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 torchvision.models as models

app = Flask(__name__)

# ✅ Directory and database path
OUTPUT_DIR = '/tmp/results'
if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)

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


def init_db():
    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,
            timestamp TEXT
        )
    """)
    conn.commit()
    conn.close()


init_db()

# 1️⃣ Instantiate the model
model = models.densenet169(pretrained=False)
model.classifier = nn.Linear(model.classifier.in_features, 3)

# 2️⃣ Load checkpoint
checkpoint = torch.load('densenet169_seed40_best2.pt', map_location='cpu')
state_dict = checkpoint['state_dict']

# 3️⃣ Fix state dict
new_state_dict = {}
for k, v in state_dict.items():
    # Check if it's prefixed with 'features.0.'
    if k.startswith('features.0.'):
        new_key = 'features.' + k[len('features.0.'):]  # Remove the '0.' segment
    else:
        new_key = k
    new_state_dict[new_key] = v

# 4️⃣ Load into the model
model.load_state_dict(new_state_dict)

# Done!
model.eval()
# ✅ Class Names
CLASS_NAMES = ["Normal", "Early Glaucoma", "Advanced Glaucoma"]

@app.route('/')
def home():
    return "Glaucoma Detection Flask API (3-Class 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 using PyTorch (3-class), save results, and save to SQLite 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)

        with torch.no_grad():
            output = model(input_tensor)
            probabilities = F.softmax(output, dim=1).cpu().numpy()[0]

        # ✅ Get result
        class_index = np.argmax(probabilities)
        result = CLASS_NAMES[class_index]
        confidence = float(probabilities[class_index])

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

        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': '',  # Not used for now
            '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],
            'timestamp': record[5]
        })

    return jsonify(results_list)

@app.route('/gradcam/<filename>')
def get_gradcam(filename):
    """Serve the Grad-CAM overlay image (no-op for now)."""
    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)