]
+    category: str
+    biological_meaning: str
 class ModelRegistry:
+    def __init__(self):
+        self.models = {}
+        self._initialize_default_models()
     def register_model(self, model: MathematicalModel):
+        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) -> Optional[MathematicalModel]:
+        return self.models.get(category, {}).get(name)
+    def get_all_models(self) -> Dict:
+        return self.models
     def _initialize_default_models(self):
+        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"
+        ))
 model_registry = ModelRegistry()
+QWEN_MODELS = {
+    "Qwen/Qwen3-14B": {
+        "name": "Qwen 3 14B",
+        "description": "Modelo potente y versátil de la serie Qwen.",
+        "max_tokens": 4096,
+        "best_for": "Análisis complejos y generación de código detallado."
+    }
+}
+# --- Clases de Procesamiento y Exportación ---
 class FileProcessor:
     @staticmethod
+    def extract_text_from_pdf(pdf_file: bytes) -> str:
+        try:
+            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
+            text = ""
+            for page in pdf_reader.pages:
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text + "\n"
+            return text
+        except Exception as e:
+            return f"Error reading PDF: {str(e)}"
     @staticmethod
+    def read_csv(csv_file: bytes) -> Optional[pd.DataFrame]:
+        try:
+            return pd.read_csv(io.BytesIO(csv_file))
+        except Exception:
+            return None
+    @staticmethod
+    def read_excel(excel_file: bytes) -> Optional[pd.DataFrame]:
+        try:
+            return pd.read_excel(io.BytesIO(excel_file))
+        except Exception:
+            return None
+    @staticmethod
+    def extract_from_zip(zip_file: bytes) -> List[Tuple[str, bytes]]:
+        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') and not file_name.endswith('/'):
+                        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:
     @staticmethod
     def export_to_docx(content: str, filename: str, language: str = 'en') -> str:
         doc = Document()
+        title_style = doc.styles['Title']
+        title_style.font.size = Pt(24)
+        title_style.font.bold = True
+        heading1_style = doc.styles['Heading 1']
+        heading1_style.font.size = Pt(18)
+        heading1_style.font.bold = True
+        title_text = TRANSLATIONS[language]['title']
+        doc.add_heading(title_text, 0)
+        date_text = {'en': 'Generated on', 'es': 'Generado el'}
+        doc.add_paragraph(f"{date_text.get(language, date_text['en'])}: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        doc.add_paragraph()
+        lines = content.split('\n')
+        for line in lines:
+            line = line.strip()
+            if line.startswith('###'):
+                doc.add_heading(line.replace('###', '').strip(), level=3)
+            elif line.startswith('##'):
+                doc.add_heading(line.replace('##', '').strip(), level=2)
+            elif line.startswith('#'):
+                doc.add_heading(line.replace('#', '').strip(), level=1)
+            elif line.startswith('**') and line.endswith('**'):
+                p = doc.add_paragraph()
+                p.add_run(line.replace('**', '')).bold = True
+            elif line.startswith('- ') or line.startswith('* '):
+                doc.add_paragraph(line[2:], style='List Bullet')
+            elif line:
+                doc.add_paragraph(line)
         doc.save(filename)
         return filename
     @staticmethod
     def export_to_pdf(content: str, filename: str, language: str = 'en') -> str:
         doc = SimpleDocTemplate(filename, pagesize=letter)
+        story = []
+        styles = getSampleStyleSheet()
+        title_style = ParagraphStyle('CustomTitle', parent=styles['Title'], fontSize=24, textColor=colors.HexColor('#1f4788'), spaceAfter=20)
+        heading_style = ParagraphStyle('CustomHeading1', parent=styles['Heading1'], fontSize=16, textColor=colors.HexColor('#2e5090'), spaceAfter=12)
+        title_text = TRANSLATIONS[language]['title']
+        story.append(Paragraph(title_text, title_style))
