Update app.py

app.py

@@ -1,3 +1,65 @@
 import gradio as gr
+from transformers import pipeline, ViTForImageClassification, ViTImageProcessor
+import numpy as np
+from PIL import Image
+import warnings
+import logging
+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
 
-gr.load("models/ongkn/emikes-classifier").launch()
+logging.basicConfig(level=logging.INFO)
+
+
+model = ViTForImageClassification.from_pretrained("ongkn/emikes-classifier")
+processor = ViTImageProcessor.from_pretrained("ongkn/emikes-classifier")
+
+faceGrabber = FaceGrabber()
+gradCam = GradCam()
+
+targetsForGradCam = [ClassifierOutputTarget(gradCam.category_name_to_index(model, "emi")),
+                       ClassifierOutputTarget(gradCam.category_name_to_index(model, "kes"))]
+targetLayerDff = model.vit.layernorm
+targetLayerGradCam = model.vit.encoder.layer[-2].output
+
+def classify_image(input):
+    face = faceGrabber.grab_faces(np.array(input))
+    if face is None:
+        return "No face detected", 0, input
+    face = Image.fromarray(face)
+    faceResized = face.resize((224, 224))
+    tensorResized = transforms.ToTensor()(faceResized)
+    dffImage = run_dff_on_image(model=model,
+                                target_layer=targetLayerDff,
+                                classifier=model.classifier,
+                                img_pil=faceResized,
+                                img_tensor=tensorResized,
+                                reshape_transform=gradCam.reshape_transform_vit_huggingface,
+                                n_components=5,
+                                top_k=10
+                                )
+    result = gradCam.get_top_category(model, tensorResized)
+    cls = result[0]["label"]
+    clsIdx = gradCam.category_name_to_index(model, cls)
+    clsTarget = ClassifierOutputTarget(clsIdx)
+    gradCamImage = gradCam.run_grad_cam_on_image(model=model,
+                                        target_layer=targetLayerGradCam,
+                                        targets_for_gradcam=[clsTarget],
+                                        input_tensor=tensorResized,
+                                        input_image=faceResized,
+                                        reshape_transform=gradCam.reshape_transform_vit_huggingface)
+    return result[0]["label"], result[0]["score"], face, dffImage, gradCamImage
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs="image",
+    outputs=["text", "number", "image", "image", "image"],
+    title="Attraction Classifier - subjective",
+    description=f"Takes in a (224, 224) image and outputs a class: {'emi', 'kes'}, 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](https://huggingface.co/privacy) for details.
+)
+
+iface.launch()