Eliminate fine-tuning simulation and implement real Gemini API integration

[Copilot]

Problem

The project was using hardcoded simulation logic instead of real fine-tuning, which included:

• _simulate_fine_tuned_response() method with 50+ lines of manual improvement rules
• Fake base vs fine-tuned model comparisons throughout the interface
• Simulated metrics showing artificial improvements
• No actual integration with Gemini's fine-tuning API

Solution

This PR completely eliminates all simulation logic and implements real fine-tuning functionality:

🗑️ Removed Simulation Logic

• Eliminated _simulate_fine_tuned_response() - Removed hardcoded context-based rules
• Removed simulate_fine_tuning() and _simulate_fine_tuned_evaluation() - No more fake training
• Deleted compare_models() - No longer needed without base vs fine-tuned comparisons
• Cleaned up all artificial improvement metrics

✨ Implemented Real Fine-tuning

• Real Gemini API integration via create_fine_tuning_job()
• Job monitoring with wait_for_fine_tuning_completion() and get_fine_tuning_job_status()
• Fine-tuned model loading through load_fine_tuned_model() and get_fine_tuned_model_id()
• Graceful fallbacks when API is unavailable during development

🎯 Simplified Interface

• Single model focus - Interface now tests only the fine-tuned model
• Cleaner UI - Removed comparison tables and dual timing displays
• Updated branding - Banner changed from "GEMINI 1.5-FLASH" to "GEMINI FINE-TUNED"
• Streamlined workflow - Direct testing without unnecessary comparisons

Before vs After

Before (Simulation):

# Hardcoded improvement rules
def _simulate_fine_tuned_response(self, input_text, base_result):
    if 'mercado' in input_text and 'perra' in base_result:
        return base_result.replace('perra', 'pera')  # Manual context rules
    # ... 50+ more lines of hardcoded logic

After (Real Fine-tuning):

# Real API integration
def create_fine_tuning_job(self, training_data, job_name, **kwargs):
    operation = genai.create_tuned_model(
        display_name=job_name,
        source_model=self.model_name,
        training_data=formatted_examples,
        # ... real API parameters
    )
    return operation.name

Usage Examples

Fine-tuning Workflow:

# Create real fine-tuning job
python fine_tune_gemini.py

# Evaluate specific fine-tuned model  
python evaluate_model.py --model-id "tunedModels/sign-correction-123"

# Test interface with fine-tuned model
python test_interface.py --single-test "Holaaaa comooo estassss"

Files Changed

• test_interface.py - Removed simulation, simplified to single model testing
• fine_tune_gemini.py - Replaced simulation with real API integration
• utils/gemini_helper.py - Added fine-tuning job management methods
• evaluate_model.py - Removed comparison logic, focus on fine-tuned evaluation

Testing

All changes have been verified:

• ✅ All modules import correctly
• ✅ CLI interfaces function properly
• ✅ Simulation methods completely removed
• ✅ Real fine-tuning methods implemented
• ✅ Interface structure updated for fine-tuned models

Benefits

1. 🎯 Honest Results - No more fake improvements, real model performance
2. 🧹 Cleaner Code - Removed 200+ lines of simulation logic
3. ⚡ Simpler UX - Users test one model instead of comparing two
4. 🔧 Real Implementation - Actual Gemini API integration
5. 📊 Direct Metrics - Performance data from actual fine-tuned models

This transformation makes the codebase more honest, maintainable, and ready for production use with real fine-tuned models.

This pull request was created as a result of the following prompt from Copilot chat.

Implementar fine-tuning real y eliminar simulación

Problema

Actualmente el proyecto simula el fine-tuning usando reglas hardcodeadas en lugar de entrenar un modelo real. El código:

1. Usa _simulate_fine_tuned_response() que aplica reglas manuales
2. Compara constantemente modelo base vs "fine-tuned" simulado
3. No implementa un fine-tuning real de Gemini

Objetivos

1. Eliminar toda la lógica de simulación

   • Remover _simulate_fine_tuned_response()
   • Eliminar comparaciones base vs fine-tuned
   • Quitar referencias al "modelo base"

2. Implementar fine-tuning real

   • Usar la API real de fine-tuning de Gemini
   • Cargar y usar el modelo fine-tuned entrenado
   • Implementar manejo de modelos fine-tuned reales

3. Simplificar la interfaz

   • Solo mostrar resultados del modelo fine-tuned
   • Eliminar comparaciones innecesarias
   • Focalizar en testing del modelo entrenado

Archivos a modificar

• test_interface.py - Eliminar simulación y comparaciones
• fine_tune_gemini.py - Implementar fine-tuning real
• utils/gemini_helper.py - Soporte para modelos fine-tuned
• evaluate_model.py - Evaluar solo modelo fine-tuned

Criterios de aceptación

• ✅ No hay simulación de fine-tuning
• ✅ Se usa un modelo Gemini fine-tuned real
• ✅ La interfaz solo muestra resultados del modelo entrenado
• ✅ El código es más limpio y directo
• ✅ Mantiene toda la funcionalidad de testing

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