Upload 5 files
Browse files- Dockerfile +24 -0
- app.py +212 -0
- efficientnet_glam_best.pt +3 -0
- glam_efficient_model.py +103 -0
- requirements.txt +10 -0
Dockerfile
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FROM python:3.9-slim
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# 2️⃣ Set working directory
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WORKDIR /app
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# 3️⃣ Install required system dependencies (fixes libGL and libgthread errors)
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RUN apt-get update && \
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apt-get install -y libgl1-mesa-glx libglib2.0-0 && \
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rm -rf /var/lib/apt/lists/*
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# 4️⃣ Copy requirements
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COPY requirements.txt .
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# 5️⃣ Install Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# 6️⃣ Copy all files from the root of your project
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COPY . .
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# 7️⃣ Expose the port
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EXPOSE 7860
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# 8️⃣ Command to run the app
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CMD ["python", "app.py"]
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app.py
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from flask import Flask, request, jsonify, send_file
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from PIL import Image
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import torch
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import torch.nn.functional as F
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from torchvision import transforms
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import os
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import numpy as np
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from datetime import datetime
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import sqlite3
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import torch.nn as nn
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import cv2
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# ✅ New Grad-CAM++ imports
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from pytorch_grad_cam import GradCAMPlusPlus
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from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
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from pytorch_grad_cam.utils.image import show_cam_on_image
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# ✅ Import Hugging Face-style GLAM EfficientNet model
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from glam_efficientnet_model import GLAMEfficientNetForClassification, GLAMEfficientNetConfig
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app = Flask(__name__)
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# ✅ Directory and database path
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OUTPUT_DIR = '/tmp/results'
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if not os.path.exists(OUTPUT_DIR):
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os.makedirs(OUTPUT_DIR)
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DB_PATH = os.path.join(OUTPUT_DIR, 'results.db')
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def init_db():
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"""Initialize SQLite database for storing results."""
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conn = sqlite3.connect(DB_PATH)
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cursor = conn.cursor()
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS results (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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image_filename TEXT,
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prediction TEXT,
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confidence REAL,
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gradcam_filename TEXT,
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gradcam_gray_filename TEXT,
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timestamp TEXT
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)
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""")
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conn.commit()
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conn.close()
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init_db()
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# ✅ Load GLAM EfficientNet Model
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config = GLAMEfficientNetConfig()
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model = GLAMEfficientNetForClassification(config)
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model.load_state_dict(torch.load('efficientnet_glam_best.pt', map_location='cpu'))
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model.eval()
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# ✅ Class Names
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CLASS_NAMES = ["Advanced", "Early", "Normal"]
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# ✅ Transformation for input images
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transform = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225]),
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])
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@app.route('/')
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def home():
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"""Check that the API is working."""
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return "Glaucoma Detection Flask API (EfficientNet + GLAM) is running!"
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@app.route("/test_file")
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def test_file():
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"""Check if the .pt model file is present and readable."""
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filepath = "efficientnet_glam_best.pt"
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if os.path.exists(filepath):
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return f"✅ Model file found at: {filepath}"
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else:
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return "❌ Model file NOT found."
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@app.route('/predict', methods=['POST'])
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def predict():
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"""Perform prediction and save results (including Grad-CAM++) to the database."""
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if 'file' not in request.files:
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return jsonify({'error': 'No file uploaded'}), 400
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uploaded_file = request.files['file']
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if uploaded_file.filename == '':
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return jsonify({'error': 'No file selected'}), 400
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try:
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# ✅ Save the uploaded image
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timestamp = int(datetime.now().timestamp())
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uploaded_filename = f"uploaded_{timestamp}.png"
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uploaded_file_path = os.path.join(OUTPUT_DIR, uploaded_filename)
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uploaded_file.save(uploaded_file_path)
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# ✅ Perform prediction
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img = Image.open(uploaded_file_path).convert('RGB')
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input_tensor = transform(img).unsqueeze(0)
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# ✅ Get prediction
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output = model(input_tensor) # Dict with "logits"
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probabilities = F.softmax(output["logits"], dim=1).cpu().detach().numpy()[0]
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class_index = np.argmax(probabilities)
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result = CLASS_NAMES[class_index]
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confidence = float(probabilities[class_index])
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# ✅ Grad-CAM++ setup
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# IMPORTANT: Choose the target layer from the GLAM EfficientNet model.
