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| # 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 | |
| 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,info, model_name): | |
| with open(file.name, 'r') as f: | |
| content = f.read() | |
| saved_test_dataset = "train.txt" | |
| saved_test_label = "train_label.txt" | |
| saved_train_info="train_info.txt" | |
| # Save the uploaded file content to a specified location | |
| shutil.copyfile(file.name, saved_test_dataset) | |
| shutil.copyfile(label.name, saved_test_label) | |
| shutil.copyfile(info.name, saved_train_info) | |
| # For demonstration purposes, we'll just return the content with the selected model name | |
| # if(model_name=="highGRschool10"): | |
| # checkpoint="ratio_proportion_change3/output/FS/bert_fine_tuned.model.ep32" | |
| # elif(model_name=="lowGRschoolAll"): | |
| # checkpoint="ratio_proportion_change3/output/IS/bert_fine_tuned.model.ep14" | |
| # elif(model_name=="fullTest"): | |
| # checkpoint="ratio_proportion_change3/output/correctness/bert_fine_tuned.model.ep48" | |
| # else: | |
| # checkpoint=None | |
| # print(checkpoint) | |
| subprocess.run([ | |
| "python", "new_test_saved_finetuned_model.py", | |
| "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded", | |
| "-finetune_task", model_name, | |
| "-test_dataset_path","../../../../train.txt", | |
| # "-test_label_path","../../../../train_label.txt", | |
| "-finetuned_bert_classifier_checkpoint", | |
| "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42", | |
| "-e",str(1), | |
| "-b",str(5) | |
| ], shell=True) | |
| # 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 = ["highGRschool10", "lowGRschoolAll", "fullTest"] | |
| # Create the Gradio interface | |
| with gr.Blocks(css=""" | |
| body { | |
| background-color: #1e1e1e!important; | |
| font-family: 'Arial', sans-serif; | |
| color: #f5f5f5!important;; | |
| } | |
| .gradio-container { | |
| max-width: 850px!important; | |
| margin: 0 auto!important;; | |
| padding: 20px!important;; | |
| background-color: #292929!important; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 20px rgba(0, 0, 0, 0.2); | |
| } | |
| .gradio-container-4-44-0 .prose h1 { | |
| font-size: var(--text-xxl); | |
| color: #ffffff!important; | |
| } | |
| #title { | |
| color: white!important; | |
| font-size: 2.3em; | |
| font-weight: bold; | |
| text-align: center!important; | |
| margin-bottom: 20px; | |
| } | |
| .description { | |
| text-align: center; | |
| font-size: 1.1em; | |
| color: #bfbfbf; | |
| margin-bottom: 30px; | |
| } | |
| .file-box { | |
| max-width: 180px; | |
| padding: 5px; | |
| background-color: #444!important; | |
| border: 1px solid #666!important; | |
| border-radius: 6px; | |
| height: 80px!important;; | |
| margin: 0 auto!important;; | |
| text-align: center; | |
| color: transparent; | |
| } | |
| .file-box span { | |
| color: #f5f5f5!important; | |
| font-size: 1em; | |
| line-height: 45px; /* Vertically center text */ | |
| } | |
| .dropdown-menu { | |
| max-width: 220px; | |
| margin: 0 auto!important; | |
| background-color: #444!important; | |
| color:#444!important; | |
| border-radius: 6px; | |
| padding: 8px; | |
| font-size: 1.1em; | |
| border: 1px solid #666; | |
| } | |
| .button { | |
| background-color: #4CAF50!important; | |
| color: white!important; | |
| font-size: 1.1em; | |
| padding: 10px 25px; | |
| border-radius: 6px; | |
| cursor: pointer; | |
| transition: background-color 0.2s ease-in-out; | |
| } | |
| .button:hover { | |
| background-color: #45a049!important; | |
| } | |
| .output-text { | |
| background-color: #333!important; | |
| padding: 12px; | |
| border-radius: 8px; | |
| border: 1px solid #666; | |
| font-size: 1.1em; | |
| } | |
| .footer { | |
| text-align: center; | |
| margin-top: 50px; | |
