app.py

from flask import Flask, request, jsonify, send_file
from PIL import Image
import torch
import torch.nn.functional as F
from torchvision import transforms
import os
import numpy as np
from datetime import datetime
import sqlite3
import torch.nn as nn
import cv2
import json

# Grad-CAM++ imports
from pytorch_grad_cam import GradCAMPlusPlus
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image

app = Flask(__name__)

# ✅ Directory and database
OUTPUT_DIR = '/tmp/results'
os.makedirs(OUTPUT_DIR, exist_ok=True)

DB_PATH = os.path.join(OUTPUT_DIR, 'results.db')


def init_db():
    """Initialize SQLite database for storing results."""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS results (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            image_filename TEXT,
            prediction TEXT,
            confidence REAL,
            gradcam_filename TEXT,
            gradcam_gray_filename TEXT,
            timestamp TEXT
        )
    """)
    conn.commit()
    conn.close()


init_db()


# ✅ Import your EfficientNetB0_TransformerGLAM model
from efficientnet_transformer_glam import EfficientNetb0_TransformerGLAM  # Ensure this is in the path


# ✅ Instantiate the model
model = EfficientNetb0_TransformerGLAM(
    num_classes=3,
    embed_dim=512,
    num_heads=8,
    mlp_dim=512,
    dropout=0.5,
    window_size=7,
    reduction_ratio=8
)

# ✅ Load the trained checkpoint
model.load_state_dict(torch.load('efficientnet_glam_best.pt', map_location='cpu'))
model.eval()

# ✅ Class Names
CLASS_NAMES = ["Advanced", "Early", "Normal"]

# ✅ Transforms
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225]),
])

@app.route('/')
def home():
    """Check that the API is working."""
    return "Glaucoma Detection Flask API (EfficientNetB0_TransformerGLAM Model) is running!"

@app.route("/test_file")
def test_file():
    """Check if the .pt model file is present and readable."""
    filepath = "densenet169_seed40_best.pt"
    if os.path.exists(filepath):
        return f"✅ Model file found at: {filepath}"
    else:
        return "❌ Model file NOT found."

@app.route('/predict', methods=['POST'])
def predict():
    """Perform prediction and save results (including Grad-CAM++) to the database."""
    if 'file' not in request.files:
        return jsonify({'error': 'No file uploaded.'}), 400

    uploaded_file = request.files['file']
    if uploaded_file.filename == '':
        return jsonify({'error': 'No file selected.'}), 400

    try:
        # ✅ Save the uploaded image
        timestamp = int(datetime.now().timestamp())
        uploaded_filename = f"uploaded_{timestamp}.png"
        uploaded_file_path = os.path.join(OUTPUT_DIR, uploaded_filename)
        uploaded_file.save(uploaded_file_path)

        # ✅ Perform prediction
        img = Image.open(uploaded_file_path).convert('RGB')
        input_tensor = transform(img).unsqueeze(0)

        # Model Inference
        with torch.no_grad():
            output = model(input_tensor)
        probabilities = F.softmax(output, dim=1).cpu().numpy()[0]
        class_index = np.argmax(probabilities)
        result = CLASS_NAMES[class_index]
        confidence = float(probabilities[class_index])

        # ✅ Grad-CAM++ setup
        target_layer = model.fusion_block # Final block of EfficientNet feature extractor
        cam_model = GradCAMPlusPlus(model=model, target_layers=[target_layer])

        cam_output = cam_model(input_tensor=input_tensor,
                               targets=[ClassifierOutputTarget(class_index)])[0]

        # ✅ Create RGB overlay
        original_img = np.asarray(img.resize((224, 224)), dtype=np.float32) / 255.0
        overlay = show_cam_on_image(original_img, cam_output, use_rgb=True)
        
        # ✅ Create grayscale version
        cam_normalized = np.uint8(255 * cam_output)

        # ✅ Save overlay
        gradcam_filename = f"gradcam_{timestamp}.png"
        gradcam_file_path = os.path.join(OUTPUT_DIR, gradcam_filename)
        cv2.imwrite(gradcam_file_path, cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))

        # ✅ Save grayscale
        gray_filename = f"gradcam_gray_{timestamp}.png"
        gray_file_path = os.path.join(OUTPUT_DIR, gray_filename)
        cv2.imwrite(gray_file_path, cam_normalized)

        # ✅ Save results to database
        conn = sqlite3.connect(DB_PATH)
        cursor = conn.cursor()
        cursor.execute("""
            INSERT INTO results (image_filename, prediction, confidence, gradcam_filename, gradcam_gray_filename, timestamp) 
            VALUES (?, ?, ?, ?, ?, ?)
        """, (uploaded_filename, result, confidence, gradcam_filename, gray_filename, datetime.now().isoformat()))
        conn.commit()
        conn.close()

        # ✅ Return results
        return jsonify({
            'prediction': result,
            'confidence': confidence,
            'normal_probability': float(probabilities[0]),
            'early_glaucoma_probability': float(probabilities[1]),
            'advanced_glaucoma_probability': float(probabilities[2]),
            'gradcam_image': gradcam_filename,
            'gradcam_gray_image': gray_filename,
            'image_filename': uploaded_filename
        })

    except Exception as e:
        return jsonify({'error': str(e)}), 500


@app.route('/results', methods=['GET'])
def results():
    """List all results from the SQLite database."""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("SELECT * FROM results ORDER BY timestamp DESC")
    results_data = cursor.fetchall()
    conn.close()

    results_list = []
    for record in results_data:
        results_list.append({
            'id': record[0],
            'image_filename': record[1],
            'prediction': record[2],
            'confidence': record[3],
            'gradcam_filename': record[4],
            'gradcam_gray_filename': record[5],
            'timestamp': record[6]
        })

    return jsonify(results_list)


@app.route('/gradcam/<filename>')
def get_gradcam(filename):
    """Serve the Grad-CAM overlay image."""
    filepath = os.path.join(OUTPUT_DIR, filename)
    if os.path.exists(filepath):
        return send_file(filepath, mimetype='image/png')
    else:
        return jsonify({'error': 'File not found.'}), 404


@app.route('/image/<filename>')
def get_image(filename):
    """Serve the original uploaded image."""
    filepath = os.path.join(OUTPUT_DIR, filename)
    if os.path.exists(filepath):
        return send_file(filepath, mimetype='image/png')
    else:
        return jsonify({'error': 'File not found.'}), 404


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)