app.py

# import gradio as gr
# from transformers import pipeline

# pipe = pipeline("image-classification", model="chaojie777/google-vit-base-patch16-224-in21k")
# images = ["images/daisy.jpg", "images/dandelion.jpg", "images/rosa.jpg", "images/sunflower.jpg", "images/tulip.jpg"]

# iface = gr.Interface.from_pipeline(
#         pipe, 
#         examples= [ [example] for example in images],
#         description="Final project that labels flowers images into: Daisy, Dandelion, Rose, Sunflower, Tulip",
#         title="Flower Classifier - Vit"
# )

# iface.launch()
import torch
import torchvision.transforms as transforms
from torchvision import models
from torch import nn
import torch.nn.functional as F
import gradio as gr
from PIL import Image
import json

# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")

# 设置模型路径
model_path = './best.pth'  # 替换为您训练的模型的路径
num_classes = 5
label_name_list = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

# 创建并加载模型
resnet18 = models.resnet18(pretrained=True)
num_ftrs = resnet18.fc.in_features
resnet18.fc = nn.Linear(num_ftrs, num_classes)
resnet18 = resnet18.to(device)

# 加载训练好的模型参数
resnet18.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model = resnet18
model.eval()

# 图片转换
train_transform = transforms.Compose([
    # transforms.RandomRotation(5),
    # transforms.RandomHorizontalFlip(),
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

def predict(inp):
    # 定义预处理变换
    transform = train_transform

    # 加载图片并进行预处理
    image = transform(inp).unsqueeze(0).to(device)

    # 使用模型进行预测
    with torch.no_grad():
        output = model(image)

    # 数据后处理
    # 计算预测概率
    pred_score = nn.functional.softmax(output[0], dim=0)
    pred_score = pred_score.cpu().numpy()

    # 获取预测结果
    pred_index = torch.argmax(output, dim=1).item()
    pred_label = label_name_list[pred_index]

    # 转为json字符串格式
    result_dict = {'pred_score': str(max(pred_score)), 'pred_index': str(pred_index), 'pred_label': pred_label}
    result_json = json.dumps(result_dict)

    return result_json
images = ["images/daisy.jpg", "images/dandelion.jpg", "images/rosa.jpg", "images/sunflower.jpg", "images/tulip.jpg"]
# 设置Gradio接口
demo = gr.Interface(fn=predict,
                    inputs=gr.Image(type="pil"),
                    outputs="text",
                    examples= [ [example] for example in images],
                    )

# 启动Gradio接口
demo.launch()

# import torch
# import torchvision.transforms as transforms
# from torchvision import models
# from torch import nn
# import torch.nn.functional as F
# import gradio as gr
# import json


# # Get cpu or gpu device for training.
# device = "cuda" if torch.cuda.is_available() else "cpu"
# print(f"Using {device} device")

# model_path = './resnet18_flower.pth'
# num_classes = 5
# label_name_list = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

# # step1: 创建并加载模型
# resnet18 = models.resnet18(pretrained=True)
# num_ftrs = resnet18.fc.in_features
# resnet18.fc = nn.Linear(num_ftrs, num_classes)
# resnet18 = resnet18.to(device)

# resnet18.load_state_dict(torch.load(model_path,map_location=torch.device('cpu')))

# model = resnet18
# # model = base_model_vgg13
# model.eval()


# # step2: 图片转换
# train_transform = transforms.Compose([
#                     # transforms.RandomRotation(5),
#                     # transforms.RandomHorizontalFlip(),
#                     transforms.Resize((224,224)),
#                     transforms.ToTensor(),
#                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
#                     ])


# def predict(inp):
#   # 定义预处理变换
#   transform = train_transform

#   # 加载图片并进行预处理
#   # image = Image.open(image_path)
#   image = transform(inp).unsqueeze(0).to(device)

#   # step3：使用模型进行预测
#   with torch.no_grad():
#       output = model(image)
      
#   # step4：数据后处理
#   # 计算预测概率
#   pred_score = nn.functional.softmax(output[0], dim=0)
#   pred_score = pred_score.cpu().numpy()

#   # 获取预测结果
#   pred_index = torch.argmax(output, dim=1).item()
#   pred_label = label_name_list[pred_index]
  
#   # 转为json字符串格式
#   result_dict = {'pred_score':str(max(pred_score)),'pred_index':str(pred_index),'pred_label':pred_label }
#   result_json = json.dumps(result_dict)

#   return result_json


# demo = gr.Interface(fn=predict, 
#              inputs=gr.Image(type="pil"),
#              outputs="text",
#              examples=["./592px-Red_sunflower.jpg"],
#              )
             
# # demo.launch(debug=True)
# demo.launch()