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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")
get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
def launch_image_pipe(input):
out = image_pipe(input)
return out[0]['generated_text']
def translate(input_text, source, target):
try:
model = f"Helsinki-NLP/opus-mt-{source}-{target}"
pipe = pipeline("translation", model=model)
translation = pipe(input_text)
return translation[0]['translation_text'], ""
except KeyError:
return "", f"Error: Translation direction {source_readable} to {target} is not supported by Helsinki Translation Models"
def summarize(input):
output = get_completion(input)
summary_origin = output[0]['summary_text']
summary_translated = translate(summary_origin,'en','fr')
return summary_origin, summary_translated[0]
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, references):
with gr.Row():
with gr.Column(scale=4):
# reference_list = "> " + "\n> ".join(references)
# content = f"## {topic}\n"
# content += f"### {description}\n"
# for ref in references:
# content += f"> {ref}\n"
# gr.Markdown(content)
gr.Markdown("""
## Image Captioning
### Upload a image, check what AI understand and have vision on it.
> category: Image-to-Text
> model: [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base)
""")
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 = "Upload a image, check what AI understand and have vision on it."
references = ["category: Image-to-Text",
"model: [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base)"]
image_pipeline_button = create_tabs_header(topic, description, references)
with gr.Row():
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.Row():
with gr.Column(scale=4):
gr.Markdown("""
## Text Summarization and Translation
### Summarize the paragraph and translate it into other language.
> pipeline: summarization, model: [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)
> pipeline: translation, model: [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr)
""")
with gr.Column(scale=1):
text_pipeline_button = gr.Button(value="Process")
with gr.Row():
with gr.Column():
source_text = gr.Textbox(label="Text to summarize", lines=6)
with gr.Column():
summary_textbox = gr.Textbox(lines=3, placeholder="", label="Summarization")
translated_textbox = gr.Textbox(lines=3, placeholder="", label="Translate Result")
text_pipeline_button.click(summarize,
inputs=[source_text],
outputs=[summary_textbox, translated_textbox])
with gr.TabItem("Name Entity"):
gr.Markdown("""
> Name Entity Recognition
""")
create_playground_footer()
playground.launch(share=True) |