app.py

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("<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=[file_input,label_input,info_input,model_dropdown,increment_slider], outputs=[output_text,output_image])


# Launch the app
demo.launch()