Spaces:
Running
Running
sonu
commited on
Commit
·
ab1e39e
1
Parent(s):
a3c06ef
Add application file
Browse files- app.py +65 -0
- examples/example1.jpg +0 -0
- requirements.txt +5 -0
app.py
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import torch
|
3 |
+
from PIL import Image
|
4 |
+
from transformers import AutoProcessor, AutoModelForCausalLM
|
5 |
+
|
6 |
+
# Load model and processor
|
7 |
+
processor = AutoProcessor.from_pretrained("sonukiller/git-base-cartoon")
|
8 |
+
model = AutoModelForCausalLM.from_pretrained("sonukiller/git-base-cartoon")
|
9 |
+
|
10 |
+
# Move model to GPU if available
|
11 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
12 |
+
model = model.to(device)
|
13 |
+
|
14 |
+
def generate_caption(image):
|
15 |
+
"""
|
16 |
+
Generate a caption for the given image using the custom model
|
17 |
+
"""
|
18 |
+
# Preprocess the image
|
19 |
+
inputs = processor(images=image, return_tensors="pt").to(device)
|
20 |
+
|
21 |
+
# Generate caption
|
22 |
+
with torch.no_grad():
|
23 |
+
generated_ids = model.generate(
|
24 |
+
pixel_values=inputs.pixel_values,
|
25 |
+
max_length=50,
|
26 |
+
num_beams=4,
|
27 |
+
early_stopping=True
|
28 |
+
)
|
29 |
+
|
30 |
+
# Decode the generated ids to text
|
31 |
+
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
32 |
+
|
33 |
+
return generated_caption
|
34 |
+
|
35 |
+
# Create Gradio interface
|
36 |
+
with gr.Blocks(title="Custom Image Captioning", css="footer {visibility: hidden}") as demo:
|
37 |
+
gr.Markdown("# Custom Image Captioning Model")
|
38 |
+
gr.Markdown("Upload an image and get a caption generated by a custom-trained model.")
|
39 |
+
|
40 |
+
with gr.Row():
|
41 |
+
with gr.Column():
|
42 |
+
input_image = gr.Image(type="pil", label="Input Image")
|
43 |
+
caption_button = gr.Button("Generate Caption")
|
44 |
+
|
45 |
+
with gr.Column():
|
46 |
+
output_text = gr.Textbox(label="Generated Caption")
|
47 |
+
|
48 |
+
caption_button.click(
|
49 |
+
fn=generate_caption,
|
50 |
+
inputs=input_image,
|
51 |
+
outputs=output_text
|
52 |
+
)
|
53 |
+
|
54 |
+
gr.Examples(
|
55 |
+
examples=[
|
56 |
+
"examples/example1.jpg",
|
57 |
+
],
|
58 |
+
inputs=input_image,
|
59 |
+
outputs=output_text,
|
60 |
+
fn=generate_caption,
|
61 |
+
cache_examples=True,
|
62 |
+
)
|
63 |
+
|
64 |
+
# Launch the app
|
65 |
+
demo.launch()
|
examples/example1.jpg
ADDED
![]() |
requirements.txt
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch==2.0.1
|
2 |
+
Pillow==9.5.0
|
3 |
+
transformers==4.31.0
|
4 |
+
gradio==3.38.0
|
5 |
+
accelerate==0.21.0
|