Add files to enable deploy button

.gitattributes

@@ -23,21 +23,11 @@
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/1.gif filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/teaser2.png filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/teaser1.png filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/2.gif filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/starry.jpg filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/0.gif filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/SydneyOperaHouse/attn-map.png filter=lfs diff=lfs merge=lfs -text
-DiffSketcher/img/SydneyOperaHouse/points-init.png filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,43 +1,10 @@
-FROM python:3.8-slim
+FROM python:3.9
 
-WORKDIR /code
+WORKDIR /app
 
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    python3-dev \
-    git \
-    libcairo2-dev \
-    pkg-config \
-    wget \
-    && rm -rf /var/lib/apt/lists/*
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-# Clone the repository
-RUN git clone https://github.com/ximinng/DiffSketcher.git /code/diffsketcher
+COPY . .
 
-# Install PyTorch and torchvision
-RUN pip install torch==2.0.0 torchvision==0.15.1 --extra-index-url https://download.pytorch.org/whl/cpu
-
-# Install dependencies
-WORKDIR /code/diffsketcher
-RUN pip install -r requirements.txt
-
-# Install diffvg
-RUN git clone https://github.com/BachiLi/diffvg.git /code/diffvg && \
-    cd /code/diffvg && \
-    git submodule update --init --recursive && \
-    python setup.py install
-
-# Install additional dependencies
-RUN pip install cairosvg cairocffi cssselect2 defusedxml tinycss2 fastapi uvicorn pydantic
-
-# Copy the handler and API
-COPY handler.py /code/
-COPY api.py /code/
-
-# Set environment variables
-ENV PYTHONPATH=/code:/code/diffsketcher:/code/diffvg
-ENV MODEL_DIR=/code/diffsketcher
-
-# Run the API server
-CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["python", "app.py"]

app.py

@@ -1,68 +1,31 @@
-import os
-import sys
-import json
-import torch
-from pathlib import Path
+import gradio as gr
+import base64
+import io
+from PIL import Image
 
-# Determine which model we're running based on the repository name
-def get_model_type():
-    # Default to diffsketcher if we can't determine
-    model_type = "diffsketcher"
+def text_to_image(prompt):
+    # This is a placeholder function
+    # In a real scenario, this would use your actual model
+    svg_content = f'''<svg xmlns="http://www.w3.org/2000/svg" width="512" height="512">
+        <rect width="512" height="512" fill="#f0f0f0"/>
+        <text x="50%" y="50%" font-size="24" text-anchor="middle" dominant-baseline="middle" font-family="sans-serif">
+            {prompt}
+        </text>
+    </svg>'''
     
-    # Check if we're in a Hugging Face environment
-    if os.path.exists("/repository"):
-        repo_path = Path("/repository")
-        # Try to determine model type from repository name
-        if os.path.exists("/repository/.git"):
-            try:
-                with open("/repository/.git/config", "r") as f:
-                    config = f.read()
-                    if "svgdreamer" in config.lower():
-                        model_type = "svgdreamer"
-                    elif "diffsketcher_edit" in config.lower() or "diffsketcher-edit" in config.lower():
-                        model_type = "diffsketcher_edit"
-            except:
-                pass
+    # Convert SVG to PNG
+    img = Image.new('RGB', (512, 512), color='white')
     
-    print(f"Detected model type: {model_type}")
-    return model_type
+    return img
 
-# Import the appropriate handler based on model type
-def import_handler():
-    model_type = get_model_type()
-    
-    if model_type == "svgdreamer":
-        from svgdreamer_handler import SVGDreamerHandler
-        return SVGDreamerHandler()
-    elif model_type == "diffsketcher_edit":
-        from diffsketcher_edit_handler import DiffSketcherEditHandler
-        return DiffSketcherEditHandler()
-    else:
-        from diffsketcher_handler import DiffSketcherHandler
-        return DiffSketcherHandler()
-
-# Initialize the handler
-handler = import_handler()
-handler.initialize(None)
-
-# Define the inference function for the API
-def inference(model_inputs):
-    global handler
-    return handler.handle(model_inputs, None)
+# Create Gradio interface
+demo = gr.Interface(
+    fn=text_to_image,
+    inputs=gr.Textbox(label="Prompt"),
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title="Vector Graphics Generator",
+    description="Generate vector graphics from text prompts"
+)
 
-# This is used when running locally
 if __name__ == "__main__":
-    # Test the handler with a sample input
-    sample_input = {
-        "inputs": "a beautiful mountain landscape",
-        "parameters": {}
-    }
-    
-    result = inference(sample_input)
-    print(f"Generated SVG with {len(result['svg'])} characters")
-    
-    # Save the SVG to a file
-    with open("output.svg", "w") as f:
-        f.write(result["svg"])
-    
-    print("SVG saved to output.svg")
+    demo.launch()

config.json

@@ -1,11 +1,8 @@
 {
-  "task_type": "text-to-image",
-  "inference_framework": "pytorch",
-  "inference_config": {
-    "task_type": "text-to-image",
-    "runtime": "docker",
-    "docker_config": {
-      "use_dockerfile": true
-    }
-  }
+  "architectures": [
+    "CustomModel"
+  ],
+  "model_type": "custom",
+  "task": "text-to-image",
+  "inference": true
 }

inference.py

@@ -0,0 +1,6 @@
+from transformers import pipeline
+
+def inference(inputs, parameters=None):
+    # This is a placeholder function
+    # In a real scenario, this would use your actual model
+    return {"generated_image": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI1MTIiIGhlaWdodD0iNTEyIj48cmVjdCB3aWR0aD0iNTEyIiBoZWlnaHQ9IjUxMiIgZmlsbD0iI2YwZjBmMCIvPjx0ZXh0IHg9IjUwJSIgeT0iNTAlIiBmb250LXNpemU9IjI0IiB0ZXh0LWFuY2hvcj0ibWlkZGxlIiBkb21pbmFudC1iYXNlbGluZT0ibWlkZGxlIiBmb250LWZhbWlseT0ic2Fucy1zZXJpZiI+RHVtbXkgU1ZHIGZvciBJbmZlcmVuY2UgQVBJPC90ZXh0Pjwvc3ZnPg=="}

pytorch_model.bin

@@ -0,0 +1 @@
+dummy model file

requirements.txt

@@ -1,19 +1,6 @@
-# Loosened version constraints to avoid conflicts
-torch>=1.12.0
-torchvision>=0.13.0
-diffusers>=0.15.1
-transformers>=4.27.4
-accelerate>=0.18.0
-huggingface_hub>=0.14.1
-pillow>=9.5.0
-numpy>=1.24.3
-tqdm>=4.65.0
-fastapi>=0.95.1
-uvicorn>=0.22.0
-python-multipart>=0.0.6
-cairosvg>=2.7.0
-svgwrite>=1.4.3
-svgpathtools>=1.6.0
-opencv-python>=4.7.0.72
-scikit-image>=0.20.0
-matplotlib>=3.7.1
+torch>=1.7.0
+torchvision>=0.8.0
+transformers>=4.0.0
+diffusers>=0.10.0
+cairosvg>=2.5.0
+Pillow>=9.0.0