Upload app.py

app.py

@@ -0,0 +1,64 @@
+import os
+from transformers import pipeline
+from tqdm import tqdm
+from PIL import Image
+import matplotlib.pyplot as plt
+from math import sqrt
+import gradio as gr
+import numpy as np  
+    
+model_info = """
+**模型名称**: Google/vit-base-patch16-224  
+**模型介绍**: 本程序根据huggingface上Google开源模型vit，在猫狗图片数据上进行微调,上传一张图片，将会预测其类别并显示结果。模型官网：https://huggingface.co/google/vit-base-patch16-224  
+**程序作者**: 计科三班 王志建、计科三班 罗楷轩  
+**特别支持**: 计科三班 黄成栋  
+"""
+    
+# 加载图像分类模型
+checkpoint_dir = "./checkpoint/checkpoint-181" # 模型检查点目录
+classifier = pipeline("image-classification", model=checkpoint_dir) # 创建图像分类器模型
+vitclassifier = pipeline("image-classification",model="google/vit-base-patch16-224")
+
+
+demo = gr.Blocks()  
+
+# 定义推理函数
+def flip_myvit(image):
+    # 图像预处理
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    # 进行图像分类
+    result = classifier(image)
+    # 返回分类结果
+    text = "{:.3f}%".format(result[0]['score'] * 100)
+    return result[0]['label'],text
+ 
+  
+def flip_vit(image):
+    # 图像预处理
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    # 进行图像分类
+    result = vitclassifier(image)
+    # 返回分类结果
+    text = "{:.3f}%".format(result[0]['score'] * 100)
+    return result[0]['label'],text 
+  
+with demo:  
+    gr.Markdown(model_info)  
+    with gr.Tabs():  
+        with gr.TabItem("myvit"):  
+            myvit_input = gr.Image() 
+            myvit_output1 = gr.Textbox(label="预测结果")
+            myvit_output2 = gr.Textbox(label="准确度")
+            myvit_button = gr.Button("开始")  
+        with gr.TabItem("vit"):  
+            vit_input = gr.Image()  
+            vit_output1 = gr.Textbox(label="预测结果")  
+            vit_output2 = gr.Textbox(label="准确度") 
+            vit_button = gr.Button("开始")  
+      
+    myvit_button.click(flip_myvit, inputs=myvit_input, outputs=[myvit_output1,myvit_output2])  
+    vit_button.click(flip_vit, inputs=vit_input, outputs=[vit_output1,vit_output2])  
+
+demo.title="猫狗分类器"
+demo.launch()  
+