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# For example, use the final convolutional block:
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target_layer = model.features[-1]
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cam_model = GradCAMPlusPlus(model=model, target_layers=[target_layer])
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# ✅ Get Grad-CAM++ map
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cam_output = cam_model(input_tensor=input_tensor, targets=[ClassifierOutputTarget(class_index)])[0]
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# ✅ Create RGB overlay
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original_img = np.asarray(img.resize((224, 224)), dtype=np.float32) / 255.0
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overlay = show_cam_on_image(original_img, cam_output, use_rgb=True)
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# ✅ Create grayscale version
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cam_normalized = np.uint8(255 * cam_output)
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# ✅ Save overlay
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gradcam_filename = f"gradcam_{timestamp}.png"
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gradcam_file_path = os.path.join(OUTPUT_DIR, gradcam_filename)
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cv2.imwrite(gradcam_file_path, cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
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# ✅ Save grayscale
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gray_filename = f"gradcam_gray_{timestamp}.png"
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gray_file_path = os.path.join(OUTPUT_DIR, gray_filename)
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cv2.imwrite(gray_file_path, cam_normalized)
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# ✅ Save results to database
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conn = sqlite3.connect(DB_PATH)
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cursor = conn.cursor()
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cursor.execute("""
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INSERT INTO results (image_filename, prediction, confidence, gradcam_filename, gradcam_gray_filename, timestamp)
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VALUES (?, ?, ?, ?, ?, ?)
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""", (uploaded_filename, result, confidence, gradcam_filename, gray_filename, datetime.now().isoformat()))
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conn.commit()
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conn.close()
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# ✅ Return results
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return jsonify({
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'prediction': result,
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'confidence': confidence,
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'normal_probability': float(probabilities[0]),
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'early_glaucoma_probability': float(probabilities[1]),
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'advanced_glaucoma_probability': float(probabilities[2]),
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'gradcam_image': gradcam_filename,
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'gradcam_gray_image': gray_filename,
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'image_filename': uploaded_filename
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@app.route('/results', methods=['GET'])
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def results():
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"""List all results from the SQLite database."""
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conn = sqlite3.connect(DB_PATH)
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cursor = conn.cursor()
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cursor.execute("SELECT * FROM results ORDER BY timestamp DESC")
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results_data = cursor.fetchall()
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conn.close()
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results_list = []
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for record in results_data:
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results_list.append({
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'id': record[0],
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'image_filename': record[1],
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'prediction': record[2],
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'confidence': record[3],
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'gradcam_filename': record[4],
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'gradcam_gray_filename': record[5],
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'timestamp': record[6]
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})
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return jsonify(results_list)
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@app.route('/gradcam/<filename>')
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def get_gradcam(filename):
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"""Serve the Grad-CAM overlay image."""
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filepath = os.path.join(OUTPUT_DIR, filename)
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if os.path.exists(filepath):
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return send_file(filepath, mimetype='image/png')
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else:
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return jsonify({'error': 'File not found'}), 404
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@app.route('/image/<filename>')
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def get_image(filename):
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"""Serve the original uploaded image."""
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filepath = os.path.join(OUTPUT_DIR, filename)
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if os.path.exists(filepath):
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return send_file(filepath, mimetype='image/png')
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else:
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return jsonify({'error': 'File not found'}), 404
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=7860)
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efficientnet_glam_best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:bcdc2e2bc5aef943b6658e2e2e1fd62a856d860aef97e7f2bdc2ca3b03a8fe5b
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size 45758832
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glam_efficient_model.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import PreTrainedModel, PretrainedConfig
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from transformers import EfficientNetModel
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from typing import Optional, Union
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# --------------------------------------------------
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# Import your GLAM, SwinWindowAttention blocks here
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# --------------------------------------------------
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# from .glam_module import GLAM
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# from .swin_module import SwinWindowAttention
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class GLAMEfficientNetConfig(PretrainedConfig):
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"""Hugging Face-style configuration for GLAM EfficientNet."""
