Spaces:
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e276af2
1
Parent(s):
506c323
init
Browse files- .gitignore +2 -0
- app.py +51 -0
- requirements.txt +2 -0
- src/model.py +28 -0
.gitignore
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*test*
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__pycache__
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app.py
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import streamlit as st
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mapper = {
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"wikilingua": "64b53c9e04b2bfaeb2f0b38cb7712bcbd4755c3f",
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"vietnews": "d8d516ad2c112b429155c3e6077182ae5fe5b33d"
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}
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if "model" not in st.session_state:
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from src.model import Model
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st.session_state.task="wikilingua"
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model = Model(revision=mapper["wikilingua"])
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st.session_state.model = model
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st.set_page_config(page_title="ViT5 Reproduce", layout="wide")
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hide_menu_style = """
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<style>
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footer {visibility: hidden;}
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</style>
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"""
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st.markdown(hide_menu_style, unsafe_allow_html= True)
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with st.sidebar:
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task = st.selectbox(label="Task", options=["wikilingua", "vietnews"])
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if task != st.session_state.task:
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from src.model import Model
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st.session_state.task=task
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model = Model(revision=mapper[task])
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left, middle, right = st.columns([4,1,4])
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left_container = left.container(border=True)
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left_container.write(f"**Input**")
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left_container.divider()
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text = left_container.text_area(label="", height=512, label_visibility="hidden", max_chars=4096*5)
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summary_button = middle.button("Summary ➩", type="primary", use_container_width=True)
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right_container = right.container(border=True)
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right_container.markdown(f"**Output**")
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right_container.divider()
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if summary_button:
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output = st.session_state.model.inference(text=text)
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st.session_state["output"] = output
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if "output" in st.session_state:
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right_container.text_area(label="", value=st.session_state["output"], height=512, label_visibility="hidden")
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requirements.txt
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streamlit==1.35.0
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transformers==4.41.0
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src/model.py
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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class Model:
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def __init__(self, revision) -> None:
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self.tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")
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self.model = AutoModelForSeq2SeqLM.from_pretrained("truong-xuan-linh/vit5-reproduce", revision=revision)
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def preprocess_function(self, text):
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inputs = self.tokenizer(
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text, max_length=1024, truncation=True, padding=True, return_tensors="pt"
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)
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return inputs
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def inference(self, text):
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max_target_length = 256
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inputs = self.preprocess_function(text)
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outputs = self.model.generate(
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input_ids=inputs['input_ids'],
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max_length=max_target_length,
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attention_mask=inputs['attention_mask'],
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)
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with self.tokenizer.as_target_tokenizer():
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outputs = [self.tokenizer.decode(out, clean_up_tokenization_spaces=False, skip_special_tokens=True) for out in outputs]
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return outputs[0]
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