+        date_text = {'en': 'Generated on', 'es': 'Generado el'}
+        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, 0.3 * inch))
+        lines = content.split('\n')
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+            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('**'):
+                story.append(Paragraph(f"<b>{line.replace('**', '')}</b>", styles['Normal']))
+            elif line.startswith('- ') or line.startswith('* '):
+                story.append(Paragraph(f"• {line[2:]}", styles['Normal'], bulletText='•'))
+            else:
+                # Limpiar caracteres que pueden dar problemas en ReportLab
+                clean_line = line.replace('🧬', '[DNA]').replace('🤖', '[BOT]').replace('📁', '[FILE]').replace('🚀', '[ROCKET]').replace('📊', '[GRAPH]').replace('💻', '[CODE]').replace('💾', '[SAVE]').replace('🎯', '[TARGET]').replace('🔍', '[SEARCH]').replace('💡', '[TIP]')
+                story.append(Paragraph(clean_line, styles['Normal']))
+            story.append(Spacer(1, 0.1 * inch))
+        doc.build(story)
         return filename
+# --- Clase del Analizador de IA con Qwen ---
 class AIAnalyzer:
+    def __init__(self, client, model_registry):
         self.client = client
         self.model_registry = model_registry
     def get_language_prompt_prefix(self, language: str) -> str:
+        return TRANSLATIONS.get(language, TRANSLATIONS['en']).get('response_prefix', f"Please respond in {language}. ")
+    def analyze_fitting_results(self, data: pd.DataFrame, qwen_model: str, detail_level: str, language: str, additional_specs: str) -> Dict:
+        if self.client is None:
+            return {"error": "AI client not initialized. Check NEBIUS_API_KEY."}
+        data_summary = f"FITTING RESULTS DATA:\n- Columns: {list(data.columns)}\n- Number of models/entries: {len(data)}\n- Full Data:\n{data.to_string()}"
+        lang_prefix = self.get_language_prompt_prefix(language)
+        user_specs_section = f"\nADDITIONAL USER SPECIFICATIONS: {additional_specs}\n" if additional_specs else ""
+        if detail_level == "detailed":
+            prompt = f"""{lang_prefix}{user_specs_section}
+            You are an expert biotechnologist and data scientist. Analyze the provided model fitting results in detail.
+            1.  **Overall Summary:** Briefly describe the dataset (number of experiments, models, metrics).
+            2.  **Analysis by Experiment/Condition:** For each unique experiment/condition found in the data:
+                - Identify the best performing model for each category (e.g., Biomass, Substrate, Product) based on R2 (higher is better) and RMSE (lower is better).
+                - List the best model's name, its key metrics (R2, RMSE), and its fitted parameters.
+                - Provide a brief biological interpretation of the parameters for the best model in that context.
+            3.  **Overall Best Models:** Identify the most robust models across all experiments for each category. Justify your choice (e.g., "Model X was best in 3 out of 4 experiments").
+            4.  **Parameter Trends:** If possible, comment on how key parameters (like μmax, Ks) change across different experimental conditions.
+            5.  **Conclusion and Recommendations:** Summarize the findings and recommend which models to use for future predictions under specific conditions.
+            Format the entire response using clear Markdown headers, lists, and bold text."""
+        else: # summarized
+            prompt = f"""{lang_prefix}{user_specs_section}
+            You are an expert biotechnologist. Provide a concise, summarized analysis of the provided model fitting results.
+            1.  **Best Models Summary Table:** Create a table that shows the best model for each Experiment and Type (Biomass, Substrate, etc.) along with its R2 value.
+            2.  **Overall Winners:** State the single best model for Biomass, Substrate, and Product across all experiments.
+            3.  **Key Insights:** In 2-3 bullet points, what are the most important findings? (e.g., "Gompertz model consistently outperforms others for biomass.", "μmax is highest at pH 7.5.").
