Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+import cv2
+import numpy as np
+
+# 1. Download OpenCV Haarcascades for eye tracking
+import urllib.request
+import os
+cascade_path = 'haarcascade_eye.xml'
+if not os.path.exists(cascade_path):
+    urllib.request.urlretrieve(
+        'https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_eye.xml', 
+        cascade_path
+    )
+eye_cascade = cv2.CascadeClassifier(cascade_path)
+
+# 2. Re-initialize and load the model (Using your weights)
+model = models.mobilenet_v2(weights=None)
+# Ensure this matches exactly how you defined it in the Masterpiece training step
+model.classifier = nn.Sequential(
+    nn.Dropout(p=0.5),
+    nn.Linear(model.last_channel, 2)
+)
+model.load_state_dict(torch.load('ddobj_model.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# 3. Transforms (Grayscale is key to matching the MRL dataset!)
+transform = transforms.Compose([
+    transforms.Grayscale(num_output_channels=3), # Convert to 3-channel grayscale
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# 4. The Smart Prediction Function
+def predict_drowsiness(image):
+    # Convert Gradio image to OpenCV format
+    img_cv = np.array(image)
+    gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY)
+    
+    # Detect eyes in the image
+    eyes = eye_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+    
+    if len(eyes) == 0:
+        return "ERROR: Could not detect any eyes in the image. Please upload a clear face photo.", None
+    
+    # Take the first detected eye (largest/clearest)
+    (x, y, w, h) = eyes[0]
+    
+    # Crop the eye from the original image
+    eye_crop = img_cv[y:y+h, x:x+w]
+    
+    # Convert the cropped eye back to PIL for PyTorch
+    eye_pil = Image.fromarray(eye_crop)
+    input_tensor = transform(eye_pil).unsqueeze(0)
+    
+    # Run the model
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted = torch.max(outputs, 1)
+        
+    classes = ["DROWSY ALERT! 🚨 (Eyes Closed)", "NOT DROWSY ✅ (Eyes Open)"]
+    result = classes[predicted.item()]
+    
+    # Return the prediction AND show the user the exact crop the model looked at
+    return result, eye_pil
+
+# 5. Build the UI
+interface = gr.Interface(
+    fn=predict_drowsiness,
+    inputs=gr.Image(label="Upload Full Face Photo"),
+    outputs=[
+        gr.Textbox(label="DDobj System Status"), 
+        gr.Image(label="What the AI saw (Eye Crop)")
+    ],
+    title="DDobj: Driver Drowsiness Detection",
+    description="Upload a photo. The system will automatically locate the eyes, isolate them, and analyze them for fatigue.",
+    theme="default"
+)
+
+if __name__ == "__main__":
+    interface.launch()