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README.md

@@ -1,82 +1,98 @@
 ---
 license: mit
 tags:
+- text-to-image
 - vector-graphics
 - svg
-- diffusion
 - art-generation
-- sketch
+- diffusion
 library_name: transformers
-pipeline_tag: image-to-image
+pipeline_tag: text-to-image
+task: text-to-image
 ---
 
-# DiffSketcher - Painterly Vector Graphics
+# Diffsketcher - Vector Graphics Model
 
-DiffSketcher generates painterly vector graphics with sketchy, artistic style. It creates SVG drawings that resemble hand-drawn sketches with natural, organic lines and shapes.
+Generates painterly vector graphics from text prompts
 
-## 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**: `text-to-image`
+- **Task**: `text-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**: painterly, sketchy, artistic
+- ✅ **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
+- `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
+
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
 
-# Generate a cat drawing
+# Generate a painterly cat drawing
 response = requests.post(
-    "http://localhost:5000/diffsketcher/generate_base64",
+    "https://api-inference.huggingface.co/models/jree423/diffsketcher",
+    headers=headers,
     json={
-        "prompt": "a beautiful cat drawing",
-        "num_paths": 16,
-        "width": 512,
-        "height": 512
+        "inputs": "a beautiful cat drawing",
+        "parameters": {
+            "num_paths": 16,
+            "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 SVG
+with open("cat_drawing.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