image examples and demo

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs

README.md

@@ -1,6 +1,6 @@
 ---
 title: Eyegazer Demo
-emoji: ⚡
+emoji: 👀
 colorFrom: indigo
 colorTo: green
 sdk: gradio

app.py

@@ -0,0 +1,86 @@
+import gradio as gr
+import os
+from peft import PeftModel
+from PIL import Image
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+from torchvision.transforms import (
+    CenterCrop,
+    Compose,
+    Normalize,
+    RandomHorizontalFlip,
+    RandomResizedCrop,
+    Resize,
+    ToTensor,
+)
+
+model_name = 'google/vit-large-patch16-224'
+adapter = 'monsoon-nlp/eyegazer-vit-binary'
+
+image_processor = AutoImageProcessor.from_pretrained(model_name)
+
+normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
+train_transforms = Compose(
+    [
+        RandomResizedCrop(image_processor.size["height"]),
+        RandomHorizontalFlip(),
+        ToTensor(),
+        normalize,
+    ]
+)
+
+val_transforms = Compose(
+    [
+        Resize(image_processor.size["height"]),
+        CenterCrop(image_processor.size["height"]),
+        ToTensor(),
+        normalize,
+    ]
+)
+
+model = AutoModelForImageClassification.from_pretrained(
+    model_name,
+    ignore_mismatched_sizes=True,
+    num_labels=2,
+)
+
+lora_model = PeftModel.from_pretrained(model, adapter)
+
+def query(img):
+    pimg = val_transforms(img.convert("RGB"))
+    batch = pimg.unsqueeze(0)
+    op = lora_model(batch)
+    vals = op.logits.tolist()[0]
+
+    if vals[0] > vals[1]:
+        return "Predicted unaffected"
+    else:
+        return "Predicted affected to some degree"
+
+iface = gr.Interface(
+    fn=query,
+    examples=[
+        os.path.join(os.path.dirname(__file__), "images/i1.png"),
+        os.path.join(os.path.dirname(__file__), "images/0a09aa7356c0.png"),
+        os.path.join(os.path.dirname(__file__), "images/0a4e1a29ffff.png"),
+        os.path.join(os.path.dirname(__file__), "images/0c43c79e8cfb.png"),
+        os.path.join(os.path.dirname(__file__), "images/0c7e82daf5a0.png"),
+    ],
+    inputs=[
+        gr.inputs.Image(
+            image_mode='RGB',
+            sources=['upload', 'clipboard'],
+            type='pil',
+            label='Input Fundus Camera Image',
+            show_label=True,
+        ),
+    ],
+    outputs=[
+        gr.Markdown(value="", label="Predicted label"),
+    ],
+    title="ViT retinopathy model",
+    description="Diabetic retinopathy model trained on APTOS 2019 dataset; demonstration, not medical dvice",
+    allow_flagging="never",
+)
+iface.launch()

requirements.txt

@@ -0,0 +1,8 @@
+transformers==4.33.0
+sentencepiece==0.1.97
+peft==0.6.0
+accelerate==0.23.0
+bitsandbytes==0.41.1
+datasets==2.12.0
+torchvision==0.15.2
+pillow==9.5.0