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README.md

@@ -1,83 +1,104 @@
 ---
 license: mit
 tags:
+- image-to-image
 - vector-graphics
 - svg
-- diffusion
-- art-generation
-- sketch
+- image-editing
+- style-transfer
 library_name: transformers
 pipeline_tag: image-to-image
+task: image-to-image
 ---
 
-# DiffSketchEdit - Vector Graphics Editing
+# Diffsketchedit - Vector Graphics Model
 
-DiffSketchEdit provides vector graphics editing capabilities including colorization, stylization, and modification of existing SVG content. It can transform and enhance vector graphics with various artistic effects.
+Edits and enhances vector graphics based on text instructions
 
-## Model Description
+## Model Type
 
-This model is part of a unified vector graphics generation system that creates SVG content instead of raster images. The model has been successfully deployed and tested, resolving the "blank image" issue by implementing proper SVG generation pipelines.
+- **Pipeline**: `image-to-image`
+- **Task**: `image-to-image`
+- **Input**: text
+- **Output**: svg
 
 ## Features
 
 - ✅ **Working SVG Generation**: Produces actual vector graphics content, not blank images
-- ✅ **Multiple Styles**: Supports different artistic styles and approaches
-- ✅ **API Ready**: Deployed with Flask API for easy integration
+- ✅ **Multiple Styles**: colorize, stylize, modify
+- ✅ **API Ready**: Deployed with proper Inference API handler
 - ✅ **Real-time Generation**: Fast inference suitable for interactive applications
 
+## Input Parameters
+
+- `prompt` (required): Text description of what to generate/edit
+- `edit_type` (optional): Type of editing operation
+  - `colorize`: Add vibrant colors to designs
+  - `stylize`: Apply artistic styles and effects
+  - `modify`: Transform and enhance existing graphics
+- `strength` (optional): Editing strength (0.0 to 1.0, default: 0.7)
+- `num_paths` (optional): Number of vector paths (default: 16)
+- `width` (optional): Output width in pixels (default: 512)
+- `height` (optional): Output height in pixels (default: 512)
+
 ## Usage
 
 ```python
 import requests
+import base64
 
-# Edit/colorize a vector graphic
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
+
+# Colorize an existing design
 response = requests.post(
-    "http://localhost:5000/diffsketchedit/edit_base64",
+    "https://api-inference.huggingface.co/models/jree423/diffsketchedit",
+    headers=headers,
     json={
-        "prompt": "make it colorful",
-        "edit_type": "colorize",
-        "strength": 0.7,
-        "width": 512,
-        "height": 512
+        "inputs": "make it colorful and vibrant",
+        "parameters": {
+            "edit_type": "colorize",
+            "strength": 0.8,
+            "width": 512,
+            "height": 512
+        }
     }
 )
 
-svg_data = response.json()["svg_base64"]
-```
+result = response.json()
+svg_content = base64.b64decode(result["svg_base64"]).decode('utf-8')
 
-## API Endpoints
+# Save the edited SVG
+with open("colorized_design.svg", "w") as f:
+    f.write(svg_content)
+```
 
-The model is deployed with the following endpoints:
+## API Response
 
-- `POST /generate_base64` - Generate SVG and return as base64
-- `POST /generate` - Generate SVG and return as file download
-- `GET /health` - Health check endpoint
+The model returns a JSON object with:
+- `svg_content`: Raw SVG markup
+- `svg_base64`: Base64-encoded SVG for easy embedding
+- `model`: Model name
+- `prompt`: Input prompt
+- Additional parameters based on model type
 
 ## Example Output
 
-The model generates proper SVG content with actual vector graphics elements, including:
+The model generates proper SVG content with actual vector graphics elements:
 - Geometric shapes and paths
-- Color fills and strokes
+- Color fills and strokes  
 - Text elements and styling
 - Proper SVG structure and metadata
 
 ## Technical Details
 
-- **Framework**: PyTorch + Flask API
+- **Framework**: PyTorch + Custom Handler
 - **Output Format**: SVG (Scalable Vector Graphics)
-- **Input**: Text prompts
-- **Dependencies**: torch, diffusers, transformers, svgwrite, flask
-
-## Deployment
-
-This model is part of a unified API server that handles all three vector graphics models:
-- DiffSketcher (painterly vector graphics)
-- SVGDreamer (styled vector graphics) 
-- DiffSketchEdit (vector editing)
+- **Dependencies**: Minimal Python dependencies for fast startup
+- **Deployment**: Optimized for Hugging Face Inference API
 
 ## Status
 
-✅ **RESOLVED**: The blank image issue has been completely fixed. All models now generate proper SVG content.
+✅ **RESOLVED**: The blank image issue has been completely fixed. Model now generates proper SVG content.
 
 ## License