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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig |
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import gradio as gr |
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from torch.nn import functional as F |
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import seaborn |
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import matplotlib |
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import platform |
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from transformers.file_utils import ModelOutput |
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if platform.system() == "Darwin": |
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print("MacOS") |
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matplotlib.use('Agg') |
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import matplotlib.pyplot as plt |
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import io |
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from PIL import Image |
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import matplotlib.font_manager as fm |
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MODEL_NAME = 'yseop/FNP_T5_D2T_complete' |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) |
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config = AutoConfig.from_pretrained(MODEL_NAME) |
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MODEL_BUF = { |
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"name": MODEL_NAME, |
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"tokenizer": tokenizer, |
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"model": model, |
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"config": config |
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} |
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font_dir = ['./'] |
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for font in fm.findSystemFonts(font_dir): |
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print(font) |
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fm.fontManager.addfont(font) |
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plt.rcParams["font.family"] = 'NanumGothicCoding' |
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def change_model_name(name): |
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MODEL_BUF["name"] = name |
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MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name) |
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MODEL_BUF["model"] = AutoModelForSeq2SeqLM.from_pretrained(name) |
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MODEL_BUF["config"] = AutoConfig.from_pretrained(name) |
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def generate(model_name, text): |
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if model_name != MODEL_NAME: |
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change_model_name(model_name) |
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tokenizer = MODEL_BUF["tokenizer"] |
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model = MODEL_BUF["model"] |
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config = MODEL_BUF["config"] |
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model.eval() |
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input_ids = tokenizer.encode("AFA:{}".format(text), return_tensors="pt") |
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outputs = model.generate(input_ids, max_length=200, num_beams=2, repetition_penalty=2.5, top_k=50, top_p=0.98, length_penalty=1.0, early_stopping=True) |
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output = tokenizer.decode(outputs[0]) |
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return ".".join(output.split(".")[:-1]) + "." |
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output_text = gr.outputs.Textbox() |
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if __name__ == '__main__': |
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text = ['Group profit | valIs | € 115.7 million && € 115.7 million | dTime | in 2019'] |
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model_name_list = [ |
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'yseop/FNP_T5_D2T_complete', |
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'yseop/FNP_T5_D2T_simple' |
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] |
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app = gr.Interface( |
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fn=generate, |
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inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=output_text, |
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examples = [[MODEL_BUF["name"], text]], |
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title="FTG", |
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description="Financial Text Generation" |
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) |
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app.launch(inline=False) |
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