deleted

app.py

@@ -1,96 +1,3 @@
-
-
-# import gradio as gr
-# from huggingface_hub import hf_hub_download
-# import pickle
-# import gradio as gr
-# import numpy as np
-# import subprocess
-# import shutil
-# import matplotlib.pyplot as plt
-# from sklearn.metrics import roc_curve, auc
-# # Define the function to process the input file and model selection
-# def process_file(file,label, model_name):
-#     with open(file.name, 'r') as f:
-#         content = f.read()
-#     saved_test_dataset = "train.txt"
-#     saved_test_label = "train_label.txt"
-    
-#     # Save the uploaded file content to a specified location
-#     shutil.copyfile(file.name, saved_test_dataset)
-#     shutil.copyfile(label.name, saved_test_label)
-#     # For demonstration purposes, we'll just return the content with the selected model name
-#     if(model_name=="FS"):
-#         checkpoint="ratio_proportion_change3/output/FS/bert_fine_tuned.model.ep32"
-#     elif(model_name=="IS"):
-#         checkpoint="ratio_proportion_change3/output/IS/bert_fine_tuned.model.ep14"
-#     elif(model_name=="CORRECTNESS"):
-#         checkpoint="ratio_proportion_change3/output/correctness/bert_fine_tuned.model.ep48"
-#     elif(model_name=="EFFECTIVENESS"):
-#         checkpoint="ratio_proportion_change3/output/effectiveness/bert_fine_tuned.model.ep28"
-#     else:
-#         checkpoint=None
-
-#     print(checkpoint)
-#     # subprocess.run(["python", "src/test_saved_model.py",
-#     #                 "--finetuned_bert_checkpoint",checkpoint
-#     #                 ])
-#     result = {}
-#     with open("result.txt", 'r') as file:
-#         for line in file:
-#             key, value = line.strip().split(': ', 1)
-#             # print(type(key))
-#             if key=='epoch':
-#                 result[key]=value
-#             else:
-#                  result[key]=float(value)
-# # Create a plot
-#     with open("roc_data.pkl", "rb") as f:
-#         fpr, tpr, _ = pickle.load(f)
-
-
-
-#     roc_auc = auc(fpr, tpr)
-#     fig, ax = plt.subplots()
-#     ax.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
-#     ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
-#     ax.set(xlabel='False Positive Rate', ylabel='True Positive Rate', title=f'ROC Curve: {model_name}')
-#     ax.legend(loc="lower right")
-#     ax.grid()
-
-#     # Save plot to a file
-#     plot_path = "plot.png"
-#     fig.savefig(plot_path)
-#     plt.close(fig)
-
-#     # Prepare text output
-#     text_output = f"Model: {model_name}\nResult:\n{result}"
-
-#     return text_output,plot_path
-
-# # List of models for the dropdown menu
-# models = ["FS", "IS", "CORRECTNESS","EFFECTIVENESS"]
-
-# # Create the Gradio interface
-# with gr.Blocks() as demo:
-#     gr.Markdown("# ASTRA")
-#     gr.Markdown("Upload a .txt file and select a model from the dropdown menu.")
-    
-#     with gr.Row():
-#         file_input = gr.File(label="Upload a .txt file", file_types=['.txt'])
-#         label_input = gr.File(label="Upload a .txt file", file_types=['.txt'])
-#         model_dropdown = gr.Dropdown(choices=models, label="Select a model")
-    
-#     with gr.Row():
-#         output_text = gr.Textbox(label="Output Text")
-#         output_image = gr.Image(label="Output Plot")
-
-#     btn = gr.Button("Submit")
-#     btn.click(fn=process_file, inputs=[file_input,label_input, model_dropdown], outputs=[output_text,output_image])
-
-# # Launch the app
-# demo.launch()
-
 import gradio as gr
 from huggingface_hub import hf_hub_download
 import pickle

result.txt

@@ -1,8 +0,0 @@
-epoch: EP0_test
-avg_loss: 0.8247231508152465
-total_acc: 50.0
-precisions: 0.25
-recalls: 0.5
-f1_scores: 0.3333333333333333
-time_taken_from_start: 77.90863966941833
-auc_score: 0.5

roc_data.pkl

@@ -1,3 +0,0 @@
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-oid sha256:8b84bcb95e30a1c23f399643fc8dd20b57d7a849faf2670a3df9b7d0f9285f34
-size 257