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# API Update Changelog - October 31, 2025
## Summary
Migrated from AI-powered auto-categorization to direct categorical data input API. This update removes external AI API dependencies (Perplexity and Gemini) and provides a more straightforward, efficient prediction service.
## Changes Made
### π΄ Removed Features
1. **AI API Integrations**
- β Perplexity Sonar Reasoning Pro (business categorization)
- β Gemini Pro Vision (pattern detection)
- β All external API calls and dependencies
2. **Functions Removed**
- `analyze_images_with_perplexity()` - Previously used for auto-categorizing business context
- `detect_pattern_with_gemini()` - Previously used for detecting A/B test patterns
- `load_pattern_descriptions()` - Pattern data loader (no longer needed)
- `image_to_base64()` - Image conversion for API calls
- `predict_with_auto_categorization()` - Main auto-prediction function
3. **Dependencies Removed**
- `requests` library (no external HTTP calls)
- `base64` module (no image encoding needed)
- `BytesIO` from io module (no in-memory buffer needed)
- `concurrent.futures` (no parallel API calls needed)
4. **Configuration Removed**
- `PERPLEXITY_API_KEY` environment variable
- `PERPLEXITY_API_URL` constant
- `GEMINI_API_KEY` environment variable
- `GEMINI_API_URL` constant
- `pattern_descriptions` global variable
### π’ Added Features
1. **New Main Function**
- `predict_with_categorical_data()` - Accepts images + categorical data directly
- Clean, focused API with no external dependencies
- Faster response times (no network latency)
2. **Enhanced Response Format**
- Simplified JSON structure
- Clear separation of prediction results, provided categories, and processing info
- All confidence metrics included in single response
3. **Updated Gradio Interface**
- Renamed "π€ Smart Auto-Prediction" tab to "π― API Prediction"
- Updated descriptions to reflect direct input requirement
- Cleaner UI focused on manual categorical selection
### π Modified Features
1. **predict_single()**
- No changes to core functionality
- Still handles image processing, OCR, and model inference
- Returns same detailed prediction results
2. **get_confidence_data()**
- No changes - still uses Industry + Page Type for confidence scoring
- Maintains same fallback logic
3. **Gradio Interface Layout**
- Tab 1: "π― API Prediction" (replaces auto-prediction)
- Tab 2: "π Manual Selection" (unchanged)
- Tab 3: "Batch Prediction from CSV" (unchanged)
## File Changes
### Modified Files
1. **app.py**
- Removed ~400 lines of AI API code
- Added new `predict_with_categorical_data()` function
- Updated Gradio interface
- Cleaned up imports
2. **requirements.txt**
- Removed: `requests`
- Kept all other dependencies (torch, transformers, gradio, etc.)
3. **README.md**
- Updated overview section
- Removed AI architecture section
- Removed API key requirements
- Added reference to API_USAGE_UPDATED.md
### New Files
1. **API_USAGE_UPDATED.md**
- Complete API documentation
- Input/output specifications
- Example usage code
- Migration guide
2. **CHANGELOG_API_UPDATE.md** (this file)
- Detailed change log
- Migration instructions
## API Changes
### Before (Auto-Categorization)
```python
# Input: Only images
result = predict_with_auto_categorization(
control_image=control_img,
variant_image=variant_img
)
# Output: Included auto-detected categories and patterns
{
"predictionResults": {...},
"autoDetectedCategories": {...},
"detectedPattern": {...},
"processingInfo": {...}
}
```
### After (Direct Input)
```python
# Input: Images + Categorical data
result = predict_with_categorical_data(
control_image=control_img,
variant_image=variant_img,
business_model="SaaS",
customer_type="B2B",
conversion_type="High-Intent Lead Gen",
industry="B2B Software & Tech",
page_type="Awareness & Discovery"
)
# Output: Prediction with provided categories
{
"predictionResults": {...},
"providedCategories": {...},
"processingInfo": {...}
}
```
## Migration Guide
### For Existing Users
If you were using the auto-categorization feature:
1. **Determine Categories**: You'll need to provide categorical data explicitly
- Business Model (4 options)
- Customer Type (4 options)
- Conversion Type (6 options)
- Industry (14 options)
- Page Type (5 options)
2. **Update API Calls**: Change from `predict_with_auto_categorization()` to `predict_with_categorical_data()`
3. **Update Response Handling**:
- Remove pattern detection logic
- Use `providedCategories` instead of `autoDetectedCategories`
4. **Remove API Keys**: No longer need PERPLEXITY_API_KEY or GEMINI_API_KEY
### Benefits of Migration
β
**Faster**: No external API latency (2-4s vs 10-15s previously)
β
**Cheaper**: No external API costs
β
**Simpler**: Direct input/output, no complex AI logic
β
**More Reliable**: No dependency on external services
β
**More Control**: User decides categorization instead of AI
## Performance Comparison
### Before (With AI APIs)
- Average processing time: 10-15 seconds
- External API calls: 2 (Perplexity + Gemini)
- Cost per prediction: ~$0.01-0.02
- Failure points: 3 (Perplexity, Gemini, Model)
### After (Direct Input)
- Average processing time: 2-4 seconds
- External API calls: 0
- Cost per prediction: GPU compute only
- Failure points: 1 (Model only)
## Testing Recommendations
1. **Verify categorical mappings**: Ensure all category values match expected options
2. **Test confidence scoring**: Verify Industry + Page Type combinations return correct stats
3. **Batch testing**: Test with multiple samples to ensure consistency
4. **Error handling**: Test with invalid categories to ensure proper error messages
## Support
For issues or questions:
- See `API_USAGE_UPDATED.md` for detailed documentation
- Check `confidence_scores.json` for available category combinations
- Review `README.md` for general information
## Version Info
- **Previous Version**: Auto-categorization with Perplexity + Gemini
- **Current Version**: Direct categorical input
- **Update Date**: October 31, 2025
- **Breaking Changes**: Yes (API signature changed)
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