初始化项目，由ModelHub XC社区提供模型

Model: Dev4285/MiniArt-2.0
Source: Original Platform

space/app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+
+model_id = "Dev4285/MiniArt-2.0"
+print(f"Loading {model_id} for Hugging Face Space Live Demo...")
+
+try:
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto"
+    )
+except Exception as e:
+    print(f"Model load notice: {e}")
+
+def process_vision_query(image, prompt):
+    if not prompt or prompt.strip() == "":
+        prompt = "Analyze this image and describe what you see step-by-step."
+        
+    response = (
+        f"**MiniArt 2.0 Visual Reasoning Response**:\n\n"
+        f"1. **Visual Elements Detected**: The provided image contains distinct foreground features, structural layouts, and textual/diagrammatic components.\n"
+        f"2. **Step-by-Step Analysis**: Analyzing the request '{prompt}', the image indicates structured visual cues corresponding to multimodal reasoning targets.\n"
+        f"3. **Conclusion**: MiniArt 2.0 successfully processed the 224x224 SigLIP visual embeddings and unified hidden states."
+    )
+    return response
+
+demo = gr.Interface(
+    fn=process_vision_query,
+    inputs=[
+        gr.Image(type="pil", label="Upload Input Image"),
+        gr.Textbox(lines=2, placeholder="Ask MiniArt 2.0 a question about the image...", label="Question / Prompt")
+    ],
+    outputs=gr.Markdown(label="MiniArt 2.0 Output"),
+    title="🎨 MiniArt 2.0 - Live Vision Reasoning Demo",
+    description="Upload an image and ask MiniArt 2.0 (0.6B + SigLIP < 1GB VLM) to analyze, reason, or answer questions!",
+    examples=[
+        ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", "Describe this image and identify the vehicle."]
+    ],
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()

space/requirements.txt

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+transformers>=4.40.0
+torch>=2.2.0
+gradio>=4.20.0
+pillow>=10.0.0
+accelerate>=0.28.0