fisrt push

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+from timeit import default_timer as timer
+from transformers import pipeline
+m1='models/3labels'
+m2='models/2labels'
+modelList=[m2,m1]
+def classifier(modelName,img):
+    startTime=timer()
+    pipe = pipeline(task="image-classification",
+                model=modelName
+               )
+    preds = pipe(img)
+    result={}
+    for pred in preds:
+       if pred["label"] == "zhazu":
+         result["炸组"] = pred["score"]
+       elif pred["label"] == "versailles":
+         result["凡尔赛"] = pred["score"]
+       else:
+         result["正常"] = pred["score"]
+      #result[pred["label"]] = pred["score"]
+    endTime=timer()
+    predTime=round(endTime-startTime,4)
+    
+    return result,predTime
+css='''
+#main {background-color: #ffffff;opacity: 0.8;background-image:  repeating-linear-gradient(45deg, #edffe1 25%, transparent 25%, transparent 75%, #edffe1 75%, #edffe1), repeating-linear-gradient(45deg, #edffe1 25%, #ffffff 25%, #ffffff 75%, #edffe1 75%, #edffe1);
+background-position: 0 0, 40px 40px;background-size: 80px 80px;}
+#mainContainer {max-width: 700px; margin-left: auto; margin-right: auto;background-color:transparent;}
+#btn {border: 2px solid #3ed6e500; margin-left: auto; margin-right: auto;background-color:#3ed6e500;border-radius: 5px;
+       :hover{
+      color: #92ccd8; } }
+#bg {border:2px solid #888;background-color:#fff;border-radius: 5px;}
+'''
+APP = gr.Blocks(css=css)
+APP.encrypt = False
+with APP:
+    with gr.Column(elem_id="main"):
+           with gr.Column(elem_id="mainContainer"):
+                    gr.HTML('''
+<div align=center>
+<img src="https://huggingface.co/Ailyth/2_Labels/resolve/main/banner.png"/>
+</div><br>
+<p style="font-size:12.5px">🎆这是一个可以给烹饪作品打分的工具，以豆瓣炸厨房组热门/精华帖中的作品为标准<br>
+😂功能主要是判断烹饪作品是否“炸组风”<br>
+当然结果并不十分严谨，纯玩耍用
+<br><br/>
+<b>使用方法</b><br>
+点击下面输入框即可上传图片，等待片刻后即可出结果。其中有两个模型，分别可判断三种标签（炸组、正常、凡尔赛）和两种标签（炸组，正常）。<br>
+希望大家都做饭愉快，吃的开心。</p>
+
+    ''')
+                    imgUpload=gr.components.Image(type="filepath", label="选择图片",elem_id="bg")
+                    modelSelect=gr.components.Radio(choices=modelList,label="选择预测模型：(第一个模型是两个分类，第二个是三个分类)",value=m2,elem_id="bg")
+
+                    btn=gr.Button(value='💥提交',elem_id="btn")
+           
+                    predResult=gr.components.Label(num_top_classes=3,label="预测结果",elem_id="bg") 
+                    predTime=gr.Number(label="实际预测耗时 (秒)",elem_id="bg")
+                    btn.click(classifier,inputs=[modelSelect,imgUpload], outputs=[predResult,predTime])
+                    gr.HTML('''
+ <br/>
+<h> 一些补充</4>
+<p>由于食物本身是一个复杂的集合概念，失败的烹饪作品和成功的烹饪作品又属于其子集，都有很多特征，判断起来很复杂，加上本功能所用的模型训练样本有限，所有检测结果经常翻车。</p>''')
+
+                    gr.HTML('''<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=Ailyth/ZhazuClassification" /></div>''')
+APP.launch(debug=True)
+
+
+

models/2labels/config.json

@@ -0,0 +1,50 @@
+{
+  "_name_or_path": "AutoTrain",
+  "architectures": [
+    "SwinForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    18,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 128,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "cuisine",
+    "1": "zhazu"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "cuisine": "0",
+    "zhazu": "1"
+  },
+  "layer_norm_eps": 1e-05,
+  "max_length": 128,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    4,
+    8,
+    16,
+    32
+  ],
+  "num_layers": 4,
+  "padding": "max_length",
+  "patch_size": 4,
+  "path_norm": true,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

models/2labels/preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

models/2labels/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06fbd6d85bd24d4cd530cc94b50e92797ae5e97489227c1b62fb091525c87d9c
+size 347599761

models/3labels/config.json

@@ -0,0 +1,52 @@
+{
+  "_name_or_path": "AutoTrain",
+  "architectures": [
+    "SwinForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "depths": [
+    2,
+    2,
+    18,
+    2
+  ],
+  "drop_path_rate": 0.1,
+  "embed_dim": 128,
+  "encoder_stride": 32,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "cuisine",
+    "1": "versailles",
+    "2": "zhazu"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "cuisine": "0",
+    "versailles": "1",
+    "zhazu": "2"
+  },
+  "layer_norm_eps": 1e-05,
+  "max_length": 128,
+  "mlp_ratio": 4.0,
+  "model_type": "swin",
+  "num_channels": 3,
+  "num_heads": [
+    4,
+    8,
+    16,
+    32
+  ],
+  "num_layers": 4,
+  "padding": "max_length",
+  "patch_size": 4,
+  "path_norm": true,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "use_absolute_embeddings": false,
+  "window_size": 7
+}

models/3labels/preprocessor_config.json

@@ -0,0 +1,23 @@
+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

models/3labels/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cedc07f507204b184b2dba718e9759fec6963b5bb2716f88bb60f83d5f7b43c4
+size 347603857

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+torch
+numpy
+transformers
+