Meta Video Ad Deconstructor

## What This Skill Does

- **Generate Summaries**: Product, features, audience, CTA extraction
- **Deconstruct Marketing Dimensions**: Hooks, social proof, urgency, emotion, etc.
- **Support Multiple Content Types**: Consumer products and gaming ads
- **Progress Tracking**: Callback support for long analyses
- **JSON Output**: Structured data for downstream processing

## Setup

### 1. Environment Variables

```bash
# Required for Gemini
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
```

### 2. Dependencies

```bash
pip install vertexai
```

## Usage

### Basic Ad Deconstruction

```python
from scripts.deconstructor import AdDeconstructor
from scripts.models import ExtractedVideoContent
import vertexai
from vertexai.generative_models import GenerativeModel

# Initialize Vertex AI
vertexai.init(project="your-project-id", location="us-central1")
gemini_model = GenerativeModel("gemini-1.5-flash")

# Create deconstructor
deconstructor = AdDeconstructor(gemini_model=gemini_model)

# Create extracted content (from video-ad-analyzer or manually)
content = ExtractedVideoContent(
    video_path="ad.mp4",
    duration=30.0,
    transcript="Tired of messy cables? Meet CableFlow...",
    text_timeline=[{"at": 0.0, "text": ["50% OFF TODAY"]}],
    scene_timeline=[{"timestamp": 0.0, "description": "Person frustrated with tangled cables"}]
)

# Generate summary
summary = deconstructor.generate_summary(
    transcript=content.transcript,
    scenes="0.0s: Person frustrated with tangled cables",
    text_overlays="50% OFF TODAY"
)
print(summary)
```

### Full Deconstruction

```python
# Deconstruct all marketing dimensions
def on_progress(fraction, dimension):
    print(f"Progress: {fraction*100:.0f}% - Analyzed {dimension}")

analysis = deconstructor.deconstruct(
    extracted_content=content,
    summary=summary,
    is_gaming=False,  # Set True for gaming ads
    on_progress=on_progress
)

# Access dimensions
for dimension, data in analysis.dimensions.items():
    print(f"\n{dimension}:")
    print(data)
```

## Output Structure

### Summary Output

```
Product/App: CableFlow Cable Organizer

Key Features:
Magnetic design: Keeps cables organized automatically
Universal fit: Works with all cable types
Premium materials: Durable silicone construction

Target Audience: Tech users frustrated with cable management

Call to Action: Order now and get 50% off
```

### Deconstruction Output

```python
{
    "spoken_hooks": {
        "elements": [
            {
                "hook_text": "Tired of messy cables?",
                "timestamp": "0:00",
                "hook_type": "Problem Question",
                "effectiveness": "High - directly addresses pain point"
            }
        ]
    },
    "social_proof": {
        "elements": [
            {
                "proof_type": "User Count",
                "claim": "Over 1 million happy customers",
                "credibility_score": 7
            }
        ]
    },
    # ... more dimensions
}
```

## Marketing Dimensions Deconstructed

| Dimension | What It Extracts |
|-----------|------------------|
| `spoken_hooks` | Opening hooks from transcript |
| `visual_hooks` | Attention-grabbing visuals |
| `text_hooks` | On-screen text hooks |
| `social_proof` | Testimonials, user counts, reviews |
| `urgency_scarcity` | Limited time offers, stock warnings |
| `emotional_triggers` | Fear, desire, belonging, etc. |
| `problem_solution` | Pain points and solutions |
| `cta_analysis` | Call-to-action effectiveness |
| `target_audience` | Who the ad targets |
| `unique_mechanism` | What makes product special |

## Customizing Prompts

Edit prompts in `prompts/marketing_analysis.md` to customize:

- What dimensions to analyze
- Output format
- Scoring criteria
- Gaming vs consumer product focus

## Common Questions This Answers

- "What hooks does this ad use?"
- "What's the emotional appeal?"
- "How does this ad create urgency?"
- "Who is this ad targeting?"
- "What social proof is shown?"
- "Deconstruct this competitor's ad"