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import gradio as gr
import base64
import os
api_key = os.getenv('API_KEY')
def predict(input, file_input):
print("input:", input)
print("file_input:", file_input.name)
from gradio_client import Client
client = Client(api_key)
extract_result = client.predict(
input,
file_input.name,
fn_index=1
)
if extract_result:
print(extract_result)
return extract_result
else:
return "Too many user, please wait a monument!"
def view_pdf(pdf_file):
with open(pdf_file.name, 'rb') as f:
pdf_data = f.read()
# print("pdf_file", pdf_file)
# pdf_data = pdf_file
b64_data = base64.b64encode(pdf_data).decode('utf-8')
# print("b64_data", b64_data)
return f"<embed src='data:application/pdf;base64,{b64_data}' type='application/pdf' width='100%' height='700px' />"
en_1 = ["""could you please help me extract the information of 'title'/'journal'/'year'/'author'/'institution'/'email' from the previous content in a markdown table format?
If any of this information was not available in the paper, please replaced it with the string `""`, If the property contains multiple entities, please use a list to contain.
"""]
en_2 = ["""could you please help me extract the information of 'title'/'journal'/'year'/'author'/'institution'/'email' from the previous content in a json format?
If any of this information was not available in the paper, please replaced it with the string `""`, If the property contains multiple entities, please use a list to contain.
"""]
examples = [en_1, en_2]
with gr.Blocks(title="ChatPaperGPT") as demo:
gr.Markdown(
'''<p align="center" width="100%">
<img src="https://big-cheng.com/img/pdf.png" alt="pdf-logo" width="50"/>
<p>
<h1 align="center"> Paper Extract GPT </h1>
<p> How to use:
<br> <strong>#1</strong>: Upload your pdf.
<br> <strong>#2</strong>: Click the View PDF button to view it.
<br> <strong>#3</strong>: Enter your extraction prompt in the input box (of course, you can click example to test).
<br> <strong>#4</strong>: Click Generate to extract, and the extracted information will be displayed in markdown form.
</p>
'''
)
with gr.Row():
with gr.Column():
gr.Markdown('## Upload PDF')
file_input = gr.File(type="filepath")
viewer_button = gr.Button("View PDF")
file_out = gr.HTML()
with gr.Column():
with gr.Row():
model_input = gr.Textbox(lines=7, placeholder='Input prompt about extract information from paper',
label='Input')
with gr.Row():
gen = gr.Button("Generate")
clr = gr.Button("Clear")
example = gr.Examples(examples=examples, inputs=model_input)
with gr.Row():
outputs = gr.Markdown(label='Output', show_label=True, value="""| Title | Journal | Year | Author | Institution | Email |
|---------------------------------------------|--------------------|------|-----------------------------------------------|-------------------------------------------------------|-----------------------|
| Paleomagnetic Study of Deccan Traps from Jabalpur to Amarkantak, Central India | J. Geomag. Geoelectr. | 1973 | R. K. VERMA, G. PULLAIAH, G.R. ANJANEYULU, P. K. MALLIK | National Geophysical Research Institute, Hyderabad, and Indian School o f Mines, Dhanbad | "" |
""")
inputs = [model_input, file_input]
gen.click(fn=predict, inputs=inputs, outputs=outputs)
clr.click(fn=lambda value: [gr.update(value=""), gr.update(value="")], inputs=clr,
outputs=[model_input, outputs])
viewer_button.click(view_pdf, inputs=file_input, outputs=file_out)
# parser_button.click(extract_text, inputs=file_input, outputs=[xml_out, md_out, rich_md_out])
demo.launch()
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