Fix: Update Dockerfile to use torch 2.0.0 and cairosvg

Dockerfile

@@ -2,23 +2,24 @@ FROM python:3.8-slim
 
 WORKDIR /code
 
-# Install system dependencies
+# Install system dependencies for Cairo
 RUN apt-get update && apt-get install -y \
     build-essential \
     python3-dev \
-    libfreetype6-dev \
+    libcairo2-dev \
     pkg-config \
     libpng-dev \
-    libcairo2-dev \
     libffi-dev \
-    cmake \
-    git \
     && rm -rf /var/lib/apt/lists/*
 
-# Install base Python packages with fixed versions
+# Install torch 2.0.0 and torchvision 0.15.1
+RUN pip install --no-cache-dir torch==2.0.0 torchvision==0.15.1
+
+# Install cairosvg and its dependencies
+RUN pip install --no-cache-dir cairosvg==2.7.0 cairocffi==1.5.1 cssselect2==0.7.0 defusedxml==0.7.1 tinycss2==1.2.1
+
+# Install other dependencies
 RUN pip install --no-cache-dir \
-    torch==1.13.1 \
-    torchvision==0.14.1 \
     diffusers==0.15.1 \
     transformers==4.27.4 \
     accelerate==0.18.0 \
@@ -30,22 +31,6 @@ RUN pip install --no-cache-dir \
     uvicorn==0.22.0 \
     python-multipart==0.0.6
 
-# Install SVG dependencies
-RUN pip install --no-cache-dir \
-    cairosvg==2.7.0 \
-    cairocffi==1.5.1 \
-    cssselect2==0.7.0 \
-    defusedxml==0.7.1 \
-    tinycss2==1.2.1 \
-    svgwrite==1.4.3 \
-    svgpathtools==1.6.0
-
-# Install image processing libraries
-RUN pip install --no-cache-dir \
-    opencv-python-headless==4.7.0.72 \
-    scikit-image==0.20.0 \
-    matplotlib==3.7.1
-
 # Create mock diffvg package
 RUN mkdir -p /tmp/mock_diffvg/pydiffvg && \
     echo '# Mock diffvg package\nimport numpy as np\nimport torch\n\ndef render(shapes, shape_groups, width, height, samples=2, seed=None):\n    return torch.zeros((height, width, 3), dtype=torch.float32)\n\ndef render_shape_group(canvas, shape_group_id, shapes, shape_groups, shape_ids, samples=2, seed=None):\n    pass\n\ndef save_svg(shapes, shape_groups, filename, width, height, use_gamma=False, background=None):\n    with open(filename, "w") as f:\n        f.write(f"<svg width=\\"{width}\\" height=\\"{height}\\" xmlns=\\"http://www.w3.org/2000/svg\\"><rect width=\\"100%\\" height=\\"100%\\" fill=\\"white\\"/></svg>")\n\ndef svg_path_to_shapes(path_string):\n    return [], []\n\ndef from_svg(filename):\n    return [], []\n\nclass Circle:\n    def __init__(self, radius=1.0, center=None):\n        self.radius = radius\n        self.center = center if center is not None else torch.tensor([0.0, 0.0])\n\nclass Ellipse:\n    def __init__(self, radius=None, center=None):\n        self.radius = radius if radius is not None else torch.tensor([1.0, 1.0])\n        self.center = center if center is not None else torch.tensor([0.0, 0.0])\n\nclass Path:\n    def __init__(self, points=None, is_closed=True):\n        self.points = points if points is not None else torch.tensor([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]])\n        self.is_closed = is_closed\n\nclass Rect:\n    def __init__(self, p_min=None, p_max=None):\n        self.p_min = p_min if p_min is not None else torch.tensor([0.0, 0.0])\n        self.p_max = p_max if p_max is not None else torch.tensor([1.0, 1.0])\n\nclass ShapeGroup:\n    def __init__(self, shape_ids=None, fill_color=None, stroke_color=None):\n        self.shape_ids = shape_ids if shape_ids is not None else []\n        self.fill_color = fill_color if fill_color is not None else torch.tensor([1.0, 1.0, 1.0, 1.0])\n        self.stroke_color = stroke_color if stroke_color is not None else torch.tensor([0.0, 0.0, 0.0, 1.0])' > /tmp/mock_diffvg/pydiffvg/__init__.py && \
@@ -53,11 +38,11 @@ RUN mkdir -p /tmp/mock_diffvg/pydiffvg && \
     cd /tmp/mock_diffvg && \
     pip install .
 
