Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# 加载 Trash-Net 模型
+model_name = "prithivMLmods/Trash-Net"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# 定义垃圾分类函数
+def trash_classification(image):
+    """输入图片，返回垃圾分类结果"""
+    if image is None:
+        return {}
+    
+    # 转换图片为 RGB
+    image = Image.fromarray(image).convert("RGB")
+    
+    # 转换成模型需要的 tensor
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # 模型预测
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    # 分类标签
+    labels = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
+    
+    # 返回每个类别的概率
+    predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
+    return predictions
+
+# 创建 Gradio 接口
+iface = gr.Interface(
+    fn=trash_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Trash Classification",
+    description="Upload an image to classify the type of waste material."
+)
+
+# 启动
+if __name__ == "__main__":
+    iface.launch()