remove unnecessary stuff from app.py

app.py

@@ -2,11 +2,8 @@ import gradio as gr
 from transformers import pipeline, ViTForImageClassification, ViTImageProcessor
 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
@@ -15,64 +12,8 @@ from face_grab import FaceGrabber
 from gradcam import GradCam
 from torchvision import transforms
 
-
 logging.basicConfig(level=logging.INFO)
 
-def grab_faces(img: np.ndarray) -> Optional[np.ndarray]:
-    cascades = [
-        "haarcascade_frontalface_default.xml",
-        "haarcascade_frontalface_alt.xml",
-        "haarcascade_frontalface_alt2.xml",
-        "haarcascade_frontalface_alt_tree.xml"
-    ]
-
-    detector = dlib.get_frontal_face_detector() # load face detector
-    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks_GTX.dat") # load face predictor
-    mmod = dlib.cnn_face_detection_model_v1("mmod_human_face_detector.dat") # load face detector
-
-    paddingBy = 0.1 # padding by 10%
-
-    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) # convert to grayscale
-
-    detected = None
-
-    if detected is None:
-        faces = detector(gray) # detect faces
-        if len(faces) > 0:
-            detected = faces[0]
-            detected = (detected.left(), detected.top(), detected.width(), detected.height())
-            logging.info("Face detected by dlib")
-
-    if detected is None:
-        faces = mmod(img)
-        if len(faces) > 0:
-            detected = faces[0]
-            detected = (detected.rect.left(), detected.rect.top(), detected.rect.width(), detected.rect.height())
-            logging.info("Face detected by mmod")
-
-    for cascade in cascades:
-        cascadeClassifier = cv.CascadeClassifier(cv.data.haarcascades + cascade)
-        faces = cascadeClassifier.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) # detect faces
-        if len(faces) > 0:
-            detected = faces[0]
-            logging.info(f"Face detected by {cascade}")
-            break
-
-    if detected is not None: # if face detected
-        x, y, w, h = detected # grab first face
-        padW = int(paddingBy * w) # get padding width
-        padH = int(paddingBy * h) # get padding height
-        imgH, imgW, _ = img.shape # get image dims
-        x = max(0, x - padW)
-        y = max(0, y - padH)
-        w = min(imgW - x, w + 2 * padW)
-        h = min(imgH - y, h + 2 * padH)
-        x = max(0, x - (w - detected[2]) // 2) # center the face horizontally
-        y = max(0, y - (h - detected[3]) // 2) # center the face vertically
-        face = img[y:y+h, x:x+w] # crop face
-        return face
-
-    return None
 
 model = ViTForImageClassification.from_pretrained("ongkn/attraction-classifier")
 processor = ViTImageProcessor.from_pretrained("ongkn/attraction-classifier")
@@ -92,7 +33,6 @@ def classify_image(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,
@@ -122,4 +62,5 @@ iface = gr.Interface(
     title="Attraction Classifier - subjective",
     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()

gradcam.py

@@ -21,6 +21,8 @@ warnings.filterwarnings("ignore")
 
 logging.basicConfig(level=logging.INFO)
 
+
+
 class HuggingfaceToTensorModelWrapper(torch.nn.Module):
     def __init__(self, model):
         super(HuggingfaceToTensorModelWrapper, self).__init__()
@@ -29,6 +31,8 @@ class HuggingfaceToTensorModelWrapper(torch.nn.Module):
     def forward(self, x):
         return self.model(x).logits
 
+
+
 class GradCam():
     def __init__(self):
         pass