import gradio as gr from huggingface_hub import hf_hub_download import pickle from gradio import Progress import numpy as np import subprocess import shutil import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc import pandas as pd # Define the function to process the input file and model selection def process_file(file,label,info,model_name,inc_slider,progress=Progress(track_tqdm=True)): # progress = gr.Progress(track_tqdm=True) progress(0, desc="Starting the processing") 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) # Load the test_info file and the graduation rate file test_info = pd.read_csv('train_info.txt', sep=',', header=None, engine='python') grad_rate_data = pd.DataFrame(pd.read_pickle('school_grduation_rate.pkl'),columns=['school_number','grad_rate']) # Load the grad_rate data # Step 1: Extract unique school numbers from test_info unique_schools = test_info[0].unique() # Step 2: Filter the grad_rate_data using the unique school numbers schools = grad_rate_data[grad_rate_data['school_number'].isin(unique_schools)] # Define a threshold for high and low graduation rates (adjust as needed) grad_rate_threshold = 0.9 # Step 4: Divide schools into high and low graduation rate groups high_grad_schools = schools[schools['grad_rate'] >= grad_rate_threshold]['school_number'].unique() low_grad_schools = schools[schools['grad_rate'] < grad_rate_threshold]['school_number'].unique() # Step 5: Sample percentage of schools from each group high_sample = pd.Series(high_grad_schools).sample(frac=inc_slider/100, random_state=1).tolist() low_sample = pd.Series(low_grad_schools).sample(frac=inc_slider/100, random_state=1).tolist() # Step 6: Combine the sampled schools random_schools = high_sample + low_sample # Step 7: Get indices for the sampled schools indices = test_info[test_info[0].isin(random_schools)].index.tolist() # Load the test file and select rows based on indices test = pd.read_csv('train.txt', sep=',', header=None, engine='python') selected_rows_df2 = test.loc[indices] # Save the selected rows to a file selected_rows_df2.to_csv('selected_rows.txt', sep='\t', index=False, header=False, quoting=3, escapechar=' ') # For demonstration purposes, we'll just return the content with the selected model name if(model_name=="High Graduated Schools"): finetune_task="highGRschool10" elif(model_name== "Low Graduated Schools" ): finetune_task="highGRschool10" elif(model_name=="Full Set"): finetune_task="highGRschool10" else: finetune_task=None # print(checkpoint) progress(0.1, desc="Files created and saved") # if (inc_val<5): # model_name="highGRschool10" # elif(inc_val>=5 & inc_val<10): # model_name="highGRschool10" # else: # model_name="highGRschool10" progress(0.2, desc="Executing models") subprocess.run([ "python", "new_test_saved_finetuned_model.py", "-workspace_name", "ratio_proportion_change3_2223/sch_largest_100-coded", "-finetune_task", "highGRschool10", "-test_dataset_path","../../../../selected_rows.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(1000) ]) progress(0.6,desc="Model execution completed") 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) progress(1.0) # Prepare text output text_output = f"Model: {model_name}\nResult:\n{result}" # Prepare text output with HTML formatting text_output = f""" Model: {model_name}\n Result Summary:\n -----------------\n Precision: {result['precisions']:.2f}\n Recall: {result['recalls']:.2f}\n Time Taken: {result['time_taken_from_start']:.2f} seconds\n Total Schools in test: {len(unique_schools):.4f}\n Total Schools taken: {len(random_schools):.4f}\n High grad schools: {len(high_sample):.4f}\n Low grad schools: {len(low_sample):.4f}\n -----------------\n Note: The ROC Curve is also displayed for the evaluation. """ return text_output,plot_path # List of models for the dropdown menu models = ["High Graduated Schools", "Low Graduated Schools", "Full Set"] # 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("
Upload a .txt file and select a model from the dropdown menu.
") 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") increment_slider = gr.Slider(minimum=1, maximum=100, step=1, label="Schools Percentage", value=1) 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,increment_slider], outputs=[output_text,output_image]) # Launch the app demo.launch()