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Update app.py
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app.py
CHANGED
@@ -1,31 +1,49 @@
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import gradio as gr
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playground = gr.Blocks()
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def create_playground_header():
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gr.Markdown("""
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-
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**
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""")
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def create_playground_footer():
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gr.Markdown("""
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""")
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with playground:
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create_playground_header()
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with gr.Tabs():
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with gr.TabItem("Text"):
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gr.Markdown("""
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> Text Summarization and Translation
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""")
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with gr.TabItem("Image"):
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gr.Markdown("""
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> Image Captioning
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""")
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with gr.TabItem("Name Entity"):
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gr.Markdown("""
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> Name Entity Recognition
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import gradio as gr
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from transformers import pipeline
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playground = gr.Blocks()
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image_pipe = pipeline("image-to-text",
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model="Salesforce/blip-image-captioning-base")
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def create_playground_header():
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gr.Markdown("""
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#🤗 Hugging Face Labs
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**Explore different LLM on Hugging Face platform. Just play and enjoy**
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""")
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def create_playground_footer():
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gr.Markdown("""
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**To Learn More about 🤗 Hugging Face, [Click Here](https://huggingface.co/docs)**
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""")
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def create_tabs_header(description):
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with gr.Row():
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with gr.Column(scale=4):
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gr.Markdown(f"**{description}**")
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with gr.Column(scale=1):
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test_pipeline_button = gr.Button(value="Process")
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return test_pipeline_button
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with playground:
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create_playground_header()
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with gr.Tabs():
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with gr.TabItem("Image"):
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image_pipeline_button = create_tabs_header("Image Captioning")
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with gr.Row(visible=True) as use_pipeline:
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with gr.Column():
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img = gr.Image(type='pil')
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with gr.Column():
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generated_textbox = gr.Textbox(lines=2, placeholder="", label="Generated Text")
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image_pipeline_button.click(image_pipe,
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inputs=[img],
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outputs=[generated_textbox]])
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with gr.TabItem("Text"):
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gr.Markdown("""
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> Text Summarization and Translation
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""")
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with gr.TabItem("Name Entity"):
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gr.Markdown("""
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> Name Entity Recognition
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