didi-lot1-ai/artefacte_lot1/output_teste/video-analysis/TESTING.md

# Video Analysis - Testing Checklist

## ⚠️ HIGH PRIORITY: Model Comparison for Semantic Analysis

### Background

Currently both deepfake detection and semantic analysis use **BusterX** (Qwen2.5-VL-7B, 7B parameters). However, we have access to a much larger model **Qwen3-VL-30B** (30B parameters) that could provide significantly better semantic understanding.

### Hypothesis

Semantic analysis (content understanding, scene description, narrative) would benefit from the larger Qwen3-VL-30B model, while deepfake detection should continue using the specialized BusterX model.

---

## Test Plan: Qwen3-VL-30B for Semantic Analysis

### Current Configuration

```bash
# modules/video-analysis/deploy/.env
VIDEO_ANALYSIS_VLLM_BASE_URL=http://didiAI-video-vllm-buster:54500  # Port 54500
VIDEO_ANALYSIS_VLLM_MODEL=busterx                      # 7B parameters
```

### Test Configuration

```bash
# modules/video-analysis/deploy/.env
VIDEO_ANALYSIS_VLLM_BASE_URL=http://didiAI-llm-vllm-vision:14002  # Port 14002
VIDEO_ANALYSIS_VLLM_MODEL=qwen3-vl                     # 30B parameters
```

---

## Testing Procedure

### Step 1: Baseline Test (BusterX 7B)

```bash
# Current configuration - no changes needed
cd /home/vasi/ml-projects/modules/video-analysis/deploy

# Test semantic analysis with BusterX
curl -X POST http://localhost:8007/analyze/video/semantic \
  -F "file=@test_video_60s.mp4" \
  -F "chunk_duration_s=10.0" \
  -F "frames_per_chunk=24" \
  -o baseline_busterx.json

# Review results
jq '{
  model: "BusterX-7B",
  num_chunks: .num_chunks,
  chunk_descriptions: [.chunk_results[].description],
  final_summary: .final_summary,
  total_latency_s: .total_latency_s
}' baseline_busterx.json
```

### Step 2: Test with Qwen3-VL-30B

```bash
cd /home/vasi/ml-projects/modules/video-analysis/deploy

# Backup current config
cp .env .env.backup

# Update to use Qwen3-VL
cat > .env << 'ENVFILE'
HF_TOKEN=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
HF_CACHE_DIR=/cai2_ds_storage/hf_cache
VIDEO_ANALYSIS_RUNS_DIR=/app/runs

# Switch to Qwen3-VL for testing
VIDEO_ANALYSIS_VLLM_BASE_URL=http://didiAI-llm-vllm-vision:14002
VIDEO_ANALYSIS_VLLM_MODEL=qwen3-vl

# Semantic analysis settings
VIDEO_ANALYSIS_SEMANTIC_CHUNK_DURATION_S=10.0
VIDEO_ANALYSIS_SEMANTIC_FRAMES_PER_CHUNK=24
VIDEO_ANALYSIS_SEMANTIC_ENABLE_AGGREGATION=true
VIDEO_ANALYSIS_SEMANTIC_AGGREGATION_MODEL=gpt-oss-120b
VIDEO_ANALYSIS_SEMANTIC_LLM_BASE_URL=http://deploy-llm-api-1:8100
ENVFILE

# Rebuild and restart
docker compose build video-analysis-api
docker compose up -d video-analysis-api

# Wait for startup
sleep 10

# Test with same video
curl -X POST http://localhost:8007/analyze/video/semantic \
  -F "file=@test_video_60s.mp4" \
  -F "chunk_duration_s=10.0" \
  -F "frames_per_chunk=24" \
  -o test_qwen3vl.json

# Review results
jq '{
  model: "Qwen3-VL-30B",
  num_chunks: .num_chunks,
  chunk_descriptions: [.chunk_results[].description],
  final_summary: .final_summary,
  total_latency_s: .total_latency_s
}' test_qwen3vl.json
```

### Step 3: Compare Results

```bash
# Side-by-side comparison
echo "=== BusterX 7B ==="
jq -r '.chunk_results[0].description' baseline_busterx.json
echo ""
echo "=== Qwen3-VL 30B ==="
jq -r '.chunk_results[0].description' test_qwen3vl.json
echo ""

echo "=== Final Summaries ==="
echo "BusterX: $(jq -r '.final_summary' baseline_busterx.json)"
echo ""
echo "Qwen3-VL: $(jq -r '.final_summary' test_qwen3vl.json)"
```

---

## Evaluation Criteria

### Quality Metrics

1. **Description Detail**
   - [ ] More specific object identification
   - [ ] Better action recognition
   - [ ] More context understanding
   
2. **Narrative Coherence**
   - [ ] Logical flow between segments
   - [ ] Temporal consistency
   - [ ] Better story understanding

3. **Accuracy**
   - [ ] Correct identification of people/objects
   - [ ] Accurate scene descriptions
   - [ ] Proper action sequences

### Performance Metrics

| Metric | BusterX 7B | Qwen3-VL 30B | Difference |
|--------|-----------|--------------|------------|
| Latency per chunk | ~12s | ~??s | ?? |
| Total latency (60s video) | ~77s | ~??s | ?? |
| Token usage per chunk | ~5200 | ~????? | ?? |

---

## Expected Outcomes

### If Qwen3-VL is Better:

**Action:** Update default configuration to use Qwen3-VL for semantic analysis

```bash
# Keep two separate configs:
# 1. Deepfake endpoint → BusterX (specialized)
# 2. Semantic endpoint → Qwen3-VL (better understanding)
```

**Implementation:**
- Add `VIDEO_ANALYSIS_DEEPFAKE_MODEL` and `VIDEO_ANALYSIS_SEMANTIC_MODEL` settings
- Configure different models per endpoint

### If BusterX is Sufficient:

**Action:** Keep current configuration, document findings

**Rationale:** 
- Latency advantage (30B model is slower)
- VRAM savings
- BusterX might be sufficient for semantic tasks

---

## Test Videos

Suggested test scenarios:

1. **Meeting/Conference** (60s)
   - Multiple people
   - Complex interactions
   - Scene changes

2. **Action Sequence** (60s)
   - Fast movements
   - Object manipulation
   - Environmental changes

3. **Indoor/Outdoor Transition** (60s)
   - Lighting changes
   - Multiple scenes
   - Context shifts

---

## Rollback Procedure

If issues occur:

```bash
cd /home/vasi/ml-projects/modules/video-analysis/deploy

# Restore original config
cp .env.backup .env

# Rebuild
docker compose build video-analysis-api
docker compose up -d video-analysis-api
```

---

## Status

- [ ] Baseline test completed (BusterX)
- [ ] Test with Qwen3-VL completed
- [ ] Results compared
- [ ] Decision made
- [ ] Configuration updated (if needed)
- [ ] Documentation updated

---

## Notes

Add observations here during testing:

```
Date: ___________
Tester: _________

Observations:




Recommendation:



```