| font-size: 0.9em; | |
| color: #b0b0b0; | |
| } | |
| .svelte-12ioyct .wrap { | |
| display: none !important; | |
| } | |
| .file-label-text { | |
| display: none !important; | |
| } | |
| div.svelte-sfqy0y { | |
| display: flex; | |
| flex-direction: inherit; | |
| flex-wrap: wrap; | |
| gap: var(--form-gap-width); | |
| box-shadow: var(--block-shadow); | |
| border: var(--block-border-width) solid var(--border-color-primary); | |
| border-radius: var(--block-radius); | |
| background: #1f2937!important; | |
| overflow-y: hidden; | |
| } | |
| .block.svelte-12cmxck { | |
| position: relative; | |
| margin: 0; | |
| box-shadow: var(--block-shadow); | |
| border-width: var(--block-border-width); | |
| border-color: var(--block-border-color); | |
| border-radius: var(--block-radius); | |
| background: #1f2937!important; | |
| width: 100%; | |
| line-height: var(--line-sm); | |
| } | |
| .svelte-12ioyct .wrap { | |
| display: none !important; | |
| } | |
| .file-label-text { | |
| display: none !important; | |
| } | |
| input[aria-label="file upload"] { | |
| display: none !important; | |
| } | |
| gradio-app .gradio-container.gradio-container-4-44-0 .contain .file-box span { | |
| font-size: 1em; | |
| line-height: 45px; | |
| color: #1f2937 !important; | |
| } | |
| .wrap.svelte-12ioyct { | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: center; | |
| align-items: center; | |
| min-height: var(--size-60); | |
| color: #1f2937 !important; | |
| line-height: var(--line-md); | |
| height: 100%; | |
| padding-top: var(--size-3); | |
| text-align: center; | |
| margin: auto var(--spacing-lg); | |
| } | |
| span.svelte-1gfkn6j:not(.has-info) { | |
| margin-bottom: var(--spacing-lg); | |
| color: white!important; | |
| } | |
| label.float.svelte-1b6s6s { | |
| position: relative!important; | |
| top: var(--block-label-margin); | |
| left: var(--block-label-margin); | |
| } | |
| label.svelte-1b6s6s { | |
| display: inline-flex; | |
| align-items: center; | |
| z-index: var(--layer-2); | |
| box-shadow: var(--block-label-shadow); | |
| border: var(--block-label-border-width) solid var(--border-color-primary); | |
| border-top: none; | |
| border-left: none; | |
| border-radius: var(--block-label-radius); | |
| background: rgb(120 151 180)!important; | |
| padding: var(--block-label-padding); | |
| pointer-events: none; | |
| color: #1f2937!important; | |
| font-weight: var(--block-label-text-weight); | |
| font-size: var(--block-label-text-size); | |
| line-height: var(--line-sm); | |
| } | |
| .file.svelte-18wv37q.svelte-18wv37q { | |
| display: block!important; | |
| width: var(--size-full); | |
| } | |
| tbody.svelte-18wv37q>tr.svelte-18wv37q:nth-child(odd) { | |
| background: ##7897b4!important; | |
| color: white; | |
| background: #aca7b2; | |
| } | |
| .gradio-container-4-31-4 .prose h1, .gradio-container-4-31-4 .prose h2, .gradio-container-4-31-4 .prose h3, .gradio-container-4-31-4 .prose h4, .gradio-container-4-31-4 .prose h5 { | |
| color: white; | |
| """) as demo: | |
| gr.Markdown("<h1 id='title'>ASTRA</h1>", elem_id="title") | |
| gr.Markdown("<p class='description'>Upload a .txt file and select a model from the dropdown menu.</p>") | |
| with gr.Row(): | |
| file_input = gr.File(label="Upload a test file", file_types=['.txt'], elem_classes="file-box") | |
| label_input = gr.File(label="Upload test labels", file_types=['.txt'], elem_classes="file-box") | |
| info_input = gr.File(label="Upload test info", file_types=['.txt'], elem_classes="file-box") | |
| model_dropdown = gr.Dropdown(choices=models, label="Select Finetune Task", elem_classes="dropdown-menu") | |
| 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,info_input, model_dropdown], outputs=[output_text,output_image]) | |
| # Launch the app | |
| demo.launch() |