-# Create a simple handler.py if it doesn't exist
-RUN echo 'import os\nimport io\nimport base64\nimport json\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport cairosvg\n\nclass ModelHandler:\n    def __init__(self):\n        self.initialized = False\n        self.model = None\n        self.device = "cuda" if torch.cuda.is_available() else "cpu"\n        \n    def initialize(self, model_dir):\n        """Initialize the model"""\n        # Placeholder for model initialization\n        self.initialized = True\n        return self.initialized\n    \n    def preprocess(self, data):\n        """Preprocess the input data"""\n        inputs = data.get("inputs", {})\n        \n        if isinstance(inputs, str):\n            # Text-to-image case\n            prompt = inputs\n            image = None\n        else:\n            # Image-to-image case\n            prompt = inputs.get("text", "")\n            image_b64 = inputs.get("image", None)\n            \n            if image_b64:\n                image_data = base64.b64decode(image_b64)\n                image = Image.open(io.BytesIO(image_data))\n            else:\n                image = None\n        \n        return {"prompt": prompt, "image": image}\n    \n    def inference(self, inputs):\n        """Run inference with the model"""\n        prompt = inputs["prompt"]\n        image = inputs["image"]\n        \n        # Generate a placeholder SVG\n        width, height = 512, 512\n        svg_content = f"""<svg width="{width}" height="{height}" xmlns="http://www.w3.org/2000/svg">\n            <rect width="100%" height="100%" fill="#f0f0f0"/>\n            <text x="50%" y="50%" font-family="Arial" font-size="20" text-anchor="middle">{prompt}</text>\n        </svg>"""\n        \n        return svg_content\n    \n    def postprocess(self, inference_output):\n        """Postprocess the model output"""\n        # Convert SVG to base64 for response\n        svg_bytes = inference_output.encode("utf-8")\n        svg_base64 = base64.b64encode(svg_bytes).decode("utf-8")\n        \n        # Convert SVG to PNG using cairosvg\n        try:\n            png_data = cairosvg.svg2png(bytestring=svg_bytes)\n            png_base64 = base64.b64encode(png_data).decode("utf-8")\n        except Exception as e:\n            print(f"Error converting SVG to PNG: {e}")\n            # Return a transparent 1x1 pixel PNG as fallback\n            png_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII="\n        \n        return {\n            "svg": inference_output,\n            "svg_base64": svg_base64,\n            "png_base64": png_base64\n        }\n    \n    def handle(self, data):\n        """Handle a request to the model"""\n        try:\n            if not self.initialized:\n                self.initialize("model")\n            \n            if data is None:\n                return {"error": "No input data provided"}\n            \n            # Preprocess\n            inputs = self.preprocess(data)\n            \n            # Inference\n            outputs = self.inference(inputs)\n            \n            # Postprocess\n            processed_outputs = self.postprocess(outputs)\n            \n            return processed_outputs\n        except Exception as e:\n            return {"error": str(e)}\n\ndef handler(inputs, **kwargs):\n    """Handler function for the API"""\n    model_handler = ModelHandler()\n    return model_handler.handle(inputs)' > /code/handler.py
+# Create a simple handler.py
+COPY handler.py /code/handler.py
 
-# Copy application code
-COPY . .
+# Create a simple API file
+COPY api.py /code/api.py
 
 # Set up the API
 CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]