Kaushik066 commited on
Commit
9ca0a51
·
verified ·
1 Parent(s): b235544

adding webcam feature

Browse files
Files changed (1) hide show
  1. app.py +44 -6
app.py CHANGED
@@ -1,17 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  import streamlit as st
2
- from transformers import pipeline
3
- from PIL import Image
 
 
 
 
 
 
 
 
 
4
 
5
  pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
6
 
7
  st.title("Hot Dog? Or Not?")
8
 
9
- file_name = st.file_uploader("Upload a hot dog candidate image")
10
 
11
- if file_name is not None:
12
- col1, col2 = st.columns(2)
13
 
14
- image = Image.open(file_name)
 
 
 
 
 
15
  col1.image(image, use_column_width=True)
16
  predictions = pipeline(image)
17
 
 
1
+ # Transformers and its models
2
+ import transformers
3
+
4
+ # For Image Processing
5
+ from transformers import ViTImageProcessor
6
+
7
+ # For Model
8
+ from transformers import ViTModel, ViTConfig
9
+
10
+ # For data augmentation
11
+ from torchvision import transforms, datasets
12
+
13
+ # For GPU
14
+ from transformers import set_seed
15
+ from torch.optim import AdamW
16
+ from accelerate import Accelerator, notebook_launcher
17
+
18
+ # For Data Loaders
19
+ import datasets
20
+ from torch.utils.data import Dataset, DataLoader
21
+
22
+ # For Display
23
+ from tqdm.notebook import tqdm
24
+
25
+ # Other Generic Libraries
26
+ import PIL
27
  import streamlit as st
28
+ import gc
29
+ from glob import glob
30
+ import shutil
31
+ import torch.nn.functional as F
32
+
33
+ # Initialse Globle Variables
34
+ MODEL_TRANSFORMER = 'google/vit-base-patch16-224'
35
+ BATCH_SIZE = 8
36
+
37
+ # Set the device (GPU or CPU)
38
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
39
 
40
  pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
41
 
42
  st.title("Hot Dog? Or Not?")
43
 
44
+ # Read images from directory
45
 
 
 
46
 
47
+ # Read image from Camera
48
+ enable = st.checkbox("Enable camera")
49
+ picture = st.camera_input("Take a picture", disabled=not enable)
50
+ if picture:
51
+ col1, col2 = st.columns(2)
52
+ image = PIL.Image.open(picture)
53
  col1.image(image, use_column_width=True)
54
  predictions = pipeline(image)
55