# DynamicCache Error Fix - Quick Summary

## Problem
```
ERROR: Local model error: 'DynamicCache' object has no attribute 'seen_tokens'
```

**Result**: Quality Score 0.00 for all transcripts, no analysis extracted.

---

## Root Cause
Version incompatibility in transformers library's caching mechanism during model generation.

---

## ✅ Fixes Applied

### 1. Code Fix (llm.py)
Added `use_cache=False` parameter to disable problematic caching:

```python
outputs = query_llm_local.model.generate(
    **inputs,
    max_new_tokens=max_tokens,
    temperature=temperature,
    do_sample=temperature > 0,
    pad_token_id=query_llm_local.tokenizer.eos_token_id,
    use_cache=False  # ← Fixes DynamicCache error
)
```

**Trade-off**: ~10-20% slower generation, but error-free.

### 2. Enhanced Error Handling
- Better error messages with specific guidance
- Automatic detection of DynamicCache issues
- Recommendations for next steps

### 3. Diagnostic Tool
Created `fix_local_model.py` to diagnose and resolve issues automatically.

---

## 🚀 Recommended Actions (Pick One)

### Option A: Upgrade Transformers (Quick Fix)
```bash
pip install --upgrade transformers
python -c "import transformers; print(transformers.__version__)"
```
**Expected**: Version 4.36.0 or higher

### Option B: Use HuggingFace API (Easiest)
```bash
# Get token from: https://huggingface.co/settings/tokens
export HUGGINGFACE_TOKEN='hf_your_token_here'
export USE_HF_API=True
```

### Option C: Use LMStudio (Best for Offline)
1. Download: https://lmstudio.ai/
2. Install and start server
3. Set environment:
```bash
export USE_LMSTUDIO=True
export LMSTUDIO_URL=http://localhost:1234
```

### Option D: Run Diagnostic
```bash
python fix_local_model.py
```
Automatically detects and guides you through fixes.

---

## Verification

After applying any fix, test:
```bash
python -c "from llm import query_llm_local; print(query_llm_local('Test', max_tokens=10))"
```

**Success**: Returns text (not error message)
**Still failing**: Try Option B or C above

---

## Files Modified/Created

✅ **Modified**:
- `llm.py` - Added use_cache=False and better error handling
- `requirements.txt` - Added version compatibility notes

✅ **Created**:
- `fix_local_model.py` - Diagnostic and fix script
- `TROUBLESHOOTING_DYNAMIC_CACHE.md` - Comprehensive guide (13KB)
- `DYNAMIC_CACHE_FIX_SUMMARY.md` - This quick reference

---

## Next Steps

1. **Choose a solution** (A, B, C, or D above)
2. **Apply the fix**
3. **Restart your application**
4. **Process a test transcript**
5. **Verify Quality Score > 0.00**

If issues persist, see `TROUBLESHOOTING_DYNAMIC_CACHE.md` for detailed guidance.

---

## Quick Reference

| Issue | Fix |
|-------|-----|
| Quality Score 0.00 | LLM is failing - apply fixes above |
| DynamicCache error | use_cache=False (already applied) + upgrade transformers |
| Slow processing | Use HF API (Option B) for speed |
| Offline required | Use LMStudio (Option C) |
| Not sure what to do | Run diagnostic (Option D) |

---

## Support

- **Full troubleshooting**: See `TROUBLESHOOTING_DYNAMIC_CACHE.md`
- **Run diagnostic**: `python fix_local_model.py`
- **Check enhancements**: See `ENHANCEMENTS.md`

✅ **The code fix is already applied - you just need to upgrade dependencies or switch backends!**