+            Format as a brief and clear Markdown report."""
         try:
+            # Análisis principal
             analysis_response = self.client.chat.completions.create(
+                model=qwen_model,
+                messages=[{"role": "user", "content": f"{prompt}\n\n{data_summary}"}],
                 max_tokens=4000,
                 temperature=0.6,
+                top_p=0.95
             )
             analysis_text = analysis_response.choices[0].message.content
+            # Generación de código
+            code_prompt = f"""{lang_prefix}
+            Based on the provided data, generate a single, complete, and executable Python script for analysis.
+            The script must:
+            1.  Contain the provided data hardcoded into a pandas DataFrame.
+            2.  Define functions to analyze the data to find the best model per experiment and type.
+            3.  Include functions to create visualizations using matplotlib or seaborn, such as:
+                - A bar chart comparing the R2 values of the best models across experiments.
+                - A summary table of the best models.
+            4.  Have a `if __name__ == "__main__":` block that runs the analysis and shows the plots.
+            The code should be well-commented and self-contained."""
             code_response = self.client.chat.completions.create(
+                model=qwen_model,
+                messages=[{"role": "user", "content": f"{code_prompt}\n\n{data_summary}"}],
+                max_tokens=3500,
+                temperature=0.4, # Más determinista para código
+                top_p=0.95
             )
             code_text = code_response.choices[0].message.content
             return {
                 "analisis_completo": analysis_text,
                 "codigo_implementacion": code_text,
             }
         except Exception as e:
+            return {"error": f"An error occurred with the AI API: {str(e)}"}
+# --- Lógica de la Aplicación ---
+def process_files(files, model_name: str, detail_level: str, language: str, additional_specs: str) -> Tuple[str, str]:
     if not files:
+        return TRANSLATIONS[language]['error_no_files'], "Please upload files first."
+    if client is None:
+        return TRANSLATIONS[language]['error_no_api'], "AI client is not configured."
+    analyzer = AIAnalyzer(client, model_registry)
+    all_analysis_parts = []
+    all_code_parts = []
+    for file in files:
+        try:
+            with open(file.name, 'rb') as f:
+                file_content = f.read()
+            file_name = os.path.basename(file.name)
+            file_ext = Path(file_name).suffix.lower()
+            df = None
+            if file_ext == '.csv':
+                df = FileProcessor.read_csv(file_content)
+            elif file_ext in ['.xlsx', '.xls']:
+                df = FileProcessor.read_excel(file_content)
             if df is not None:
+                all_analysis_parts.append(f"# Analysis for: {file_name}")
+                result = analyzer.analyze_fitting_results(df, model_name, detail_level, language, additional_specs)
                 if "error" in result:
+                    all_analysis_parts.append(f"An error occurred: {result['error']}")
                 else:
+                    all_analysis_parts.append(result.get("analisis_completo", "No analysis generated."))
+                    all_code_parts.append(f"# Code generated from: {file_name}\n{result.get('codigo_implementacion', '# No code generated.')}")
             else:
+                 all_analysis_parts.append(f"# Could not process file: {file_name}")
+        except Exception as e:
+            all_analysis_parts.append(f"# Error processing {file.name}: {str(e)}")
+    final_analysis = "\n\n---\n\n".join(all_analysis_parts)
+    final_code = "\n\n" + "="*80 + "\n\n".join(all_code_parts) if all_code_parts else generate_implementation_code(final_analysis)
     return final_analysis, final_code
+def generate_implementation_code(analysis_results: str) -> str:
+    # This is a fallback in case the API fails to generate code.
+    # It provides a generic template.
+    return """
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+def analyze_data(df):
+    \"\"\"
+    Analyzes the dataframe to find the best model per experiment and type.
+    This is a template function. You may need to adapt it to your specific column names.
