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# coding: utf-8
import gradio as gr
from theme import CustomTheme
import pandas as pd
import time
import openai
import os
custom_theme = CustomTheme()
openai.api_key = os.environ.get("OPENAI_API_KEY")
ft_model_ada = 'ada:ft-personal-2023-05-17-17-03-33'
# ft_model_ada = 'ada:ft-personal-2023-06-29-17-16-28'
ft_model_davinci = 'ada:ft-personal-2023-05-17-17-03-33'
def upload_file(file, model_name):
with open(file.name, "r", encoding='unicode_escape') as f:
content = f.read().replace('\n', '')
sentences = list(map(str.strip, content.split(".")))
df = pd.DataFrame({"claim": sentences}, index=range(len(sentences)))
# df = pd.DataFrame({"claim": sentences[:10]}, index=range(len(sentences[:10])))
col = f"openai_pred_{model_name}"
model = ft_model_ada if model_name == "ada" else ft_model_davinci
results = []
start_time = time.time()
for i in range(len(sentences)):
res = openai.Completion.create(model=model, prompt= sentences[i] + ' ->', max_tokens=1, temperature=0)
results.append(res['choices'][0]['text'])
end_time = time.time()
# results = sentences[:10]
df[col] = results
df[col] = df[col].map({" True": 1, " False": 0})
time_taken = round(end_time - start_time, 2)
display_df_openai_ada = df.loc[df[col] == 1]
return display_df_openai_ada, time_taken
def export_csv(d, file):
filename = file.name.split("/")[-1].split(".")[0]
d.to_csv(f"{filename}_output.csv")
# filename = file.name.split(".")[0]
return gr.File.update(value = f"{filename}_output.csv", visible=True)
with gr.Blocks(theme=custom_theme) as demo:
gr.Markdown(value="# Upload transcription txt file")
with gr.Row():
input_document_pdf = gr.File(label="Uplaod transcription file")
with gr.Row():
with gr.Column():
model_name_ada = gr.Text(label="model_name", value="ada", visible=False)
output_df = gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "dynamic"), headers=["claim", "prediction"],
label="Predictions", wrap= True)
time_taken = gr.Text(label="Time Taken", lines=1)
download = gr.Button("Download output", variant="primary")
csv = gr.File(interactive=False, visible=False)
input_document_pdf.upload(
upload_file,
[input_document_pdf, model_name_ada],
[output_df, time_taken]
)
download.click(export_csv, [output_df, input_document_pdf], [csv])
if __name__ == "__main__":
demo.launch(debug=True)
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