app.py

# Complete code for Hugging Face Spaces deployment
# Your CSV file name: brain_tumor_dataset.csv

import gradio as gr
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import os

print("Loading Brain Tumor Detection System...")

# Load dataset from CSV file
csv_file = 'brain_tumor_dataset.csv'

if not os.path.exists(csv_file):
    print(f"Error: {csv_file} not found!")
    print("Please upload brain_tumor_dataset.csv to the current directory")
else:
    df = pd.read_csv(csv_file)
    print(f"✅ Dataset loaded: {len(df)} patient records")
    print(f"📊 Columns in CSV: {list(df.columns)}")

# Check which columns exist and use only available ones
available_columns = df.columns.tolist()

# Define required columns
required_columns = ['Age', 'Gender', 'Headache_Score', 'Memory_Loss', 'Visual_Disturbance', 
                    'Nausea', 'Vomiting', 'Seizures', 'Weakness', 'Coordination_Issues', 'Tumor_Detected']

# Check if all required columns exist
for col in required_columns:
    if col not in available_columns:
        print(f"⚠️ Warning: '{col}' not found in CSV")

# Use only columns that exist
feature_columns = [col for col in required_columns if col in available_columns]
print(f"✅ Using features: {feature_columns}")

# Train model with available columns
le_gender = LabelEncoder()
le_memory = LabelEncoder()
le_visual = LabelEncoder()
le_nausea = LabelEncoder()
le_vomiting = LabelEncoder()
le_seizures = LabelEncoder()
le_weakness = LabelEncoder()
le_coordination = LabelEncoder()
le_tumor = LabelEncoder()

# Encode categorical columns
df['Gender_E'] = le_gender.fit_transform(df['Gender'])
df['Memory_E'] = le_memory.fit_transform(df['Memory_Loss'])
df['Visual_E'] = le_visual.fit_transform(df['Visual_Disturbance'])
df['Nausea_E'] = le_nausea.fit_transform(df['Nausea'])
df['Vomiting_E'] = le_vomiting.fit_transform(df['Vomiting'])
df['Seizures_E'] = le_seizures.fit_transform(df['Seizures'])
df['Weakness_E'] = le_weakness.fit_transform(df['Weakness'])
df['Coordination_E'] = le_coordination.fit_transform(df['Coordination_Issues'])
df['Tumor_E'] = le_tumor.fit_transform(df['Tumor_Detected'])

# Prepare features
X = df[['Age', 'Gender_E', 'Headache_Score', 'Memory_E', 'Visual_E', 
        'Nausea_E', 'Vomiting_E', 'Seizures_E', 'Weakness_E', 'Coordination_E']]
y = df['Tumor_E']

# Train model
model = RandomForestClassifier(n_estimators=200, max_depth=15, random_state=42)
model.fit(X, y)
print("✅ Model trained successfully!")

def predict_tumor(age, gender, headache, memory_loss, visual_disturbance, 
                  nausea, vomiting, seizures, weakness, coordination_issues):
    
    gender_e = 0 if gender == 'Male' else 1
    memory_e = 1 if memory_loss == 'Yes' else 0
    visual_e = 1 if visual_disturbance == 'Yes' else 0
    nausea_e = 1 if nausea == 'Yes' else 0
    vomiting_e = 1 if vomiting == 'Yes' else 0
    seizures_e = 1 if seizures == 'Yes' else 0
    weakness_e = 1 if weakness == 'Yes' else 0
    coordination_e = 1 if coordination_issues == 'Yes' else 0
    
    input_data = [[age, gender_e, headache, memory_e, visual_e, nausea_e, 
                   vomiting_e, seizures_e, weakness_e, coordination_e]]
    
    prediction = model.predict(input_data)[0]
    probability = model.predict_proba(input_data)[0][1]
    
    result = "TUMOR DETECTED" if prediction == 1 else "NO TUMOR"
    
    if prediction == 1:
        recommendations = """
        🚨 **URGENT RECOMMENDATIONS:**
        • Consult a neurologist immediately
        • Schedule brain MRI with contrast
        • Get a complete neurological examination
        • Do not delay treatment
        """
        color = "#c8a2c8"
        icon = "⚠️"
    else:
        recommendations = """
        ✅ **HEALTHY RECOMMENDATIONS:**
        • Continue regular health checkups
        • Maintain healthy lifestyle
        • Monitor any persistent symptoms
        • Annual medical examination
        """
        color = "#e6e6fa"
        icon = "✅"
    
    return result, f"{probability*100:.1f}%", recommendations, color, icon

custom_css = """
<style>
    @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700;900&family=Poppins:wght@300;400;600;700&display=swap');
    
    body {
        background: linear-gradient(45deg, #4b0082, #9370db, #d8bfd8, #e6e6fa, #dda0dd);
        background-size: 400% 400%;
        animation: gradientShift 15s ease infinite;
        font-family: 'Poppins', sans-serif;
    }
    
