app.py

app.py

@@ -1,38 +1,22 @@
 # app.py
-import subprocess
-import sys
-
-# ---------------- 自动安装缺失依赖 ----------------
-def install(package):
-    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
-
-for pkg in ["torch", "torchvision", "transformers", "gradio", "Pillow"]:
-    try:
-        __import__(pkg if pkg != "Pillow" else "PIL")
-    except ModuleNotFoundError:
-        print(f"⚠️ 未找到 {pkg}，正在自动安装...")
-        install(pkg)
-        print(f"✅ {pkg} 安装完成")
-
-# ---------------- 导入库 ----------------
+import gradio as gr
 from transformers import AutoModelForImageClassification, AutoImageProcessor
 from PIL import Image
 import torch
-import gradio as gr
 
-# ---------------- 模型列表 ----------------
+# ---------------- 配置模型列表 ----------------
 MODEL_LIST = [
+    "prithivMLmods/Trash-Net",
     "yangy50/garbage-classification",
-    "ahmzakif/TrashNet-Classification",
-    "harriskr14/trashnet-vit"
+    "ahmzakif/TrashNet-Classification"
 ]
 
 # ---------------- 加载模型 ----------------
 models = []
 processors = []
 loaded_model_names = []
-
 print("🔹 正在加载模型，请稍等...")
+
 for model_name in MODEL_LIST:
     try:
         processor = AutoImageProcessor.from_pretrained(model_name)
@@ -62,7 +46,6 @@ def classify_image(image: Image.Image):
         except Exception as e:
             results[model_name] = f"❌ 预测失败: {e}"
 
-    # 格式化输出
     results_text = "\n".join([f"{name}: {label}" for name, label in results.items()])
     return results_text
 
@@ -71,9 +54,11 @@ iface = gr.Interface(
     fn=classify_image,
     inputs=gr.Image(type="pil", label="上传图片"),
     outputs=[gr.Textbox(label="所有模型预测结果")],
-    title="垃圾分类模型检测",
-    description="上传图片后，每个模型独立输出预测结果，不做任何人工干预。"
+    title="垃圾分类全模型检测",
+    description="上传图片后，每个模型独立输出预测结果，不做任何人工干预。",
+    allow_flagging="never"
 )
 
+# ✅ 启用 API 模式（Space 可被外部调用）
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(server_name="0.0.0.0", server_port=7860, show_api=True)