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import gradio as gr
from PIL import Image
from transformers import BitsAndBytesConfig, PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import spaces
import torch
import os
access_token = os.getenv('HF_token')
model_id = "selamw/BirdWatcher"
# model_id = "selamw/bird-Identifier"
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
def convert_to_markdown(input_text):
"""Converts bird information text to Markdown format,
making specific keywords bold and adding headings.
Args:
input_text (str): The input text containing bird information.
Returns:
str: The formatted Markdown text.
"""
bold_words = ['Look:', 'Cool Fact!:', 'Habitat:', 'Food:', 'Birdie Behaviors:']
# Split into title and content based on the first ":", handling extra whitespace
title, content = map(str.strip, input_text.split(":", 1))
# Bold the keywords
for word in bold_words:
# content = content.replace(word, f'\n\n**{word}\n')
content = content.replace(word, f'\n\n**{word}')
# content = content.replace(f': **', f':**')
# Construct the Markdown output with headings
formatted_output = f"**{title}**{content}"
return formatted_output.strip()
@spaces.GPU
def infer_fin_pali(image, question):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, quantization_config=bnb_config, token=access_token)
processor = PaliGemmaProcessor.from_pretrained(model_id, token=access_token)
inputs = processor(images=image, text=question, return_tensors="pt").to(device)
predictions = model.generate(**inputs, max_new_tokens=512)
decoded_output = processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n")
# Ensure proper Markdown formatting
formatted_output = convert_to_markdown(decoded_output)
# formatted_output = (decoded_output)
return formatted_output
css = """
#mkd {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
h1 {
text-align: center;
}
h3 {
text-align: center;
}
h2 {
text-align: left;
}
span.gray-text {
color: gray;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1>🦩 BirdWatcher 🦜</h1>")
gr.HTML("<h3>Upload an image of a bird, and the model will generate a detailed description of its species.</h3>")
with gr.Tab(label="Bird Identification"):
with gr.Row():
input_img = gr.Image(label="Input Bird Image")
with gr.Column():
with gr.Row():
question = gr.Text(label="Default Prompt", value="Describe this bird species", elem_id="default-prompt")
with gr.Row():
submit_btn = gr.Button(value="Run")
with gr.Row():
output = gr.Markdown(label="Response") # Use Markdown component to display output
# output = gr.Text(label="Response") # Use Markdown component to display output
submit_btn.click(infer_fin_pali, [input_img, question], [output])
gr.Examples(
[["01.jpg", "Describe this bird species"],
["02.jpg", "Describe this bird species"],
["03.jpg", "Describe this bird species"],
["04.jpg", "Describe this bird species"],
["05.jpg", "Describe this bird species"],
["06.jpg", "Describe this bird species"]],
inputs=[input_img, question],
outputs=[output],
fn=infer_fin_pali,
label='Examples 👇'
)
demo.launch(debug=True) |