import autogluon
from tkinter import Tk,filedialog
import pandas as pd 
from sklearn.model_selection import train_test_split
from autogluon.tabular import TabularDataset,TabularPredictor
from sklearn.metrics import roc_auc_score,f1_score,roc_curve,confusion_matrix
import gradio as gr
import matplotlib.pyplot as plt 
from sklearn.calibration import calibration_curve
import seaborn as sns 
# Tkinter部分，用于上传csv文件
# 可以使用gradio默认的部分
def upload_file():
    root=Tk()
    root.withdraw()
    file_path = filedialog.askopenfilename(title="Select CSV File", filetypes=[("Training files", "*.xlsx *.csv")])
    return file_path
# 模型训练内部评估
plt.rc('font',family='Times New Roman')
def train_and_evaluate(file):
  # 读取csv件
    df = pd.read_csv(file.name)
    label='hospital_expire_flag'
    # 分割数据集
    train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
    # 训练模型
    predictor=TabularPredictor(label=label,problem_type='binary',eval_metric='f1',path='./autogluon/').fit(train_df)
    # 载入最佳模型
    best_model=predictor._trainer.load_model(predictor.get_model_names()[-1])
    # 预测概率
    y_prob = best_model.predict_proba(test_df.drop(label,axis=1))
    
    # 计算AUC
    auc = roc_auc_score(test_df[label], y_prob)
    # 绘制ROC曲线
    fpr, tpr, _ = roc_curve(test_df[label], y_prob)
    plt.figure(figsize=(5, 4))
    plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')
    plt.plot([0, 1], [0, 1], linestyle='--')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC Curve')
    sns.despine()
    plt.legend(loc='best')
    plt.savefig('./roc_curve.png',dpi=200,bbox_inches='tight')
    
    # 绘制校准曲线
    prob_true, prob_pred = calibration_curve(test_df[label], y_prob, n_bins=10)
    plt.figure(figsize=(5, 4))
    plt.plot(prob_true, prob_pred, marker='o', label='Autogluon')
    plt.plot([0, 1], [0, 1], linestyle='--', label='Perfectly Calibrated')
    plt.ylabel('Predicted Probability',fontdict=dict(family='Times New Roman',size=15))
    plt.xlabel('True Probability',fontdict=dict(family='Times New Roman',size=15))
    plt.title('Calibration Curve',fontdict=dict(family='Times New Roman',size=15))
    sns.despine()
    plt.legend()
    plt.savefig('./Calibration_curve.png',dpi=200,bbox_inches='tight')
    
    # 绘制决策曲线
    y_pred=y_prob
    y_test=test_df[label]
    thresh_group=np.arange(0, 1, 0.01)
    net_benefit_model = np.array([])
    for thresh in thresh_group:
        y_pred_label = y_pred > thresh
        tn, fp, fn, tp = confusion_matrix(y_test, y_pred_label).ravel()
        n = len(y_test)
        net_benefit = (tp / n) - (fp / n) * (thresh / (1 - thresh))
        net_benefit_model = np.append(net_benefit_model, net_benefit)
        
    net_benefit_all = np.array([])
    tn, fp, fn, tp = confusion_matrix(y_test, y_test).ravel()
    total = tp + tn
    for thresh in thresh_group:
        net_benefit_ = (tp / total) - (tn / total) * (thresh / (1 - thresh))
        net_benefit_all = np.append(net_benefit_all, net_benefit_)
    plt.figure(figsize=(5, 4))
    ax=plt.gca()
    ax.plot(thresh_group, net_benefit_model)
    ax.plot(thresh_group, net_benefit_all, linestyle='--', label='Treat all')
    ax.plot((0, 1), (0, 0), color='black', linestyle='--', label='Treat none')
    ax.fill_between(thresh_group, net_benefit_model, 0, alpha=0.2)
    ax.set_xlim(0, 1)
    ax.set_ylim(net_benefit_model.min() - 0.15, net_benefit_model.max() + 0.15) 
    ax.set_xlabel('Threshold Probability', fontdict={'family': 'Times New Roman', 'fontsize': 15})
    ax.set_ylabel('Net Benefit', fontdict={'family': 'Times New Roman', 'fontsize': 15})
    ax.grid(which='minor')
    ax.spines['right'].set_color((0.8, 0.8, 0.8))
    ax.spines['top'].set_color((0.8, 0.8, 0.8))
    ax.legend(loc='upper right')
    sns.despine()
    plt.title('Decision Curve',fontdict=dict(family='Times New Roman',size=15))
    plt.savefig('./Decision_curve.png',dpi=200,bbox_inches='tight')
    
