init

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
+import gradio as gr
+from torch.nn import functional as F
+import seaborn
+import matplotlib
+import platform
+from transformers.file_utils import ModelOutput
+
+if platform.system() == "Darwin":
+
+    print("MacOS")
+    matplotlib.use('Agg')
+
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+import matplotlib.font_manager as fm
+
+# global var
+
+MODEL_NAME = 'https://huggingface.co/yseop/FNP_T5_D2T_complete'
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+config = AutoConfig.from_pretrained(MODEL_NAME)
+
+MODEL_BUF = {
+    "name": MODEL_NAME,
+    "tokenizer": tokenizer,
+    "model": model,
+    "config": config
+}
+
+font_dir = ['./']
+
+for font in fm.findSystemFonts(font_dir):
+    print(font)
+    fm.fontManager.addfont(font)
+
+plt.rcParams["font.family"] = 'NanumGothicCoding'
+
+def change_model_name(name):
+
+    MODEL_BUF["name"] = name
+    MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name)
+    MODEL_BUF["model"] = AutoModelForSequenceClassification.from_pretrained(name)
+    MODEL_BUF["config"] = AutoConfig.from_pretrained(name)
+
+
+def generate(text, model, tokenizer):
+   model.eval()
+   input_ids = tokenizer.encode("webNLG:{}".format(text), return_tensors="pt")
+   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)
+   return tokenizer.decode(outputs[0])
+
+
+
+
+if __name__ == '__main__':
+
+    text = 'Group profit | valIs | € 115.7 million && € 115.7 million | dTime | in 2019'
+
+    model_name_list = [
+
+        'yseop/distilbert-base-financial-relation-extraction'
+
+    ]
+
+inputs = [gradio.inputs.Textbox(label="Input")]
+
+outputs = gradio.outputs.Textbox(label='Output')
+
+iface = gradio.Interface(fn=generate, inputs=inputs, outputs=outputs, capture_session=True, examples=examples,
+                         title=title, description=description, allow_flagging=False)