# Performance Optimization Recommendations for chunk.py

## 📊 Current Performance Issues:

### 🚨 Critical Problems:
1. **Text file loading**: 501 seconds (8+ minutes)
2. **Initial model loading**: 142 seconds 
3. **T5 grammar correction**: 37+ seconds
4. **Memory usage**: 3.58 GB total

### ✅ Good Performance Areas:
- Text generation: ~0.6-0.7 seconds (5.9 sentences/sec)
- spaCy caching: 11x speed improvement
- N-gram loading: 2.89 seconds

## 🔧 Optimization Strategies:

### 1. Text File Optimization
**Problem**: 37,580 lines taking 501 seconds to process
**Solutions**:
```python
# Option A: Use smaller text file for development
def load_text_sample(file_path, max_lines=5000):
    with open(file_path, 'r', encoding='utf-8') as file:
        lines = []
        for i, line in enumerate(file):
            if i >= max_lines:
                break
            lines.append(line)
        return ' '.join(lines)

# Option B: Use memory mapping for large files
import mmap
def load_text_mmap(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        with mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) as mmapped_file:
            return mmapped_file.read().decode('utf-8')

# Option C: Process in chunks
def process_text_chunks(file_path, chunk_size=1000):
    # Process file in smaller chunks to avoid memory issues
    pass
```

### 2. Model Loading Optimization
**Problem**: SpaCy model loading takes 142 seconds
**Solutions**:
```python
# Use smaller spaCy model for development
nlp = spacy.load("en_core_web_sm")  # Instead of en_core_web_md

# Disable unnecessary components
nlp = spacy.load("en_core_web_md", disable=["ner", "textcat"])

# Use blanks when possible
nlp_simple = spacy.blank("en")
```

### 3. T5 Grammar Correction Optimization
**Problem**: 37 seconds + 3GB memory for grammar correction
**Solutions**:
```python
# Option A: Use smaller T5 model
tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")

# Option B: Load T5 only when needed (current lazy loading is good)

# Option C: Use local grammar rules instead of T5 for simple cases
def quick_grammar_fix(text):
    # Simple regex-based fixes for common errors
    text = re.sub(r'\bwas examine\b', 'was examined', text)
    # Add more simple fixes
    return text
```

### 4. Memory Optimization
**Problem**: 3.58 GB memory usage
**Solutions**:
```python
# Clear cache periodically
@lru_cache(maxsize=100)  # Reduce cache size
def cached_nlp(text):
    return nlp(str(text))

# Use memory-efficient data structures
import sys
def clear_cache():
    cached_nlp.cache_clear()
    get_word_vector.cache_clear()
    get_word_pos.cache_clear()
    gc.collect()

# Process in batches
def process_sentences_batch(sentences, batch_size=10):
    for i in range(0, len(sentences), batch_size):
        batch = sentences[i:i+batch_size]
        # Process batch
        if i % 50 == 0:  # Clear cache every 5 batches
            clear_cache()
```

## 🎯 Recommended Implementation Order:

### Phase 1: Quick Wins (5-10 minutes)
1. Reduce text file size for development
2. Use smaller spaCy model  
3. Reduce cache sizes

### Phase 2: Medium Impact (15-30 minutes)
1. Implement chunked text processing
2. Add memory clearing mechanisms
3. Optimize T5 model size

### Phase 3: Advanced Optimizations (1+ hours)
1. Implement memory mapping
2. Add streaming text processing
3. Create hybrid grammar correction system

## 📈 Expected Performance Improvements:

### After Phase 1:
- Text loading: 501s → ~30s (95% improvement)
- Memory usage: 3.58GB → ~1.5GB (60% reduction)
- Initial load: 647s → ~60s (90% improvement)

### After Phase 2:
- Text generation: 0.6s → 0.3s (50% improvement)
- Grammar correction: 37s → 15s (60% improvement)

### After Phase 3:
- Total processing: <2 minutes for full workflow
- Memory usage: <1GB steady state

## 🔧 Implementation Priority:

### HIGH PRIORITY (Immediate):
```python
# 1. Use smaller text sample
MAX_LINES = 5000  # Instead of full 37,580 lines

# 2. Use smaller spaCy model
nlp = spacy.load("en_core_web_sm")

# 3. Reduce cache sizes
@lru_cache(maxsize=100)  # Instead of 1000
```

### MEDIUM PRIORITY (Next steps):
```python
# 1. Add memory management
# 2. Optimize T5 loading
# 3. Implement batch processing
```

### LOW PRIORITY (Future):
```python
# 1. Advanced text processing
# 2. Custom grammar rules
# 3. Distributed processing
```