    FI=predictor.feature_importance(test_df)
    norm = plt.Normalize(min(FI.importance[:6]), max(FI.importance[:6]))
    colors = plt.cm.viridis(norm(FI.importance[:6].values))
    # 绘制棒图
    plt.figure(figsize=(5,4))
    plt.bar(FI.index[:6], FI.importance[:6],color=colors)
    ax=plt.gca()
    # 添加标题和标签
    plt.title('Feature Importance',fontdict=dict(family='Times New Roman',size=15),pad=0.2)
    plt.xlabel('Features')
    plt.ylabel('Permutation Shuffling Values')
    sns.despine()
    plt.xticks(rotation=45)
    plt.savefig('./feature_importance.png',dpi=200,bbox_inches='tight')
    
    return  './roc_curve.png','./Calibration_curve.png','./Decision_curve.png' ,'./feature_importance.png'
  # 外部验证
def external_evaluate(file):
  # 读取csv件
    df = pd.read_csv(file.name)
    label='hospital_expire_flag'
    # 训练模型
    predictor=TabularPredictor.load('./autogluon/')
    # 载入最佳模型
    best_model=predictor._trainer.load_model(predictor.get_model_names()[-1])
    # 预测概率
    y_prob = best_model.predict_proba(df.drop(label,axis=1))
    # 计算AUC
    auc = roc_auc_score(df[label], y_prob)
    # 绘制ROC曲线
    fpr, tpr, _ = roc_curve(df[label], y_prob)
    plt.figure(figsize=(5, 4))
    plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')
    plt.plot([0, 1], [0, 1], linestyle='--')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC Curve')
    sns.despine()
    plt.legend(loc='best')
    plt.savefig('./roc_curve_external.png',dpi=200,bbox_inches='tight')
    
    # 绘制校准曲线
    prob_true, prob_pred = calibration_curve(df[label], y_prob, n_bins=10)
    plt.figure(figsize=(5, 4))
    plt.plot(prob_true, prob_pred, marker='o', label='Autogluon')
    plt.plot([0, 1], [0, 1], linestyle='--', label='Perfectly Calibrated')
    plt.ylabel('Predicted Probability',fontdict=dict(family='Times New Roman',size=15))
    plt.xlabel('True Probability',fontdict=dict(family='Times New Roman',size=15))
    plt.title('Calibration Curve',fontdict=dict(family='Times New Roman',size=15))
    sns.despine()
    plt.legend()
    plt.savefig('./Calibration_curve_external.png',dpi=200,bbox_inches='tight')
    
    # 绘制决策曲线
    y_pred=y_prob
    y_test=df[label]
    thresh_group=np.arange(0, 1, 0.01)
    net_benefit_model = np.array([])
    for thresh in thresh_group:
        y_pred_label = y_pred > thresh
        tn, fp, fn, tp = confusion_matrix(y_test, y_pred_label).ravel()
        n = len(y_test)
        net_benefit = (tp / n) - (fp / n) * (thresh / (1 - thresh))
        net_benefit_model = np.append(net_benefit_model, net_benefit)
        
    net_benefit_all = np.array([])
    tn, fp, fn, tp = confusion_matrix(y_test, y_test).ravel()
    total = tp + tn
    for thresh in thresh_group:
        net_benefit_ = (tp / total) - (tn / total) * (thresh / (1 - thresh))
        net_benefit_all = np.append(net_benefit_all, net_benefit_)
    plt.figure(figsize=(5, 4))
    ax=plt.gca()
    ax.plot(thresh_group, net_benefit_model)
    ax.plot(thresh_group, net_benefit_all, linestyle='--', label='Treat all')
    ax.plot((0, 1), (0, 0), color='black', linestyle='--', label='Treat none')
    ax.fill_between(thresh_group, net_benefit_model, 0, alpha=0.2)
    ax.set_xlim(0, 1)
    ax.set_ylim(net_benefit_model.min() - 0.15, net_benefit_model.max() + 0.15) 
    ax.set_xlabel('Threshold Probability', fontdict={'family': 'Times New Roman', 'fontsize': 15})
    ax.set_ylabel('Net Benefit', fontdict={'family': 'Times New Roman', 'fontsize': 15})
    ax.grid(which='minor')
    ax.spines['right'].set_color((0.8, 0.8, 0.8))
    ax.spines['top'].set_color((0.8, 0.8, 0.8))
    ax.legend(loc='upper right')
    sns.despine()
    plt.title('Decision Curve',fontdict=dict(family='Times New Roman',size=15),pad=0.01)
    plt.savefig('./Decision_curve_external.png',dpi=200,bbox_inches='tight')
    # 绘制棒图
    FI=predictor.feature_importance(df)
    norm = plt.Normalize(min(FI.importance[:6]), max(FI.importance[:6]))
    colors = plt.cm.viridis(norm(FI.importance[:6].values))
  
    plt.figure(figsize=(5,4))
    plt.bar(FI.index[:6], FI.importance[:6],color=colors)
    ax=plt.gca()
    # 添加标题和标签
    plt.title('Feature Importance',fontdict=dict(family='Times New Roman',size=15),pad=0.2)
    plt.xlabel('Features')
    plt.ylabel('Permutation Shuffling Values')
    sns.despine()
    plt.xticks(rotation=45)
    plt.savefig('./feature_importance_external.png',dpi=200,bbox_inches='tight')
    
    return './roc_curve_external.png','./Calibration_curve_external.png','./Decision_curve_external.png' ,'./feature_importance_external.png' 
def preview_excel(file):
    df = pd.read_csv(file.name)
    return df.head(3)
import gradio as gr
import base64