+    \"\"\"
+    # Assuming columns 'Experiment', 'Type', 'R2', 'Model' exist
+    if not all(col in df.columns for col in ['Experiment', 'Type', 'R2', 'Model']):
+        print("DataFrame is missing required columns: 'Experiment', 'Type', 'R2', 'Model'")
+        return None
+    # Find the index of the max R2 for each group
+    best_models_idx = df.loc[df.groupby(['Experiment', 'Type'])['R2'].idxmax()]
+    print("--- Best Models by Experiment and Type ---")
+    print(best_models_idx[['Experiment', 'Type', 'Model', 'R2']].to_string(index=False))
+    return best_models_idx
+def visualize_results(best_models_df):
+    \"\"\"
+    Creates a bar plot to visualize the R2 scores of the best models.
+    \"\"\"
+    if best_models_df is None:
+        print("Cannot visualize results. Analysis failed.")
+        return
+    plt.figure(figsize=(12, 7))
+    sns.barplot(data=best_models_df, x='Experiment', y='R2', hue='Type', palette='viridis')
+    plt.title('Best Model Performance (R²) by Experiment and Type', fontsize=16)
+    plt.xlabel('Experiment', fontsize=12)
+    plt.ylabel('R² Score', fontsize=12)
+    plt.xticks(rotation=45, ha='right')
+    plt.ylim(bottom=max(0, best_models_df['R2'].min() - 0.05), top=1.0)
+    plt.legend(title='Variable Type')
+    plt.tight_layout()
+    plt.show()
+if __name__ == '__main__':
+    # TODO: Replace this with your actual data
+    # This is placeholder data.
+    data = {
+        'Experiment': ['pH_7.0', 'pH_7.0', 'pH_7.5', 'pH_7.5', 'pH_7.0', 'pH_7.5'],
+        'Model': ['Monod', 'Logistic', 'Monod', 'Logistic', 'FirstOrder', 'FirstOrder'],
+        'Type': ['Biomass', 'Biomass', 'Biomass', 'Biomass', 'Substrate', 'Substrate'],
+        'R2': [0.98, 0.99, 0.97, 0.96, 0.95, 0.99],
+        'RMSE': [0.1, 0.05, 0.12, 0.15, 0.2, 0.08]
+    }
+    df = pd.DataFrame(data)
+    print("--- Input Data ---")
+    print(df)
+    print("\\n")
+    best_models = analyze_data(df)
+    visualize_results(best_models)
+"""
 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, Optional[str]]:
+    if not app_state.current_analysis:
+        error_msg = TRANSLATIONS[language]['error_no_files'].replace('analizar', 'exportar')
+        return error_msg, None
+    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 during export: {str(e)}", None
+# --- Interfaz de Gradio ---
 def create_interface():
     current_language = "en"
+    def update_interface_language(language: str):
         app_state.current_language = language
         t = TRANSLATIONS[language]
+        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'], choices=[(t['detailed'], "detailed"), (t['summarized'], "summarized")]),
+            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'])
+        ]
     def process_and_store(files, model, detail, language, additional_specs):
+        if not files:
+            return TRANSLATIONS[language]['error_no_files'], ""
         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['light']) as demo:
+        # Estructura de la UI
+        with gr.Column():
+            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):
+                    language_selector = gr.Dropdown(choices=[("English", "en"), ("Español", "es"), ("Français", "fr"), ("Deutsch", "de"), ("Português", "pt")], value=current_language, label="Language", interactive=True)
+                    theme_selector = gr.Dropdown(choices=["Light", "Dark"], value="Light", label="Theme", interactive=True, visible=False) # Theme switching is complex, hiding for now
+            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"], type="filepath")
+                    model_selector = gr.Dropdown(choices=list(QWEN_MODELS.keys()), value="Qwen/Qwen3-14B", label=TRANSLATIONS[current_language]['select_model'], info=QWEN_MODELS["Qwen/Qwen3-14B"]['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'])
+                    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=["PDF", "DOCX"], 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)
+            with gr.Accordion(label=TRANSLATIONS[current_language]['data_format'], open=False) as data_format_accordion:
+                gr.Markdown("""
+                ### Expected CSV/Excel Structure:
+                The file should contain columns representing your model fitting results. Essential columns are:
+                - **Experiment**: An identifier for the experimental condition (e.g., `pH_7.0`, `Temp_30C`).
