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import gradio as gr
from fun_advaitbert import predict_CTH
from fun_advaitbert import llm_model_function
from fun_advaitbert import product_explaination
title="<h1 style='color:green;text-align:center;font-size:2vw;'>AdvaitBERT:HS Code AI Explanability Through Mixtral 46.7B </a></h1>"
description = """
AdvaitBERT is modified version of BERT (Bidirectional Encoder Representation for Transformers), \
finetuned on the Text corpus of Indian Customs Declarations. It is trained for performing \
downstream tasks like automating the tariff classification and validation process of Customs \
declarations in realtime. This model may help Customs administration to efficiently use AI assisted \
NLP in realtime Customs process like Assessment, Post Clearance Audit, thereby highlighting classification \
inconsistencies and help in revenue augmentation.
"""
article="<p style='color:black;text-align:right;font-size:1vw;'>Powered by NCTC </a></p>"
css = """
.gradio-container {
width: 100vw !important;
min-height: 100vh !important;
padding:0 !important;
margin:0 !important;
max-width: none !important;
}
"""
footnote = """Note: All rights, including licensing and acceptable use policies, related to the AI models, can be found on their respective model pages on Hugging Face. Powered by NCTC
"""
#Powered by NCTC
# input_txt=gr.Textbox(label='Enter Your Product Descrption',lines=3,)
# textbox = gr.Textbox(container=False,placeholder='Enter text and click the Submit button or press Enter')
textbox = gr.Textbox(label='Enter Your Product Descrption',lines=3,)
textbox_2=textbox
print('textbox',textbox)
print('textbox_2',textbox_2)
chat_prod = gr.Chatbot(label="Product Explanation", layout='panel') #height=300
#chat_Advait = gr.Chatbot(label="Advaitbert Prediction", layout='panel')
chat_alpha = gr.Chatbot(label="AI Explanability", layout='panel')
chat_Advait=gr.Interface(predict_CTH,inputs=textbox,outputs="label",)
submit = gr.Button('Submit', variant='primary',)
submit_second = gr.Button('Submit', variant='secondary',)
#submit2 = gr.Button('Submit', variant='primary',)
retry = gr.Button('🔄Retry', variant='secondary')
undo = gr.Button('↩️Undo', variant='secondary')
with gr.Blocks(css=css) as demo:
gr.HTML(f'<h1><center> {title} </center></h1>')
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=0,min_width=600):
chat_Advait.render()
with gr.Column(scale=1,min_width=600):
chat_alpha.render()
with gr.Row(equal_height=True):
with gr.Column(scale=1):
submit.render()
with gr.Column(scale=1):
undo.render()
with gr.Column(scale=1):
clear = gr.ClearButton(value='🗑️Clear',components=[chat_alpha,chat_prod,textbox])
chat_prod.render()
#submit_second.render()
gr.Markdown(footnote)
textbox.submit(llm_model_function, [textbox, chat_alpha], [textbox, chat_alpha])
textbox_2.submit(product_explaination, [textbox_2, chat_prod], [textbox_2, chat_prod])
submit.click(llm_model_function,[textbox, chat_alpha], [textbox, chat_alpha])
submit.click(product_explaination,[textbox_2, chat_prod], [textbox_2, chat_prod])
undo.click(lambda x:x[:-1], [chat_alpha], [chat_alpha])
undo.click(lambda x:x[:-1], [chat_prod], [chat_prod])
gr.Examples([
['200 SI/SI/SI LPO ALUMINIUM LIDS (QTY: 8820000 PCS/PRICE: 21.'],
],
textbox)
demo.launch(debug=True) |