Update app.py

app.py

@@ -4,8 +4,16 @@ import numpy as np
 from PIL import Image
 import cv2 as cv
 import dlib
+import warnings
 import logging
 from typing import Optional
+from pytorch_grad_cam import run_dff_on_image, GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+import torch
+from face_grab import FaceGrabber
+from gradcam import GradCam
+from torchvision import transforms
 
 
 logging.basicConfig(level=logging.INFO)
@@ -71,19 +79,49 @@ processor = ViTImageProcessor.from_pretrained("ongkn/attraction-classifier")
 
 pipe = pipeline("image-classification", model=model, feature_extractor=processor)
 
+faceGrabber = FaceGrabber()
+gradCam = GradCam()
+
+targetsForGradCam = [ClassifierOutputTarget(gradCam.category_name_to_index(model, "pos")),
+                       ClassifierOutputTarget(gradCam.category_name_to_index(model, "neg"))]
+targetLayerDff = model.vit.layernorm
+targetLayerGradCam = model.vit.encoder.layer[-2].output
+
 def classify_image(input):
-    face = grab_faces(np.array(input))
+    face = faceGrabber.grab_faces(np.array(input))
     if face is None:
         return "No face detected", 0, input
     face = Image.fromarray(face)
+    imgTensor = transforms.ToTensor()(face)
+    tensor = transforms.ToTensor()(face)
+    dffImage = run_dff_on_image(model=model,
+                                target_layer=targetLayerDff,
+                                classifier=model.classifier,
+                                img_pil=face,
+                                img_tensor=tensor,
+                                reshape_transform=gradCam.reshape_transform_vit_huggingface,
+                                n_components=5,
+                                top_k=10,
+                                threshold=0,
+                                )
+    gradCamImage = gradCam.run_grad_cam_on_image(model=model,
+                                                 target_layer=targetLayerGradCam,
+                                                 classifier=model.classifier,
+                                                 img_pil=face,
+                                                 img_tensor=tensor,
+                                                 reshape_transform=gradCam.reshape_transform_vit_huggingface,
+                                                 n_components=5,
+                                                 top_k=10,
+                                                 threshold=0,
+                                                 )
     result = pipe(face)
-    return result[0]["label"], result[0]["score"], face
+    return result[0]["label"], result[0]["score"], face, dffImage, gradCamImage
 
 iface = gr.Interface(
     fn=classify_image,
     inputs="image",
-    outputs=["text", "number", "image"],
+    outputs=["text", "number", "image", "image", "image"],
     title="Attraction Classifier - subjective",
-    description=f"Takes in a (224, 224) image and outputs an attraction class: {'pos', 'neg'}. Face detection, cropping, and resizing are done internally. Uploaded images are not stored by us, but may be stored by HF. Refer to their privacy policy for details."
+    description=f"Takes in a (224, 224) image and outputs an attraction class: {'pos', 'neg'}, along with a GradCam/DFF explanation. Face detection, cropping, and resizing are done internally. Uploaded images are not stored by us, but may be stored by HF. Refer to their privacy policy for details."
 )
 iface.launch()