+                - **Model**: The name of the mathematical model (e.g., `Monod`, `Logistic`).
+                - **Type**: The type of process being modeled (e.g., `Biomass`, `Substrate`, `Product`). This is crucial for categorical analysis.
+                - **R2 / R_squared**: The coefficient of determination. Higher is better.
+                - **RMSE**: Root Mean Squared Error. Lower is better.
+                - **[Parameter_Columns]**: Additional columns for each model parameter (e.g., `mu_max`, `Ks`, `Xmax`).
+                | Experiment | Model    | Type      |    R2 |   RMSE |  mu_max |
+                |:-----------|:---------|:----------|------:|-------:|--------:|
+                | pH_7.0     | Monod    | Biomass   | 0.985 |  0.023 |    0.45 |
+                | pH_7.0     | Logistic | Biomass   | 0.991 |  0.018 |    0.48 |
+                | pH_7.5     | Monod    | Biomass   | 0.978 |  0.027 |    0.43 |
+                """)
+            examples_ui = 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 and which temp is optimal."]
+                ],
+                inputs=[files_input, model_selector, detail_level, additional_specs],
+                label=TRANSLATIONS[current_language]['examples']
+            )
+        # Event Handlers
+        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, examples_ui]
+        )
+        analyze_btn.click(
+            fn=process_and_store,
+            inputs=[files_input, model_selector, detail_level, language_selector, additional_specs],
+            outputs=[analysis_output, code_output],
+            api_name="analyze"
+        )
+        def handle_export(fmt, lang):
+            status, file_path = export_report(fmt, lang)
+            if file_path:
+                return gr.update(value=status, visible=True), gr.update(value=file_path, 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 ---
 def main():
+    if client is None:
+        return gr.Interface(
+            fn=lambda: TRANSLATIONS['en']['error_no_api'],
+            inputs=[],
+            outputs=gr.Textbox(label="Error"),
+            title="Configuration Error"
+        )
     return create_interface()
 if __name__ == "__main__":
+    # Crear archivos y carpetas de ejemplo si no existen para que la UI no falle
+    if not os.path.exists("examples"):
+        os.makedirs("examples")
+    if not os.path.exists("examples/biomass_models_comparison.csv"):
+        pd.DataFrame({
+            'Experiment': ['Exp1_pH7', 'Exp1_pH7', 'Exp2_pH8', 'Exp2_pH8'],
+            'Model': ['Monod', 'Logistic', 'Monod', 'Logistic'],
+            'Type': ['Biomass', 'Biomass', 'Biomass', 'Biomass'],
+            'R2': [0.98, 0.99, 0.97, 0.96],
+            'RMSE': [0.1, 0.05, 0.12, 0.15],
+            'mu_max': [0.5, 0.52, 0.45, 0.46]
+        }).to_csv("examples/biomass_models_comparison.csv", index=False)
+    if not os.path.exists("examples/substrate_kinetics_results.xlsx"):
+         pd.DataFrame({
+            'Experiment': ['T30C', 'T30C', 'T37C', 'T37C'],
+            'Model': ['FirstOrder', 'MichaelisMenten', 'FirstOrder', 'MichaelisMenten'],
+            'Type': ['Substrate', 'Substrate', 'Substrate', 'Substrate'],
+            'R2': [0.95, 0.94, 0.99, 0.98],
+            'RMSE': [0.2, 0.25, 0.08, 0.1],
+            'Ks': [None, 1.5, None, 1.2]
+        }).to_excel("examples/substrate_kinetics_results.xlsx", index=False)
     demo = main()
     if demo:
+        demo.launch(server_name="0.0.0.0", server_port=7860, share=False)