jchen8000 commited on
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1 Parent(s): 0fad869

Update app.py

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  1. app.py +53 -30
app.py CHANGED
@@ -114,39 +114,62 @@ additional_inputs = [
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  gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
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  ]
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- def greet(name):
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- return f"Hello, {name}!"
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- with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
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- # Set the title and description for the app.
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- gr.Markdown("# Simple Gradio Greeter")
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- gr.Markdown("Enter your name and get a personalized greeting!")
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-
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- # Define the input component (a text box for the name).
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- name_input = gr.Textbox(
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- lines=2,
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- placeholder="Enter your name here...",
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- label="Your Name"
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- )
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- # Define the output component (a text box for the greeting).
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- greeting_output = gr.Textbox(label="Greeting")
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-
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- # Add a button that will trigger the 'greet' function when clicked.
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- # The 'fn' argument specifies the function to call.
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- # The 'inputs' argument specifies which component's value to pass to 'fn'.
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- # The 'outputs' argument specifies which component will display the return value of 'fn'.
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- submit_button = gr.Button("Get Greeting")
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- submit_button.click(
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- fn=greet,
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- inputs=name_input,
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- outputs=greeting_output
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- )
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- # Launch the Gradio app.
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- # The `share=True` argument creates a public, shareable link (useful for demos).
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- # In a local environment, it will also open a local URL.
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- demo.launch()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
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  ]
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+ # Create the Gradio interface
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+ with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
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+ with gr.Tab("Indexing"):
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+ pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
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+ index_button = gr.Button("Index PDF")
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+ load_sample = gr.Button("Alternatively, Load and Index [Attention Is All You Need.pdf] as a Sample")
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+ sample_description = gr.Markdown("This sample PDF is a seminal paper in the field of machine learning, titled 'Attention Is All You Need' at https://arxiv.org/abs/1706.03762. It introduces the Transformer model, which has become foundational in natural language processing.")
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+ index_output = gr.Textbox(label="Indexing Status")
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+ index_button.click(index_pdf, inputs=pdf_input, outputs=index_output)
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+ load_sample.click(load_sample_pdf, inputs=None, outputs=index_output)
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+
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+ with gr.Tab("Chatbot"):
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+ gr.ChatInterface(
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+ fn=generate_response,
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+ chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
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+ examples=examples_questions,
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+ additional_inputs=additional_inputs,
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+ cache_examples=False,
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+ )
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+
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+ # Launch the Gradio app
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+ demo.launch()
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+ # def greet(name):
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+ # return f"Hello, {name}!"
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+ # with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
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+ # # Set the title and description for the app.
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+ # gr.Markdown("# Simple Gradio Greeter")
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+ # gr.Markdown("Enter your name and get a personalized greeting!")
 
 
 
 
 
 
 
 
 
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+ # # Define the input component (a text box for the name).
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+ # name_input = gr.Textbox(
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+ # lines=2,
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+ # placeholder="Enter your name here...",
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+ # label="Your Name"
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+ # )
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+
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+ # # Define the output component (a text box for the greeting).
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+ # greeting_output = gr.Textbox(label="Greeting")
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+
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+ # # Add a button that will trigger the 'greet' function when clicked.
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+ # # The 'fn' argument specifies the function to call.
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+ # # The 'inputs' argument specifies which component's value to pass to 'fn'.
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+ # # The 'outputs' argument specifies which component will display the return value of 'fn'.
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+ # submit_button = gr.Button("Get Greeting")
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+ # submit_button.click(
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+ # fn=greet,
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+ # inputs=name_input,
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+ # outputs=greeting_output
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+ # )
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+
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+ # # Launch the Gradio app.
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+ # # The `share=True` argument creates a public, shareable link (useful for demos).
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+ # # In a local environment, it will also open a local URL.
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+ # demo.launch()