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model_type = "glam_efficientnet"
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def __init__(self,
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num_classes: int = 3,
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embed_dim: int = 512,
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num_heads: int = 8,
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window_size: int = 7,
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reduction_ratio: int = 8,
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dropout: float = 0.5,
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**kwargs):
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super().__init__(**kwargs)
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self.num_classes = num_classes
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.reduction_ratio = reduction_ratio
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self.dropout = dropout
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class GLAMEfficientNetForClassification(PreTrainedModel):
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"""Hugging Face-style Model for EfficientNet + GLAM + Swin Architecture."""
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config_class = GLAMEfficientNetConfig
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def __init__(self, config: GLAMEfficientNetConfig):
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super().__init__(config)
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# 1) EfficientNet Backbone
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self.features = EfficientNetModel.from_pretrained("google/efficientnet-b0").features
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self.conv1x1 = nn.Conv2d(1280, config.embed_dim, kernel_size=1)
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# 2) Swin Attention Block
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self.swin_attn = SwinWindowAttention(
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embed_dim=config.embed_dim,
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window_size=config.window_size,
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num_heads=config.num_heads,
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dropout=config.dropout
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)
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self.pre_attn_norm = nn.LayerNorm(config.embed_dim)
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self.post_attn_norm = nn.LayerNorm(config.embed_dim)
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58 |
+
|
59 |
+
# 3) GLAM Block
|
60 |
+
self.glam = GLAM(in_channels=config.embed_dim, reduction_ratio=config.reduction_ratio)
|
61 |
+
|
62 |
+
# 4) Self-Adaptive Gating
|
63 |
+
self.gate_fc = nn.Linear(config.embed_dim, 1)
|
64 |
+
|
65 |
+
# Final classification
|
66 |
+
self.dropout = nn.Dropout(config.dropout)
|
67 |
+
self.classifier = nn.Linear(config.embed_dim, config.num_classes)
|
68 |
+
|
69 |
+
def forward(self, pixel_values, labels=None, **kwargs):
|
70 |
+
# 1) Extract EfficientNet Features
|
71 |
+
feats = self.features(pixel_values).last_hidden_state
|
72 |
+
feats = self.conv1x1(feats)
|
73 |
+
|
74 |
+
B, C, H, W = feats.shape
|
75 |
+
|
76 |
+
# 2) Transformer Branch
|
77 |
+
x_perm = feats.permute(0, 2, 3, 1).contiguous()
|
78 |
+
x_norm = self.pre_attn_norm(x_perm).permute(0, 3, 1, 2).contiguous()
|
79 |
+
x_norm = self.dropout(x_norm)
|
80 |
+
|
81 |
+
T_out = self.swin_attn(x_norm)
|
82 |
+
|
83 |
+
T_out = self.post_attn_norm(T_out.permute(0, 2, 3, 1).contiguous())
|
84 |
+
T_out = T_out.permute(0, 3, 1, 2).contiguous()
|
85 |
+
|
86 |
+
# 3) GLAM Branch
|
87 |
+
G_out = self.glam(feats)
|
88 |
+
|
89 |
+
# 4) Self-Adaptive Gating
|
90 |
+
gap_feats = F.adaptive_avg_pool2d(feats, (1, 1)).view(B, C)
|
91 |
+
g = torch.sigmoid(self.gate_fc(gap_feats)).view(B, 1, 1, 1)
|
92 |
+
|
93 |
+
F_out = g * T_out + (1 - g) * G_out
|
94 |
+
|
95 |
+
# 5) Final Pooling + Classifier
|
96 |
+
pooled = F.adaptive_avg_pool2d(F_out, (1, 1)).view(B, -1)
|
97 |
+
logits = self.classifier(self.dropout(pooled))
|
98 |
+
|
99 |
+
loss = None
|
100 |
+
if labels is not None:
|
101 |
+
loss = F.cross_entropy(logits, labels)
|
102 |
+
|
103 |
+
return {"loss": loss, "logits": logits}
|
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Flask
|
2 |
+
torch
|
3 |
+
torchvision
|
4 |
+
Pillow
|
5 |
+
numpy
|
6 |
+
opencv-python
|
7 |
+
|
8 |
+
firebase-admin
|
9 |
+
psycopg2-binary
|
10 |
+
grad-cam
|