Add application file

app.py

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+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(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)
+    parent_location="ratio_proportion_change3_2223/sch_largest_100-coded/finetuning/"
+    if(model_name=="High Graduated Schools"):
+        finetune_task="highGRschool10"
+        test_info_location=parent_location+"highGRschool10/test_info.txt"
+        test_location=parent_location+"highGRschool10/test.txt"
+    elif(model_name== "Low Graduated Schools" ):
+        finetune_task="lowGRschoolAll"
+        test_info_location=parent_location+"lowGRschoolAll/test_info.txt"
+        test_location=parent_location+"lowGRschoolAll/test.txt"
+    elif(model_name=="Full Set"):
+        test_info_location=parent_location+"highGRschool10/test_info.txt"
+        test_location=parent_location+"highGRschool10/test.txt"
+        finetune_task="highGRschool10"
+    else:
+        finetune_task=None
+    # Load the test_info file and the graduation rate file
+    test_info = pd.read_csv(test_info_location, 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(test_location, 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
+
+    # 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", finetune_task,
+        "-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("<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")
+
+    
+    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=[model_dropdown,increment_slider], outputs=[output_text,output_image])
+
+
+# Launch the app
+demo.launch()