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| import streamlit as st | |
| from multiprocessing import Process | |
| import json | |
| import requests | |
| import time | |
| import os | |
| def start_server(): | |
| '''Helper to start to service through Unicorn ''' | |
| os.system("uvicorn InferenceServer:app --port 8080 --host 0.0.0.0 --workers 2") | |
| def load_models(): | |
| '''One time loading/ Init of models and starting server as a seperate process''' | |
| if not is_port_in_use(8080): | |
| with st.spinner(text="Loading model, please wait..."): | |
| proc = Process(target=start_server, args=(), daemon=True) | |
| proc.start() | |
| while not is_port_in_use(8080): | |
| time.sleep(1) | |
| st.success("Model server started.") | |
| else: | |
| st.success("Model server already running...") | |
| st.session_state['models_loaded'] = True | |
| def is_port_in_use(port): | |
| '''Helper to check if service already running''' | |
| import socket | |
| with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: | |
| return s.connect_ex(('0.0.0.0', port)) == 0 | |
| if 'models_loaded' not in st.session_state: | |
| st.session_state['models_loaded'] = False | |
| def get_correction(input_text): | |
| '''Invokes the inference service''' | |
| st.markdown(f'##### Corrected text:') | |
| st.write('') | |
| correct_request = "http://0.0.0.0:8080/restore?input_sentence="+input_text | |
| with st.spinner('Wait for it...'): | |
| correct_response = requests.get(correct_request) | |
| correct_json = json.loads(correct_response.text) | |
| corrected_sentence = correct_json["corrected_sentence"] | |
| result = diff_strings(corrected_sentence,input_text) | |
| st.markdown(result, unsafe_allow_html=True) | |
| def diff_strings(output_text, input_text): | |
| '''Highlights corrections''' | |
| c_text = "" | |
| for x in output_text.split(" "): | |
| if x in input_text.split(" "): | |
| c_text = c_text + x + " " | |
| else: | |
| c_text = c_text + '<span style="font-weight:bold; color:rgb(142, 208, 129);">' + x + '</span>' + " " | |
| return c_text | |
| if __name__ == "__main__": | |
| st.title('Rpunct') | |
| st.subheader('For Punctuation and Upper Case restoration') | |
| st.markdown("Spaces for [felflare/bert-restore-punctuation](https://huggingface.co/felflare/bert-restore-punctuation) using [Fork with CPU support](https://github.com/anuragshas/rpunct) | [Original repo](https://github.com/Felflare/rpunct)", unsafe_allow_html=True) | |
| st.markdown("Model restores the following punctuations -- [! ? . , - : ; ' ] and also the upper-casing of words.") | |
| st.markdown("Integrate with just few lines of code", unsafe_allow_html=True) | |
| st.markdown(""" | |
| ```python | |
| from rpunct import RestorePuncts | |
| rpunct = RestorePuncts() | |
| rpunct.punctuate('''my name is clara and i live in berkeley california''') | |
| ``` | |
| """) | |
| examples = [ | |
| "my name is clara and i live in berkeley california", | |
| "in 2018 cornell researchers built a high-power detector", | |
| "lorem ipsum has been the industrys standard dummy text ever since the 1500s when an unknown printer took a galley of type and scrambled it to make a type specimen book" | |
| ] | |
| if not st.session_state['models_loaded']: | |
| load_models() | |
| input_text = st.selectbox( | |
| label="Choose an example", | |
| options=examples | |
| ) | |
| st.write("(or)") | |
| input_text = st.text_input( | |
| label="Input sentence", | |
| value=input_text | |
| ) | |
| if input_text.strip(): | |
| get_correction(input_text) |