# CSS styles for the interface
css = """
body {
    background-color: #f8f9fa;
    font-family: 'Arial', sans-serif;
}
#file_input, #external_file_input, #dataframe {
    border: 2px dashed #007bff;
    padding: 20px;
    border-radius: 10px;
    background-color: #fff;
}
#train_button, #evaluate_button, #dataframe_button {
    background-color: #007bff;
    color: gray;  /* Changed to white for better contrast */
    font-size: 18px;
    border-radius: 5px;
    margin-top: 10px;
    transition: background-color 0.3s;
}
#train_button:hover, #evaluate_button:hover, #dataframe_button:hover {
    background-color: #0056b3;
}
#roc_image, #calibration_image, #decision_image, #external_eval_image1, #external_eval_image2, #external_eval_image3 {
    border: 1px solid #ddd;
    border-radius: 10px;
    padding: 10px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
h1 {
    color: blue;
    text-align: center;
    font-size: 28px;
}
h2 {
    color: #007bff;
    text-align: center;
}
p {
    color: #555;
    text-align: center;
}
.spinner {
    display: none;
    text-align: center;
    margin-top: 20px;
}
"""

# Load and encode the background image
with open("D:/Haoran/科研/毕设/分析/模型部署/automl6.png", "rb") as image_file:
    encoded_string = base64.b64encode(image_file.read()).decode()

# Create the HTML layout with a background image
background_image = f"""
<div style="position: relative; height: 30vh;">
    <div style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; background-image: url('data:image/jpeg;base64,{encoded_string}'); background-size: contain; background-repeat: no-repeat; background-position: center; opacity: 0.7;">
    </div>
    <div style="position: absolute; top: 85%; left: 50%; transform: translate(-50%, -50%); text-align: center;">
        <h1 style="color: blue; font-weight: bold; font-size: 45px; white-space: nowrap;">Clinical Prediction Model Training and Evaluation based on AutoML</h1>
        <p>Upload your CSV file with a 'hospital_expire_flag' column for binary classification. The tool will train a model, evaluate it, and display ROC, Calibration, Decision curves and Feature Importance plot.</p>
    </div>
</div>
"""

# Create Gradio Blocks interface
with gr.Blocks(css=css) as interface:
    gr.HTML(background_image)

    with gr.Row():
        file_input = gr.File(label='Upload Model Training CSV File', elem_id="file_input")
    
    pre_button = gr.Button('Preview of the First 3 Rows', elem_id='dataframe_button')
    
    with gr.Row():
        dataframe = gr.DataFrame(elem_id='dataframe')

    pre_button.click(fn=preview_excel, inputs=file_input, outputs=dataframe)

    train_button = gr.Button("Train and Internal Evaluate", elem_id="train_button")

    with gr.Row():
        img1 = gr.Image(label="ROC Curve", type='filepath', elem_id="roc_image")
        img2 = gr.Image(label="Calibration Curve", type='filepath', elem_id="calibration_image")
        img3 = gr.Image(label="Decision Curve", type='filepath', elem_id="decision_image")
        img4 = gr.Image(label="Feature Importance", type='filepath', elem_id="feature_importance_image")

    spinner = gr.Markdown("<div class='spinner'>Training model... Please wait...</div>")
    

    def handle_click(file):
        spinner.update(value="正在训练模型，请稍候...", visible=True)
        try:
            results = train_and_evaluate(file)
            return results
        except Exception as e:
            return f"训练失败: {str(e)}"
        finally:
            spinner.update(visible=False)
    train_button.click(fn=handle_click, inputs=file_input, outputs=[img1, img2, img3, img4])           
    # External evaluation section
    gr.Markdown("<h2 style='text-align: center;'>External Evaluation</h2>")
    external_file_input = gr.File(label='Upload External Evaluation CSV File', elem_id="external_file_input")
    evaluate_button = gr.Button("External Evaluate", elem_id="evaluate_button")
    with gr.Row():
        external_eval_image1 = gr.Image(label="ROC Curve", type='filepath', elem_id="external_eval_image1")
        external_eval_image2 = gr.Image(label="Calibration Curve", type='filepath', elem_id="external_eval_image2")
        external_eval_image3 = gr.Image(label="Decision Curve", type='filepath', elem_id="external_eval_image3")
        external_eval_image4 = gr.Image(label="Feature Importance", type='filepath', elem_id="external_eval_image4")
    def evaluate_click(file):
        spinner.update(value="正在进行外部评估，请稍候...", visible=True)
        try:
            results = external_evaluate(file)
            return results
        except Exception as e:
            return f"外部评估失败: {str(e)}"
        finally:
            spinner.update(visible=False)
    evaluate_button.click(fn=evaluate_click, inputs=external_file_input, outputs=[external_eval_image1, external_eval_image2, external_eval_image3, external_eval_image4])
# Launch the interface
interface.launch()