app.py

import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
import os

# Load only brain tumor model for now
def load_brain_model():
    try:
        brain_model = tf.keras.models.load_model('models/brain_best.keras')
        print("✅ Brain tumor model loaded successfully!")
        return brain_model
    except Exception as e:
        print(f"❌ Error loading brain tumor model: {e}")
        return None

brain_model = load_brain_model()

def preprocess_image(image):
    if image is None:
        return None
    # Convert to RGB if needed
    if image.mode != 'RGB':
        image = image.convert('RGB')
    # Resize to model input size
    image = image.resize((224, 224))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

def predict_cancer(cancer_type, image):
    # This function will only be called for brain tumor detection
    # since the button is hidden for other cancer types
    if brain_model is None:
        return "❌ Brain tumor model not loaded properly. Please check model files."
    
    if image is None:
        return "⚠️ Please upload an MRI scan first."
    
    if cancer_type == "Brain Tumor Detection":
        try:
            processed_image = preprocess_image(image)
            if processed_image is None:
                return "❌ Error processing image."
            
            prediction = brain_model.predict(processed_image, verbose=0)[0][0]
            confidence = prediction * 100
            
            if prediction > 0.5:
                result = f"🔴 **Brain Tumor Detected**\nConfidence: {confidence:.2f}%"
            else:
                result = f"🟢 **No Brain Tumor Detected**\nConfidence: {100 - confidence:.2f}%"
                
            return result
            
        except Exception as e:
            return f"❌ Error during prediction: {str(e)}"
    
    else:
        return "❌ This function should only be called for brain tumor detection."

def update_info(cancer_type):
    info_text = {
        "Brain Tumor Detection": 
"""🧠 **Brain Tumor Detection**

**What to upload:** MRI scan images of the brain
**Supported formats:** JPG, PNG, JPEG
**Model type:** Binary classification (Tumor/No Tumor)
**Status:** ✅ Active and ready to use

**Note:** This model detects the presence of brain tumors in MRI scans. 
Early detection is crucial for effective treatment.""",
        
        "Breast Cancer Detection": 
"""🎗️ **Breast Cancer Detection**

**Status:** 🚧 Under Development

**What it will do:** Analyze mammography or histopathology images
**Future features:** Binary classification (Malignant/Benign)

**Note:** This feature is currently being developed. 
We're working hard to bring you accurate breast cancer detection soon!

For now, please use the Brain Tumor Detection feature.""",
        
        "Skin Cancer Detection": 
"""🔍 **Skin Cancer Detection**

**Status:** 🚧 Under Development

**What it will do:** Analyze dermoscopy or clinical images of skin lesions
**Future features:** Multi-class classification for various skin conditions

**Note:** This feature is currently being developed.
We're working on accurate skin cancer detection capabilities!

For now, please use the Brain Tumor Detection feature."""
    }
    return info_text.get(cancer_type, "Please select a cancer type.")

def update_interface_visibility(cancer_type):
    """Update visibility of upload components based on cancer type"""
    if cancer_type == "Brain Tumor Detection":
        return [
            gr.update(visible=True),   # image_input
            gr.update(visible=True),   # predict_btn
            gr.update(value="Upload an MRI scan and click 'Analyze Image' to detect brain tumors.")  # result_output
        ]
    else:
        development_message = f"🚧 **{cancer_type}**\n\n🔧 This feature is currently under development.\nWe're working hard to bring you this functionality soon!\n\nFor now, please use the Brain Tumor Detection feature."
        return [
            gr.update(visible=False),  # image_input
            gr.update(visible=False),  # predict_btn
            gr.update(value=development_message)  # result_output
        ]

def create_interface():
    with gr.Blocks(
        theme=gr.themes.Soft(),
        title="🩺 Cancer Detection System"
    ) as app:
        
        gr.HTML("""
        <div style="text-align: center; padding: 20px; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 10px; margin-bottom: 20px;">
            <h1>🩺 AI-Powered Cancer Detection System</h1>
            <p><em>"Early Detection Saves Lives"</em></p>
        </div>
        """)
        
        gr.HTML("""
        <div style="background-color: #fff3cd; border: 1px solid #ffeaa7; border-radius: 5px; padding: 15px; margin: 10px 0; color: #856404;">
            <strong>⚠️ Medical Disclaimer:</strong> This tool is for educational and research purposes only. 
            It should NOT be used as a substitute for professional medical diagnosis. 
            Always consult with qualified healthcare professionals for medical decisions.
        </div>
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                cancer_type = gr.Radio(
                    choices=["Brain Tumor Detection", "Breast Cancer Detection", "Skin Cancer Detection"],
                    label="🎯 Select Cancer Type",
                    value="Brain Tumor Detection"
                )
                
                image_input = gr.Image(
                    label="📷 Upload Medical Image",
                    type="pil",
                    height=300,
                    visible=True  
                )
                
                predict_btn = gr.Button(
                    "🔍 Analyze Image",
                    variant="primary",
                    size="lg",
                    visible=True  
                )
                
                info_panel = gr.Markdown(
                    value=update_info("Brain Tumor Detection"),
                    label="ℹ️ Information"
                )
            
            with gr.Column(scale=1):
                result_output = gr.Markdown(
                    label="📊 Analysis Results",
                    value="Upload an MRI scan and click 'Analyze Image' to detect brain tumors.",
                    height=200
                )
                
                gr.Markdown("""
                ### 🔬 How it works:
                1. **Select** the type of cancer detection
                2. **Upload** a medical image (MRI, mammography, or dermoscopy)
                3. **Click** "Analyze Image" to get AI-powered analysis
                4. **Review** the results and confidence scores
                
                ### 🎯 Model Information:
                - **Brain Tumor**: ✅ MobileNet-based binary classifier (Active)
                - **Breast Cancer**: 🚧 Under development
                - **Skin Cancer**: 🚧 Under development
                
                ### 📈 Current Status:
                Only Brain Tumor Detection is currently available. 
                Other features are being developed for future releases.
                """)
        
        # Event handlers
        cancer_type.change(
            fn=lambda ct: [update_info(ct)] + update_interface_visibility(ct),
            inputs=[cancer_type],
            outputs=[info_panel, image_input, predict_btn, result_output]
        )
        
        predict_btn.click(
            fn=predict_cancer,
            inputs=[cancer_type, image_input],
            outputs=[result_output]
        )
        
        gr.HTML("""
        <div style="text-align: center; margin-top: 30px; padding: 20px; background-color: #f8f9fa; border-radius: 10px;">
            <p><strong>🚨 Remember:</strong> This is an AI assistant for educational purposes. 
            For actual medical concerns, please consult healthcare professionals.</p>
            <p><em>Built with ❤️ using Gradio and TensorFlow</em></p>
        </div>
        """)
    
    return app

if __name__ == "__main__":
    if brain_model is None:
        print("❌ Cannot start application - brain tumor model failed to load")
        print("Please ensure the following file exists in the 'models/' directory:")
        print("- brain_best.keras")
    else:
        print("🚀 Starting Cancer Detection System...")
        app = create_interface()
        app.launch(
            share=True,
            server_name="127.0.0.1",
            server_port=7862,
            show_error=True,
            quiet=False
        )