# Aclarador Integration Complete ✅

## What Was Done

Successfully integrated Aclarador's clarity analysis system using Groq API.

### Files Modified

1. **`requirements.txt`** - Added `groq` dependency
2. **`analyzers/analyzer_wrapper.py`** - Complete rewrite to use Groq API
3. **`analyzers/aclarador/`** - Cloned from https://github.com/menpente/aclarador-clean

## How It Works

### Architecture

The analyzer uses a **two-mode approach**:

1. **Groq API Mode** (when `GROQ_API_KEY` is set)
   - Loads system prompt from `analyzers/aclarador/system_prompt.md`
   - Sends text to Groq's `llama-3.3-70b-versatile` model
   - Parses AI response for issues, suggestions, and improvements
   - Calculates clarity scores based on detected issues

2. **Fallback Mode** (when API key not set)
   - Uses simple heuristics (sentence length, word length)
   - Provides basic scoring without AI analysis
   - Useful for testing and development

### Analysis Flow

```
Text Input
    ↓
Load System Prompt (Spanish clarity principles)
    ↓
Send to Groq API with temperature=0.3
    ↓
Parse Response:
  - Extract corrected text
  - Identify issues (long sentences, complex vocab, passive voice)
  - Extract improvement suggestions
    ↓
Calculate Scores:
  - Overall Score (0-100)
  - Readability Score (based on sentence/word length)
  - Complexity Score (inverse of detected issues)
    ↓
Return Analysis Result
```

## Configuration

### For Local Development

Set environment variable:
```bash
export GROQ_API_KEY="your-groq-api-key-here"
```

### For Hugging Face Spaces

1. Go to your Space Settings
2. Navigate to "Repository secrets"
3. Add new secret:
   - Name: `GROQ_API_KEY`
   - Value: Your Groq API key
4. Restart the Space

### Getting a Groq API Key

1. Visit https://console.groq.com
2. Sign up for a free account
3. Go to API Keys section
4. Create a new API key
5. Copy the key (keep it secure!)

**Note**: Groq offers a generous free tier suitable for this project.

## Testing

### Test Without API Key (Fallback Mode)

```bash
python3 -c "
from analyzers.analyzer_wrapper import AclaradorAnalyzer

analyzer = AclaradorAnalyzer()
result = analyzer.analyze('El Ayuntamiento de Madrid establece nuevas normativas.')
print(f'Score: {result[\"overall_score\"]:.1f}')
print(f'Suggestions: {result[\"suggestions\"]}')
"
```

### Test With API Key

```bash
export GROQ_API_KEY="your-key-here"

python3 -c "
from analyzers.analyzer_wrapper import AclaradorAnalyzer

analyzer = AclaradorAnalyzer()
result = analyzer.analyze('El Ayuntamiento de Madrid establece nuevas normativas.')
print(f'Score: {result[\"overall_score\"]:.1f}')
print(f'Issues: {result[\"readability_metrics\"][\"issues_detected\"]}')
print(f'Suggestions: {result[\"suggestions\"]}')
"
```

## What the Analyzer Detects

Based on Spanish clarity principles from the system prompt:

### Issues Detected
- **Long sentences** (>30 words)
- **Complex vocabulary** (technical jargon, long words)
- **Passive voice** (voz pasiva)
- **Redundancies** (repetitive phrases)
- **Verbose style** (excessive wordiness)

### Scoring System
- **Overall Score**: Weighted average of readability and complexity
- **Readability Score**: Based on sentence/word length (optimal: 15-20 word sentences)
- **Complexity Score**: Inversely related to number of issues detected

### Output Includes
- Clarity scores (0-100, higher = clearer)
- Sentence statistics (count, average length, long sentences)
- Vocabulary statistics (total words, unique words, lexical diversity)
- Detected jargon words
- Specific improvement suggestions
- Corrected text (from Groq analysis)

## System Prompt

The analyzer uses comprehensive Spanish clarity guidelines from `system_prompt.md`:

- **Sentence Structure**: Max 30 words, one idea per sentence
- **Active Voice**: Prefer "El departamento aprobó" over "Fue aprobado por"
- **Clear Vocabulary**: Avoid unnecessary jargon
- **Effective Punctuation**: Use periods, commas, colons appropriately
- **Digital Adaptation**: Optimize for web reading and SEO

## Cost Considerations

- **Groq API**: Free tier with generous limits
- **Alternative**: Fallback mode requires no API key (reduced accuracy)
- **Hugging Face Spaces**: Completely free hosting

## Next Steps

1. **Deploy to HF Spaces**:
   ```bash
   git add .
   git commit -m "Integrate Aclarador with Groq API"
   git push
   ```

2. **Add API Key** to Space secrets (Settings → Repository secrets)

3. **Test** by triggering manual fetch in Settings tab

4. **Monitor** analysis quality in Dashboard

## Troubleshooting

### "Groq API not available"
- Install: `pip install groq`
- Or add to requirements.txt (already done)

### "GROQ_API_KEY not found"
- Set environment variable locally
- Or add to HF Spaces secrets

### "Using fallback analysis"
- This is normal when API key is not configured
- Analysis still works with basic heuristics
- Set GROQ_API_KEY for full AI-powered analysis

### Rate Limits
- Groq free tier has limits (check console.groq.com)
- Consider spacing out fetches if hitting limits
- Monitor logs in HF Spaces

## Files Structure

```
analyzers/
├── analyzer_wrapper.py          # Main integration (uses Groq)
├── aclarador/
│   ├── system_prompt.md         # Spanish clarity guidelines
│   ├── agent_coordinator.py     # (Not used - simplified approach)
│   ├── agents/                  # (Not used - simplified approach)
│   └── app.py                   # Original Streamlit app (reference)
```

## Success! 🎉

The integration is complete and ready to deploy. The analyzer will:
- ✅ Use Groq API for intelligent clarity analysis
- ✅ Fall back to heuristics when API unavailable
- ✅ Provide Spanish-specific clarity recommendations
- ✅ Generate actionable improvement suggestions
- ✅ Work seamlessly with the existing Madrid Analyzer pipeline