test upload and download file

app.py

@@ -1,67 +1,54 @@
 import gradio as gr
-import torch
-from transformers import BertModel
-from transformers import BertTokenizer
-import torch.nn as nn
+import pandas as pd
+import matplotlib.pyplot as plt
+# from Prediction import *
+
+# 定义处理函数
+def process_data(csv_file, num, model_name):
+    # 读取CSV文件
+    df = pd.read_csv(csv_file.name)
+
+    # 处理数据
+    processed_data = df * num
+
+    # 生成图片
+    plt.plot(processed_data)
+    plt.xlabel('X Label')
+    plt.ylabel('Y Label')
+    plt.title('Processed Data')
+    plt.grid(True)
+    plt.savefig('output.png')
+    plt.close()
+
+    # 生成字符串结果
+    result = pd.DataFrame({'result':[1,2,3]})
+    result.to_csv('output.csv')
+    print(model_name)
+    return 'output.csv', ['output.png','output.png']
+
+my_theme = gr.Theme.from_hub("gstaff/sketch")
+with gr.Blocks(theme=my_theme, title='Test') as demo:
+    gr.Markdown("""# Test
+    xxxx
+    """)
+
+    with gr.Tab("Single Sentence"):
+        with gr.Column():
+            csv_input = gr.File(label="CSV文件")
+            text_output = gr.File(label="结果")
+            image_output = gr.Gallery(label="图像")
+        with gr.Row():
+            seed_input = gr.Slider(minimum=0, maximum=100, step=1, label="seed",info="Different seeds may generate different results")
+            model_input = gr.CheckboxGroup(["ALL_Data", "Facebook", "Kickstarter", "Twitter"], label="Countries", info="Where are they from?")
+
+        with gr.Row():
+            button = gr.Button("Submit", variant="primary")
+            button.click(fn=process_data, inputs=[csv_input, seed_input, model_input], outputs=[text_output, image_output])
+            clear = gr.ClearButton([csv_input, text_output, image_output])
+
+    with gr.Tab("Csv File"):
+        ...
+    with gr.Tab("README"):
+        seed_input = gr.Textbox(label="seed")
 
-class BertSST2Model(nn.Module):
-
-    # 初始化类
-    def __init__(self, class_size, pretrained_name='bert-base-chinese'):
-        """
-        Args: 
-            class_size  :指定分类模型的最终类别数目，以确定线性分类器的映射维度
-            pretrained_name :用以指定bert的预训练模型
-        """
-        # 类继承的初始化，固定写法
-        super(BertSST2Model, self).__init__()
-        # 加载HuggingFace的BertModel
-        # BertModel的最终输出维度默认为768
-        # return_dict=True 可以使BertModel的输出具有dict属性，即以 bert_output['last_hidden_state'] 方式调用
-        self.bert = BertModel.from_pretrained(pretrained_name,
-                                              return_dict=True)
-        # 通过一个线性层将[CLS]标签对应的维度：768->class_size
-        # class_size 在SST-2情感分类任务中设置为：2
-        self.classifier = nn.Linear(768, class_size)
-
-    def forward(self, inputs):
-        # 获取DataLoader中已经处理好的输入数据：
-        # input_ids :tensor类型，shape=batch_size*max_len   max_len为当前batch中的最大句长
-        # input_tyi :tensor类型，
-        # input_attn_mask :tensor类型，因为input_ids中存在大量[Pad]填充，attention mask将pad部分值置为0，让模型只关注非pad部分
-        input_ids, input_tyi, input_attn_mask = inputs['input_ids'], inputs[
-            'token_type_ids'], inputs['attention_mask']
-        # 将三者输入进模型，如果想知道模型内部如何运作，前面的蛆以后再来探索吧~
-        output = self.bert(input_ids, input_tyi, input_attn_mask)
-        # bert_output 分为两个部分：
-        #   last_hidden_state:最后一个隐层的值
-        #   pooler output:对应的是[CLS]的输出,用于分类任务
-        # 通过线性层将维度：768->2
-        # categories_numberic：tensor类型，shape=batch_size*class_size，用于后续的CrossEntropy计算
-        categories_numberic = self.classifier(output.pooler_output)
-        return categories_numberic
-
-
-device = torch.device("cpu")
-pretrained_model_name = './bert-base-uncased'
-# 创建模型 BertSST2Model
-model = BertSST2Model(2, pretrained_model_name)
-# 固定写法，将模型加载到device上，
-# 如果是GPU上运行，此时可以观察到GPU的显存增加
-model.to(device)
-# 加载预训练模型对应的tokenizer
-tokenizer = BertTokenizer.from_pretrained(pretrained_model_name)
-
-
-def modelscope_quickstart(sentence):
-    inputs = tokenizer(sentence,
-                    padding=True,
-                    truncation=True,
-                    return_tensors="pt",
-                    max_length=512)
-    output = model(inputs)
-    cate = output.argmax(dim=1)
-    return "分类结果为：" + str(0 if cate else 1)
-
-demo = gr.Interface(fn=modelscope_quickstart, inputs="text", outputs="text")
 demo.launch()