# Image Upload Feature - Implementation Summary

## ✅ Completed Features

### 1. Image Upload Core Functionality

- ✅ `ImageUploader` class using Meta's rupload protocol
- ✅ UUID-based upload session management
- ✅ Automatic MIME type detection
- ✅ File size tracking
- ✅ Returns `media_id`, `file_size`, `mime_type` for subsequent operations
- ✅ Error handling and validation

### 2. Three Use Cases - All Working

#### 💬 Chat/Image Analysis

- **Status**: ✅ Working
- **Endpoint**: `/chat` with `media_ids` and `attachment_metadata`
- **Response**: Full text analysis of uploaded images
- **Implementation**: Multi-step agent response parsing

#### 🎨 Similar Image Generation

- **Status**: ✅ Working
- **Endpoint**: `/image` with `media_ids` and `attachment_metadata`
- **Response**: 4 generated images with full URLs
- **Implementation**: Extract URLs from `content.imagine.session.media_sets`

#### 🎬 Video from Images

- **Status**: ✅ Working
- **Endpoint**: `/video` with `media_ids` and `attachment_metadata`
- **Response**: Generated video URL
- **Implementation**: Integration with existing `VideoGenerator`

### 3. Technical Implementation

#### Request Flow

```
Upload → media_id + metadata → Chat/Image/Video endpoints
```

#### Key Technical Details

- **Upload Protocol**: Meta's rupload with multipart/form-data
- **Entrypoint**: `KADABRA__DISCOVER__UNIFIED_INPUT_BAR` for uploads
- **Mutation**: `useKadabraSendMessageMutation` (doc_id: 34429318783334028)
- **Payload**: `messagePersistentInput` with:
  - `attachment_size`: File size in bytes
  - `attachment_type`: MIME type (e.g., "image/jpeg")
  - `meta_ai_entry_point`: Entrypoint string

#### Response Parsing

- **Chat**: `content.agent_steps[].composed_text.content[].text`
- **Image**: `content.imagine.session.media_sets[].imagine_media[].uri`
- **Fallback**: `imagine_card.session.media_sets[]` (metadata only)

### 4. Code Changes

#### Files Modified

1. **src/metaai_api/main.py**

   - Added `attachment_metadata` parameter to `prompt()`
   - Enhanced `extract_data()` with Kadabra support
   - Fixed `extract_media()` to check `content.imagine` first
   - Added URL fallback logic (uri, image_uri, maybe_image_uri, url)
   - Removed debug output

2. **src/metaai_api/utils.py**

   - Enhanced `format_response()` for multi-step agent responses
   - Added support for `XFBAbraMessageMultiStepResponseContent`

3. **src/metaai_api/image_upload.py**
   - Implemented `ImageUploader` class
   - Rupload protocol handling
   - UUID session management

#### Files Created

1. **IMAGE_UPLOAD_README.md** - Complete documentation
2. **QUICK_USAGE.md** - Quick reference guide
3. **examples/image_workflow_complete.py** - Complete example
4. **test_endpoints.py** - Comprehensive test suite

#### Files Updated

1. **README.md** - Added image upload section
2. **CHANGELOG.md** - Version 2.1.0 release notes

### 5. Testing Results

```
✅ Health Check (/healthz) - OK
✅ Image Upload (/upload) - Returns media_id, file_size, mime_type
✅ Chat with Image (/chat) - Returns full text analysis
✅ Similar Image Generation (/image) - Returns 4 images with URLs
✅ Video Generation (/video) - Returns video URL
```

## 🔧 Technical Challenges Solved

### Challenge 1: Empty Chat Responses

- **Problem**: Chat endpoint returned empty text
- **Root Cause**: Response structure uses `content.agent_steps[].composed_text`
- **Solution**: Enhanced `format_response()` to parse multi-step agent responses

### Challenge 2: Image URLs Returning None

- **Problem**: Generated images had `url: None`
- **Root Cause**: Code checked `imagine_card` (no URLs) instead of `content.imagine` (has URLs)
- **Solution**: Reordered extraction to check `content.imagine.session.media_sets` first

### Challenge 3: Multiple URL Field Names

- **Problem**: Different responses use different URL field names
- **Root Cause**: Meta's inconsistent field naming
- **Solution**: Fallback chain checking uri → image_uri → maybe_image_uri → url

### Challenge 4: Attachment Metadata

- **Problem**: Requests failed without proper metadata
- **Root Cause**: Meta requires `attachment_size` (bytes) and `attachment_type` (MIME)
- **Solution**: Upload returns metadata, pass it to all subsequent operations

## 📊 API Coverage

### SDK Methods

- ✅ `MetaAI.upload_image(file_path)` → Returns media_id + metadata
- ✅ `MetaAI.prompt(..., media_ids, attachment_metadata)` → Chat/Image
- ✅ `MetaAI.generate_video(..., media_ids, attachment_metadata)` → Video

### REST API Endpoints

- ✅ `POST /upload` → Upload image
- ✅ `POST /chat` → Analyze image
- ✅ `POST /image` → Generate similar images
- ✅ `POST /video` → Generate video from image

## 📚 Documentation

### User Documentation

- ✅ IMAGE_UPLOAD_README.md (complete guide)
- ✅ QUICK_USAGE.md (quick reference)
- ✅ README.md (main documentation updated)
- ✅ CHANGELOG.md (version 2.1.0 notes)

### Developer Documentation

- ✅ Code comments explaining response structures
- ✅ Example implementations
- ✅ Test suite demonstrating all features

### Examples

- ✅ Complete workflow example (upload → chat → image → video)
- ✅ Curl examples for all endpoints
- ✅ Python API client examples

## 🎯 Result

**All three image upload use cases are fully working:**

1. ✅ Upload → Chat/Analyze
2. ✅ Upload → Generate Similar Images (4 images with URLs)
3. ✅ Upload → Generate Video

The implementation is production-ready with comprehensive documentation, examples, and testing.
