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import gradio as gr
from transformers import pipeline
playground = gr.Blocks()
image_pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
def launch_image_pipe(input):
out = image_pipe(input)
return out[0]['generated_text']
def create_playground_header():
gr.Markdown("""
# 🤗 Hugging Face Labs
**Explore different LLM on Hugging Face platform. Just play and enjoy**
""")
def create_playground_footer():
gr.Markdown("""
**To Learn More about 🤗 Hugging Face, [Click Here](https://huggingface.co/docs)**
""")
def create_tabs_header(topic, description):
with gr.Row():
with gr.Column(scale=4):
gr.Markdown(f"""
## {topic}
> {description}
""")
with gr.Column(scale=1):
test_pipeline_button = gr.Button(value="Process")
return test_pipeline_button
with playground:
create_playground_header()
with gr.Tabs():
with gr.TabItem("Image"):
topic = "Image Captioning"
description = ["model='Salesforce/blip-image-captioning-base'"]
# image_pipeline_button = create_tabs_header("Image Captioning")
image_pipeline_button = create_tabs_header(topic, description)
gr.Markdown("""
> model='Salesforce/blip-image-captioning-base'
""")
with gr.Row(visible=True) as use_pipeline:
with gr.Column():
img = gr.Image(type='pil')
with gr.Column():
generated_textbox = gr.Textbox(lines=2, placeholder="", label="Generated Text")
image_pipeline_button.click(launch_image_pipe,
inputs=[img],
outputs=[generated_textbox])
with gr.TabItem("Text"):
gr.Markdown("""
> Text Summarization and Translation
""")
with gr.TabItem("Name Entity"):
gr.Markdown("""
> Name Entity Recognition
""")
create_playground_footer()
playground.launch(share=True) |