Spaces:
Sleeping
Sleeping
Florin Bobiș
commited on
Commit
•
9f814ca
1
Parent(s):
9b7056a
done
Browse files- .gitignore +2 -0
- app.py +51 -0
- index.py +0 -17
- package.json +0 -5
- public/favicon.ico +0 -0
- requirements.txt +3 -3
- test.http +0 -5
.gitignore
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.vercel
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.vercel
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cache/
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app.py
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import streamlit as st
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from transformers import MT5ForConditionalGeneration, T5Tokenizer
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import time
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@st.cache_resource
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def load_model():
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model = MT5ForConditionalGeneration.from_pretrained('iliemihai/mt5-base-romanian-diacritics', cache_dir='cache/')
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return model
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@st.cache_resource
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def load_tokenizer():
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tokenizer = T5Tokenizer.from_pretrained('iliemihai/mt5-base-romanian-diacritics', legacy=False, cache_dir='cache/')
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return tokenizer
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def initialize_app():
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st.set_page_config(
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page_title="Dia-critic",
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page_icon="public/favicon.ico",
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menu_items={
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"About": "### Contact\n ✉️florinbobis@gmail.com",
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},
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)
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st.title("🖋️Dia-critic")
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st.caption("Made with :heart: by NEBO Technologies")
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def generate_text(text):
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model = load_model()
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tokenizer = load_tokenizer()
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inputs = tokenizer(text, max_length=256, truncation=True, return_tensors="pt")
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outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
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output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return output
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def main():
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initialize_app()
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input_text = st.text_area("Introduceți textul mai jos")
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st.write(f'{len(input_text)} caractere.')
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if st.button("Corectează"):
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if input_text != "":
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res = ''
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with st.spinner('Sarcină în desfășurare...'):
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# start task
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res = generate_text(input_text)
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with st.container(border=True):
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st.markdown(res)
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else:
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st.warning("Câmpul este gol!")
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if __name__ == "__main__":
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main()
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index.py
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from flask import Flask, request, jsonify
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from transformers import MT5ForConditionalGeneration, T5Tokenizer
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app = Flask(__name__)
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model = MT5ForConditionalGeneration.from_pretrained('iliemihai/mt5-base-romanian-diacritics', cache_dir='cache/')
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tokenizer = T5Tokenizer.from_pretrained('iliemihai/mt5-base-romanian-diacritics', legacy=False, cache_dir='cache/')
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@app.route('/generate', methods=['POST'])
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def generate_text():
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input_text = request.get_json()['input_text']
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if input_text is None:
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return jsonify({'error': 'No input text provided'})
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inputs = tokenizer(input_text, max_length=256, truncation=True, return_tensors="pt")
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outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
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output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return jsonify({'output': output})
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package.json
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{
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"engines": {
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"node": "18.x"
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}
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}
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public/favicon.ico
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requirements.txt
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torchvision
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streamlit
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transformers==4.27.4
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torchvision
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sentencepiece
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test.http
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POST http://94.101.98.71:5000/generate
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{
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"input_text": "cat de multe as vrea sa-ti spun tie"
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}
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