    @keyframes gradientShift {
        0% { background-position: 0% 50%; }
        50% { background-position: 100% 50%; }
        100% { background-position: 0% 50%; }
    }
    
    body::before {
        content: '';
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background: radial-gradient(circle at 20% 50%, rgba(216,191,216,0.2) 0%, transparent 50%);
        pointer-events: none;
        animation: floatParticles 20s infinite;
        z-index: 0;
    }
    
    @keyframes floatParticles {
        0%, 100% { transform: translate(0, 0); opacity: 0.3; }
        50% { transform: translate(100px, -50px); opacity: 0.6; }
    }
    
    .gradio-container {
        background: transparent !important;
        max-width: 1400px !important;
        margin: auto !important;
        padding: 20px !important;
    }
    
    h1 {
        text-align: center;
        font-family: 'Orbitron', monospace;
        font-size: 3.5em !important;
        font-weight: 900 !important;
        background: linear-gradient(135deg, #4b0082, #9370db, #d8bfd8, #e6e6fa);
        background-size: 300% 300%;
        -webkit-background-clip: text;
        background-clip: text;
        color: transparent;
        animation: titleGlow 3s ease infinite, textShine 5s linear infinite;
        margin-bottom: 10px !important;
    }
    
    @keyframes titleGlow {
        0%, 100% { filter: drop-shadow(0 0 20px rgba(75,0,130,0.5)); }
        50% { filter: drop-shadow(0 0 40px rgba(147,112,219,0.8)); }
    }
    
    @keyframes textShine {
        0% { background-position: 0% 50%; }
        100% { background-position: 100% 50%; }
    }
    
    .subtitle {
        text-align: center;
        color: #d8bfd8;
        font-size: 1.2em;
        margin-bottom: 30px;
        text-shadow: 0 0 10px rgba(216,191,216,0.5);
        animation: pulse 2s infinite;
    }
    
    @keyframes pulse {
        0%, 100% { opacity: 0.8; }
        50% { opacity: 1; text-shadow: 0 0 20px rgba(216,191,216,0.8); }
    }
    
    .card {
        background: rgba(255,255,255,0.95);
        backdrop-filter: blur(10px);
        border-radius: 20px;
        padding: 25px;
        margin: 15px 0;
        box-shadow: 0 15px 35px rgba(75,0,130,0.3);
        transition: transform 0.3s, box-shadow 0.3s;
        border: 1px solid rgba(216,191,216,0.3);
    }
    
    .card:hover {
        transform: translateY(-5px);
        box-shadow: 0 20px 45px rgba(147,112,219,0.4);
    }
    
    label {
        font-weight: 600 !important;
        color: #4b0082 !important;
        font-size: 0.95em !important;
        margin-bottom: 8px !important;
        display: block !important;
    }
    
    input, select, textarea {
        background: white !important;
        border: 2px solid #d8bfd8 !important;
        border-radius: 12px !important;
        padding: 10px 15px !important;
        font-size: 14px !important;
        transition: all 0.3s !important;
        color: #4b0082 !important;
    }
    
    input:focus, select:focus, textarea:focus {
        border-color: #9370db !important;
        box-shadow: 0 0 0 3px rgba(147,112,219,0.2) !important;
        outline: none !important;
    }
    
    input[type="range"] {
        background: linear-gradient(90deg, #4b0082, #9370db, #d8bfd8, #e6e6fa) !important;
        height: 8px !important;
        border-radius: 10px !important;
    }
    
    input[type="range"]::-webkit-slider-thumb {
        background: #9370db !important;
        width: 20px !important;
        height: 20px !important;
        border-radius: 50% !important;
        cursor: pointer !important;
        box-shadow: 0 0 10px #9370db !important;
    }
    
    .gr-button {
        background: linear-gradient(135deg, #4b0082, #9370db, #d8bfd8) !important;
        background-size: 200% 200% !important;
        color: white !important;
        border: none !important;
        padding: 12px 30px !important;
        font-size: 1.1em !important;
        font-weight: bold !important;
        border-radius: 50px !important;
        cursor: pointer !important;
        transition: all 0.3s !important;
        animation: buttonGradient 3s ease infinite;
    }
    
    @keyframes buttonGradient {
        0% { background-position: 0% 50%; }
        50% { background-position: 100% 50%; }
        100% { background-position: 0% 50%; }
    }
    
    .gr-button:hover {
        transform: scale(1.05);
        box-shadow: 0 10px 25px rgba(75,0,130,0.5);
    }
    
    .footer {
        text-align: center;
        padding: 20px;
        color: #e6e6fa;
        background: rgba(75,0,130,0.3);
        border-radius: 15px;
        margin-top: 30px;
        font-size: 0.9em;
    }
    
