merge

app.py

@@ -10,12 +10,41 @@ import matplotlib.pyplot as plt
 import numpy as np
 from datetime import datetime
 import scipy
+<<<<<<< HEAD
+=======
+import shutil
+>>>>>>> 95f252d5216b18bcd503dc7425ceb1dd1ccb8f6b
 # os.system('git clone https://github.com/EleutherAI/lm-evaluation-harness')
 # os.system('cd lm-evaluation-harness')
 # os.system('pip install -e .')
 # -i https://pypi.tuna.tsinghua.edu.cn/simple
 # 第一个功能：基于输入文本和对应的损失值对文本进行着色展示
 
+
+csv_file_path = 'data.csv'
+
+def save_and_share_csv():
+    src_path = './data/0309_merge_gjo.csv'
+    dest_dir = './save/'
+    if not os.path.exists(dest_dir):
+        os.makedirs(dest_dir)
+    dest_path = os.path.join(dest_dir, '0309_merge_gjo_shared.csv')
+    shutil.copy(src_path, dest_path)
+    return """
+    <script>
+    alert('Data shared successfully! CSV saved to ./save/ directory.');
+    </script>
+    """
+    #弹窗没有但反正能保存
+
+
+def plot_ppl():
+    df = pd.read_csv(csv_file_path)
+    # 假设df已经有适当的列用于绘图
+    fig = px.line(df, x='date', y='loss_mean_at_1000', color='model', title='PPL with Time')
+    return fig
+
+
 def color_text(text_list=["hi", "FreshEval","!"], loss_list=[0.1,0.7]):
     """
     根据损失值为文本着色。
@@ -454,6 +483,24 @@ with gr.Blocks() as demo:
             see the questions
             '''
         
+<<<<<<< HEAD
 
 demo.launch(debug=True)
 
+=======
+    with gr.Row():
+        plot_btn = gr.Button("Generate Plot")
+        share_btn = gr.Button("Share Data")
+    with gr.Row():
+        plot_space = gr.Plot()
+        share_result = gr.Textbox(visible=False)
+    
+    # 当点击“Generate Plot”按钮时，调用plotly_plot_question函数并在plot_space显示结果
+    plot_btn.click(fn=plotly_plot_question, inputs=[], outputs=plot_space)
+    
+    # 当点击“Share Data”按钮时，调用save_and_share_csv函数并在share_result显示结果
+    share_btn.click(fn=save_and_share_csv, inputs=[], outputs=share_result)
+
+
+demo.launch(share=True,debug=True)
+>>>>>>> 95f252d5216b18bcd503dc7425ceb1dd1ccb8f6b

gradio_samples/bertviz/app.py

@@ -15,7 +15,7 @@ from tqdm.notebook import tqdm
 from torch.utils.data import DataLoader
 from functools import partial
 
-from transformers import AutoTokenizer, MarianTokenizer, AutoModel, AutoModelForSeq2SeqLM, MarianMTModel
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
 
 from bertviz import model_view, head_view
 from bertviz_gradio import head_view_mod
@@ -23,44 +23,44 @@ from bertviz_gradio import head_view_mod
 
 
 model_es = "Helsinki-NLP/opus-mt-en-es"
-# model_fr = "Helsinki-NLP/opus-mt-en-fr"
-# model_zh = "Helsinki-NLP/opus-mt-en-zh"
-# model_sw = "Helsinki-NLP/opus-mt-en-sw"
+model_fr = "Helsinki-NLP/opus-mt-en-fr"
+model_zh = "Helsinki-NLP/opus-mt-en-zh"
+model_sw = "Helsinki-NLP/opus-mt-en-sw"
 
 tokenizer_es = AutoTokenizer.from_pretrained(model_es)
-# tokenizer_fr = AutoTokenizer.from_pretrained(model_fr)
-# tokenizer_zh = AutoTokenizer.from_pretrained(model_zh)
-# tokenizer_sw = AutoTokenizer.from_pretrained(model_sw)
+tokenizer_fr = AutoTokenizer.from_pretrained(model_fr)
+tokenizer_zh = AutoTokenizer.from_pretrained(model_zh)
+tokenizer_sw = AutoTokenizer.from_pretrained(model_sw)
 
-model_tr_es = MarianMTModel.from_pretrained(model_es)
-# model_tr_fr = MarianMTModel.from_pretrained(model_fr)
-# model_tr_zh = MarianMTModel.from_pretrained(model_zh)
-# model_tr_sw = MarianMTModel.from_pretrained(model_sw)
+model_tr_es =AutoModelForSeq2SeqLM.from_pretrained(model_es)
+model_tr_fr = AutoModelForSeq2SeqLM.from_pretrained(model_fr)
+model_tr_zh =AutoModelForSeq2SeqLM.from_pretrained(model_zh)
+model_tr_sw = AutoModelForSeq2SeqLM.from_pretrained(model_sw)
 
 model_es = inseq.load_model("Helsinki-NLP/opus-mt-en-es", "input_x_gradient")
-# model_fr = inseq.load_model("Helsinki-NLP/opus-mt-en-fr", "input_x_gradient")
-# model_zh = inseq.load_model("Helsinki-NLP/opus-mt-en-zh", "input_x_gradient")
-# model_sw = inseq.load_model("Helsinki-NLP/opus-mt-en-sw", "input_x_gradient")
+model_fr = inseq.load_model("Helsinki-NLP/opus-mt-en-fr", "input_x_gradient")
+model_zh = inseq.load_model("Helsinki-NLP/opus-mt-en-zh", "input_x_gradient")
+model_sw = inseq.load_model("Helsinki-NLP/opus-mt-en-sw", "input_x_gradient")
 
 dict_models = {
 	'en-es': model_es,
-	# 'en-fr': model_fr,
-	# 'en-zh': model_zh,
-	# 'en-sw': model_sw,
+	'en-fr': model_fr,
+	'en-zh': model_zh,
+	'en-sw': model_sw,
 }
 
 dict_models_tr = {
 	'en-es': model_tr_es,
-	# 'en-fr': model_tr_fr,
-	# 'en-zh': model_tr_zh,
-	# 'en-sw': model_tr_sw,
+	'en-fr': model_tr_fr,
+	'en-zh': model_tr_zh,
+	'en-sw': model_tr_sw,
 }
 
 dict_tokenizer_tr = {
 	'en-es': tokenizer_es,
-	# 'en-fr': tokenizer_fr,
-	# 'en-zh': tokenizer_zh,
-	# 'en-sw': tokenizer_sw,
+	'en-fr': tokenizer_fr,
+	'en-zh': tokenizer_zh,
+	'en-sw': tokenizer_sw,
 }
 
 saliency_examples = [
@@ -196,4 +196,4 @@ with gr.Blocks(js="plotsjs_bertviz.js") as demo:
     # demo.load(None,None,None,js="plotsjs.js")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)