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import gradio as gr
from PIL import Image
import torch
import torch.nn.functional as F
import numpy as np
from torchvision import models, transforms
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
import os
import datetime
import sqlite3
# === Setup paths and model ===
device = torch.device("cpu")
ADMIN_KEY = "Diabetes_Detection"
image_folder = "collected_images"
os.makedirs(image_folder, exist_ok=True)
# === Load model ===
model = models.resnet50(weights=None)
model.fc = torch.nn.Linear(model.fc.in_features, 2)
model.load_state_dict(torch.load("resnet50_dr_classifier.pth", map_location=device))
model.to(device)
model.eval()
# === Grad-CAM setup ===
target_layer = model.layer4[-1]
cam = GradCAM(model=model, target_layers=[target_layer])
# === Image transform ===
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
# === SQLite setup ===
def init_db():
conn = sqlite3.connect("logs.db")
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE IF NOT EXISTS predictions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp TEXT,
filename TEXT,
prediction TEXT,
confidence REAL
)
""")
conn.commit()
conn.close()
def log_to_db(timestamp, filename, prediction, confidence):
conn = sqlite3.connect("logs.db")
cursor = conn.cursor()
cursor.execute("INSERT INTO predictions (timestamp, filename, prediction, confidence) VALUES (?, ?, ?, ?)",
(timestamp, filename, prediction, confidence))
conn.commit()
conn.close()
init_db() # ✅ Initialize table
# === Prediction Function ===
def predict_retinopathy(image):
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
img = image.convert("RGB").resize((224, 224))
img_tensor = transform(img).unsqueeze(0).to(device)
with torch.no_grad():
output = model(img_tensor)
probs = F.softmax(output, dim=1)
pred = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred].item()
label = "Diabetic Retinopathy (DR)" if pred == 0 else "No DR"
# Grad-CAM
rgb_img_np = np.array(img).astype(np.float32) / 255.0
rgb_img_np = np.ascontiguousarray(rgb_img_np)
grayscale_cam = cam(input_tensor=img_tensor, targets=[ClassifierOutputTarget(pred)])[0]
cam_image = show_cam_on_image(rgb_img_np, grayscale_cam, use_rgb=True)
cam_pil = Image.fromarray(cam_image)
# Save image and log
filename = f"{timestamp}_{label.replace(' ', '_')}.png"
image_path = os.path.join(image_folder, filename)
image.save(image_path)
log_to_db(timestamp, image_path, label, confidence)
return cam_pil, f"{label} (Confidence: {confidence:.2f})"
# === Gradio Interface ===
with gr.Blocks() as demo:
gr.Markdown("## 🧠 DR Detection with Grad-CAM + SQLite Logging")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Retinal Image")
cam_output = gr.Image(type="pil", label="Grad-CAM")
prediction_output = gr.Text(label="Prediction")
run_button = gr.Button("Submit")
run_button.click(
fn=predict_retinopathy,
inputs=image_input,
outputs=[cam_output, prediction_output]
)
demo.launch()
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