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| import streamlit as st | |
| import torch | |
| import pandas as pd | |
| from io import StringIO | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| class preProcess: | |
| def __init__(self, filename, titlename): | |
| self.filename = filename | |
| self.title = titlename + '\n' | |
| def read_data(self): | |
| df = pd.read_csv(self.filename) | |
| return df | |
| def check_columns(self, df): | |
| if (len(df.columns) > 3): | |
| st.error('File has more than 3 coloumns.') | |
| return False | |
| if (len(df.columns) == 0): | |
| st.error('File has no column.') | |
| return False | |
| else: | |
| return True | |
| def format_data(self, df): | |
| headers = [[] for i in range(0, len(df.columns))] | |
| for i in range(len(df.columns)): | |
| headers[i] = list(df[df.columns[i]]) | |
| zipped = list(zip(*headers)) | |
| res = [' '.join(map(str,tups)) for tups in zipped] | |
| input_format = ' labels ' + ' - '.join(list(df.columns)) + ' values ' + ' , '.join(res) | |
| return input_format | |
| def combine_title_data(self,df): | |
| data = self.format_data(df) | |
| title_data = ' '.join([self.title,data]) | |
| return title_data | |
| class Model: | |
| def __init__(self,text,mode): | |
| self.padding = 'max_length' | |
| self.truncation = True | |
| self.prefix = 'C2T: ' | |
| self.device = device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| self.text = text | |
| if mode.lower() == 'simple': | |
| self.tokenizer = AutoTokenizer.from_pretrained('saadob12/t5_C2T_big') | |
| self.model = AutoModelForSeq2SeqLM.from_pretrained('saadob12/t5_C2T_big').to(self.device) | |
| elif mode.lower() == 'analytical': | |
| self.tokenizer = AutoTokenizer.from_pretrained('saadob12/t5_C2T_autochart') | |
| self.model = AutoModelForSeq2SeqLM.from_pretrained('saadob12/t5_C2T_autochart').to(self.device) | |
| def generate(self): | |
| tokens = self.tokenizer.encode(self.prefix + self.text, truncation=self.truncation, padding=self.padding, return_tensors='pt').to(self.device) | |
| generated = self.model.generate(tokens, num_beams=4, max_length=256) | |
| tgt_text = self.tokenizer.decode(generated[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
| summary = str(tgt_text).strip('[]""') | |
| return summary | |
| uploaded_file = st.file_uploader("Choose a file") | |
| if uploaded_file is not None: | |
| # To read file as bytes: | |
| bytes_data = uploaded_file.getvalue() | |
| st.write(bytes_data) | |
| # To convert to a string based IO: | |
| stringio = StringIO(uploaded_file.getvalue().decode("utf-8")) | |
| st.write(stringio) | |
| # To read file as string: | |
| string_data = stringio.read() | |
| st.write(string_data) | |
| # Can be used wherever a "file-like" object is accepted: | |
| dataframe = pd.read_csv(uploaded_file) | |
| st.write(dataframe) | |