import gradio as gr
import pandas as pd
from Prediction import *
import os
from datetime import datetime
import re
import json
persistent_path = "/data"
os.environ['HF_HOME'] = os.path.join(persistent_path, ".huggingface")
user_input_path = os.path.join(persistent_path, 'user.jsonl')
examples = []
if os.path.exists("assets/examples.txt"):
with open("assets/examples.txt", "r", encoding="utf8") as file:
for sentence in file:
sentence = sentence.strip()
examples.append(sentence)
else:
examples = [
"Games of the imagination teach us actions have consequences in a realm that can be reset.",
"But New Jersey farmers are retiring and all over the state, development continues to push out dwindling farmland.",
"He also is the Head Designer of The Design Trust so-to-speak, besides his regular job ..."
]
device = torch.device('cpu')
tokenizer = BertTokenizer.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = BertForSequenceClassification.from_pretrained("Oliver12315/Brand_Tone_of_Voice")
model = model.to(device)
def single_sentence(sentence):
predictions = predict_single(sentence, tokenizer, model, device)
return sorted(zip(LABEL_COLUMNS, predictions), key=lambda x:x[-1], reverse=True)
def csv_process(csv_file, attr="content"):
current_time = datetime.now()
formatted_time = current_time.strftime("%Y_%m_%d_%H_%M_%S")
data = pd.read_csv(csv_file.name)
data = data.reset_index()
os.makedirs('output', exist_ok=True)
outputs = []
predictions = predict_csv(data, attr, tokenizer, model, device)
output_path = f"output/prediction_Brand_Tone_of_Voice_{formatted_time}.csv"
predictions.to_csv(output_path)
outputs.append(output_path)
return outputs
def check_save(fname, lname, cnum, email, oname, position):
errors = []
valid_vars = {}
if not fname.strip() or not lname.strip():
errors.append("Name cannot be empty")
elif fname.isdigit() or lname.isdigit():
errors.append("Name cannot be purely numerical")
else:
valid_vars["fname"] = fname
valid_vars["lname"] = lname
valid_vars["cnum"] = ''
if cnum:
if not cnum.isdigit():
errors.append("The phone number must be a pure number")
else:
valid_vars["cnum"] = cnum
if not email.strip():
errors.append("Email cannot be empty")
elif not re.match(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$', email):
errors.append("Incorrect email format")
else:
valid_vars["email"] = email
if not oname.strip():
errors.append("Organization name cannot be empty")
elif oname.isdigit():
errors.append("Organization cannot be purely numerical")
else:
valid_vars["oname"] = oname
valid_vars["position"] = ''
if position:
if position.isdigit():
errors.append("Position in your company cannot be purely numerical")
else:
valid_vars["position"] = position
if errors:
return errors
current_time = datetime.now()
formatted_time = current_time.strftime("%Y_%m_%d_%H_%M_%S")
valid_vars['time'] = formatted_time
with open(user_input_path, 'a+', encoding="utf8") as file:
file.write(json.dumps(valid_vars)+"\n")
records = {}
with open(user_input_path, 'r', encoding="utf8") as file:
for line in file:
line = line.strip()
dct = json.loads(line)
records[dct['time']] = dct
return records
my_theme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty")
with gr.Blocks(theme=my_theme, title='Brand_Tone_of_Voice_demo') as demo:
gr.HTML(
"""
Place the title of the paper here
If you like our project, please give us a star ✨ on Github for the latest update.
""")
debug_tb = gr.Textbox(label="Persistent: ", type='text')
with gr.Column(visible=True) as regis:
gr.Markdown("# Welcome to BTV! Please fill out the form below to continue.\nI’m assuming that you mention somewhere that this project/research is conducted by the University of Manchester/AMBS. By ticking this box, I consent to be approached by the research team of the University of Manchester.")
with gr.Column(variant='panel'):
fname_tb = gr.Textbox(label="First Name: ", type='text')
lname_tb = gr.Textbox(label="Last Name: ", type='text')
email_tb = gr.Textbox(label="Email: ", type='email')
cnum_tb = gr.Textbox(label="Contact: (Optional)", type='text')
oname_tb = gr.Textbox(label="Organization name: ", type='text')
position_tb = gr.Textbox(label="Positions in your company: (Optional)", type='text')
error_box = gr.HTML(value="", visible=False)
submit_btn = gr.Button("Click here to start if you have fullfill all the item!")
with gr.Row(visible=False) as mainrow:
with gr.Tab("Single Sentence"):
with gr.Row():
tbox_input = gr.Textbox(label="Input",
info="Please input a sentence here:")
gr.Markdown("""
# Detailed information about our model:
...
""")
tab_output = gr.DataFrame(label='Predictions:',
headers=["Label", "Probability"],
datatype=["str", "number"],
interactive=False)
with gr.Row():
button_ss = gr.Button("Submit", variant="primary", visible=False)
button_ss.click(fn=single_sentence, inputs=[tbox_input], outputs=[tab_output])
gr.ClearButton([tbox_input, tab_output])
gr.Examples(
examples=examples,
inputs=tbox_input,
examples_per_page=len(examples)
)
with gr.Tab("Csv File"):
with gr.Row():
csv_input = gr.File(label="CSV File:",
file_types=['.csv'],
file_count="single"
)
csv_output = gr.File(label="Predictions:")
with gr.Row():
button_cf = gr.Button("Submit", variant="primary", visible=False)
button_cf.click(fn=csv_process, inputs=[csv_input], outputs=[csv_output])
gr.ClearButton([csv_input, csv_output])
gr.Markdown("## Examples \n The incoming CSV must include the ``content`` field, which represents the text that needs to be predicted!")
gr.DataFrame(label='Csv input format:',
value=[[i, examples[i]] for i in range(len(examples))],
headers=["index", "content"],
datatype=["number","str"],
interactive=False
)
with gr.Tab("Readme"):
gr.Markdown(
"""
# Paper Name
# Authors
+ First author
+ Corresponding author
# Detailed Information
...
"""
)
def submit(*user_input):
res = check_save(*user_input)
if isinstance(res, list):
return {
error_box: gr.HTML(
value=f"""
""",
visible=True)
}
else:
return {
mainrow: gr.Row(visible=True),
regis: gr.Row(visible=False),
error_box: gr.HTML(visible=False),
debug_tb: gr.Textbox(value=json.dumps(res), type='text', visible=True)
}
submit_btn.click(
submit,
[fname_tb, lname_tb, cnum_tb, email_tb, oname_tb, position_tb],
[mainrow, regis, error_box, debug_tb],
)
demo.launch()
