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strokeprediction
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import gradio as gr

# ---------------------------------------------------------
# 1. Core Logic & Data Processing (Identical to raw code)
# ---------------------------------------------------------
np.random.seed(42)
num_patients = 1000

data = {
    'age': np.random.randint(18, 70, size=num_patients),
    'hypertension': np.random.choice([0, 1], size=num_patients, p=[0.75, 0.25]),
    'heart_disease': np.random.choice([0, 1], size=num_patients, p=[0.85, 0.15]),
    'avg_glucose_level': np.round(np.random.uniform(55.0, 280.0, size=num_patients), 1),
    'bmi': np.round(np.random.uniform(18.0, 48.0, size=num_patients), 1),
    'stroke': np.random.choice([0, 1], size=num_patients, p=[0.90, 0.10])
}

df = pd.DataFrame(data)

X = df.drop(columns=['stroke'])
y = df['stroke']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

model = LogisticRegression()
model.fit(X_train_scaled, y_train)

# Generate Feature Importance Plot
def get_feature_importance_plot():
    importance = model.coef_.flatten()
    feature_names = ['Age', 'Hypertension', 'Heart Disease', 'Avg Glucose Level', 'BMI']
    feature_importance = pd.DataFrame({
        'Clinical Feature': feature_names,
        'Statistical Weight': importance
    })
    feature_importance['Absolute Weight'] = feature_importance['Statistical Weight'].abs()
    feature_importance = feature_importance.sort_values(by='Absolute Weight', ascending=True)

    fig, ax = plt.subplots(figsize=(8, 4))
    ax.barh(feature_importance['Clinical Feature'], feature_importance['Statistical Weight'], color='#1f77b4')
    ax.axvline(0, color='black', linestyle='--', linewidth=1)
    ax.set_title('Neuro-Vascular Feature Importance (Model Weighting)', fontsize=12, fontweight='bold')
    ax.set_xlabel('Predictive Influence (Coefficient Value)', fontsize=10)
    ax.set_ylabel('Clinical Metrics', fontsize=10)
    ax.grid(axis='x', linestyle=':', alpha=0.6)
    plt.tight_layout()
    return fig

# ---------------------------------------------------------
# 2. Evaluation / Prediction Handler
# ---------------------------------------------------------
def analyze_patient(patient_age, hypertension, heart_disease, blood_glucose, patient_bmi):
    has_hypertension = 1 if hypertension == "Yes" else 0
    has_heart_disease = 1 if heart_disease == "Yes" else 0

    custom_patient = np.array([[patient_age, has_hypertension, has_heart_disease, blood_glucose, patient_bmi]])
    custom_patient_scaled = scaler.transform(custom_patient)

    prob = model.predict_proba(custom_patient_scaled)[0][1] * 100

    report = []
    report.append("==========================================================")
    report.append("             DYNAMIC CLINICAL ANALYSIS REPORT             ")
    report.append("==========================================================")
    report.append("📋 PATIENT BIOMETRICS PROFILE:")
    report.append(f"   • Age: {patient_age} years old")
    report.append(f"   • Hypertension History: {hypertension}")
    report.append(f"   • Cardiovascular Disease: {heart_disease}")
    report.append(f"   • Avg Blood Glucose: {blood_glucose} mg/dL")
    report.append(f"   • Body Mass Index (BMI): {patient_bmi} kg/m²")
    report.append("----------------------------------------------------------")
    report.append(f"🎯 CALCULATED CEREBROVASCULAR STROKE RISK: {prob:.2f}%")
    report.append("----------------------------------------------------------")

    if prob >= 15.0:
        report.append("🚨 WARNING: ELEVATED VASCULAR RISK CATEGORY")
        report.append("👉 DPT Action: Check baseline BP/HR. Avoid prolonged dependent positions or maximum manual resistance.")
        report.append("👉 Objective: Focus on submaximal aerobic training, functional mobility, and reactive balance strategy.")
    else:
        report.append("✅ CLEARANCE: BASELINE STABLE RISK PROFILE")
        report.append("👉 DPT Action: Safe to push metabolic capacity and neuroplasticity boundaries.")
        report.append("👉 Objective: High-intensity task-specific training and advanced dynamic gait coordination.")
    report.append("==========================================================")

    fig = get_feature_importance_plot()
    return "\n".join(report), fig

# ---------------------------------------------------------
# 3. Gradio Web Interface
# ---------------------------------------------------------
with gr.Blocks(title="DPT Neurological Clinical Appraisal") as demo:
    gr.Markdown("# 🧠 DPT Neurological Clinical Appraisal & Stroke Risk Calculator")
    gr.Markdown("Adjust patient biometrics below to calculate cerebrovascular risk and generate clinical DPT guidance.")

    with gr.Row():
        with gr.Column():
            age_input = gr.Slider(minimum=18, maximum=100, value=67, step=1, label="Patient Age")
            htn_input = gr.Radio(choices=["No", "Yes"], value="Yes", label="Hypertension History")
            hd_input = gr.Radio(choices=["No", "Yes"], value="No", label="Heart Disease History")
            glucose_input = gr.Slider(minimum=50.0, maximum=300.0, value=185.4, step=0.1, label="Avg Blood Glucose Level (mg/dL)")
            bmi_input = gr.Slider(minimum=15.0, maximum=50.0, value=31.2, step=0.1, label="Patient BMI (kg/m²)")
            submit_btn = gr.Button("Calculate Risk Profile", variant="primary")

        with gr.Column():
            output_report = gr.Textbox(label="Clinical Report Output", lines=18, max_lines=25)
            output_plot = gr.Plot(label="Model Feature Weights")

    submit_btn.click(
        fn=analyze_patient,
        inputs=[age_input, htn_input, hd_input, glucose_input, bmi_input],
        outputs=[output_report, output_plot]
    )

    demo.load(
        fn=analyze_patient,
        inputs=[age_input, htn_input, hd_input, glucose_input, bmi_input],
        outputs=[output_report, output_plot]
    )

if __name__ == "__main__":
    demo.launch()