    ::-webkit-scrollbar {
        width: 10px;
        height: 10px;
    }
    
    ::-webkit-scrollbar-track {
        background: rgba(216,191,216,0.1);
        border-radius: 10px;
    }
    
    ::-webkit-scrollbar-thumb {
        background: linear-gradient(135deg, #4b0082, #9370db);
        border-radius: 10px;
    }
    
    input[type="radio"] {
        accent-color: #9370db !important;
    }
    
    @media (max-width: 768px) {
        h1 { font-size: 2em !important; }
        .card { padding: 15px !important; }
    }
</style>
"""

with gr.Blocks(css=custom_css, title="Brain Tumor Detection System") as demo:
    gr.HTML("""
        <h1>🧠 BRAIN TUMOR DETECTION SYSTEM</h1>
        <div class="subtitle">
            ✨ AI-Powered Medical Diagnosis | Instant Results ✨
        </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="card"):
                gr.Markdown("### 📋 PATIENT INFORMATION")
                age = gr.Slider(
                    minimum=1, maximum=120, value=45, step=1,
                    label="🎂 Age (Years)",
                    info="Patient's current age"
                )
                gender = gr.Radio(
                    choices=['Male', 'Female'], label="⚥ Gender",
                    info="Biological sex", value='Male'
                )
                headache = gr.Slider(
                    minimum=1, maximum=60, value=30, step=1,
                    label="🤕 Headache Severity (1-60)",
                    info="Pain intensity scale - higher score means more severe headache"
                )
            
            with gr.Group(elem_classes="card"):
                gr.Markdown("### 🧠 NEUROLOGICAL SYMPTOMS")
                memory_loss = gr.Radio(
                    choices=['No', 'Yes'], label="📝 Memory Loss",
                    info="Forgetting recent events", value='No'
                )
                visual_disturbance = gr.Radio(
                    choices=['No', 'Yes'], label="👁️ Visual Disturbance",
                    info="Blurred or double vision", value='No'
                )
                coordination_issues = gr.Radio(
                    choices=['No', 'Yes'], label="🎯 Coordination Issues",
                    info="Difficulty walking or maintaining balance", value='No'
                )
        
        with gr.Column(scale=1):
            with gr.Group(elem_classes="card"):
                gr.Markdown("### 🏥 PHYSICAL SYMPTOMS")
                nausea = gr.Radio(
                    choices=['No', 'Yes'], label="🤢 Nausea",
                    info="Feeling sick to stomach", value='No'
                )
                vomiting = gr.Radio(
                    choices=['No', 'Yes'], label="🤮 Vomiting",
                    info="Throwing up, especially in morning", value='No'
                )
                seizures = gr.Radio(
                    choices=['No', 'Yes'], label="⚡ Seizures",
                    info="Uncontrolled body movements", value='No'
                )
                weakness = gr.Radio(
                    choices=['No', 'Yes'], label="💪 Weakness",
                    info="Unexplained loss of strength", value='No'
                )
                
                predict_btn = gr.Button("🔍 PREDICT NOW", variant="primary", size="lg")
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="card"):
                gr.Markdown("### 📊 DIAGNOSIS RESULT")
                result_text = gr.HTML(label="Prediction")
                probability = gr.Textbox(
                    label="Confidence Score", interactive=False,
                    placeholder="Click predict to see result",
                    lines=1, show_label=True
                )
                recommendations = gr.Markdown("### 💡 RECOMMENDATIONS\n\n*Click predict to see recommendations*")
    
    def handle_prediction(age, gender, headache, memory_loss, visual_disturbance, 
                          nausea, vomiting, seizures, weakness, coordination_issues):
        result, prob, recs, color, icon = predict_tumor(
            age, gender, headache, memory_loss, visual_disturbance,
            nausea, vomiting, seizures, weakness, coordination_issues
        )
        
        styled_result = f"""
        <div style='background: {color}; padding: 25px; border-radius: 15px; text-align: center; border: 2px solid #9370db;'>
            <div style='font-size: 4em;'>{icon}</div>
            <div style='font-size: 2em; font-weight: bold; margin: 15px 0; color: #4b0082;'>{result}</div>
            <div style='font-size: 1.2em; margin-top: 10px; color: #4b0082;'>Risk Assessment Complete</div>
        </div>
        """
        
        return styled_result, prob, recs
    
    predict_btn.click(
        handle_prediction,
        inputs=[age, gender, headache, memory_loss, visual_disturbance, 
                nausea, vomiting, seizures, weakness, coordination_issues],
        outputs=[result_text, probability, recommendations]
    )
    
    gr.HTML("""
        <div class="footer">
            <p>🧠 Brain Tumor Detection System | AI-Powered Healthcare Assistant</p>
            <p>⚠️ Medical Disclaimer: This tool is for educational purposes. Always consult healthcare professionals.</p>
            <p>📊 Trained on patient records | Real-time AI Prediction</p>
        </div>
    """)

demo.launch()