Spaces:
Sleeping
Sleeping
| # import streamlit as st | |
| # from transformers import pipeline | |
| # # pipe=pipeline("sentiment-analysis") | |
| # # col1, col2 = st.columns(2) | |
| # # with col1: | |
| # # x=st.button("Sentiment Analysis") | |
| # # with col2: | |
| # # y=st.button("Text Summarization") | |
| # # if x: | |
| # # t=st.text_input("Enter the Text") | |
| # # st.write(pipe(t)) | |
| # # if y: | |
| # t1=st.text_input("Enter the Text for Summarization") | |
| # st.write(summarizer(t1)) | |
| #from transformers import AutoTokenizer, AutoModel | |
| # import streamlit as st | |
| #tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-insurance-v0.1") | |
| # #model = AutoModel.from_pretrained("llmware/industry-bert-insurance-v0.1") | |
| # # Use a pipeline as a high-level helper | |
| # from transformers import pipeline | |
| # #pipe = pipeline("feature-extraction") | |
| # t=st.text_input("Enter the Text") | |
| # pipe = pipeline("summarization") | |
| # st.write(pipe(t)) | |
| # import pandas as pd | |
| # import numpy as np | |
| # from ydata_synthetic.synthesizers.regular import RegularSynthesizer | |
| # from ydata_synthetic.synthesizers import ModelParameters, TrainParameters | |
| # import streamlit as st | |
| # from os import getcwd | |
| # text_file=st.file_uploader("Upload the Data File") | |
| # st.write("-------------------------") | |
| # if text_file is not None: | |
| # df=pd.read_csv(text_file) | |
| # dd_list=df.columns | |
| # cat_cols=st.multiselect("Select the Categorical Columns",dd_list) | |
| # num_cols=st.multiselect("Select the Numerical Columns",dd_list) | |
| # Output_file=st.text_input('Enter Output File Name') | |
| # s=st.number_input('Enter the Sample Size',1000) | |
| # OP=Output_file + '.csv' | |
| # sub=st.button('Submit') | |
| # if sub: | |
| # batch_size = 50 | |
| # epochs = 3 | |
| # learning_rate = 2e-4 | |
| # beta_1 = 0.5 | |
| # beta_2 = 0.9 | |
| # ctgan_args = ModelParameters(batch_size=batch_size, | |
| # lr=learning_rate, | |
| # betas=(beta_1, beta_2)) | |
| # train_args = TrainParameters(epochs=epochs) | |
| # synth = RegularSynthesizer(modelname='ctgan', model_parameters=ctgan_args) | |
| # synth.fit(data=df, train_arguments=train_args, num_cols=num_cols, cat_cols=cat_cols) | |
| # df_syn = synth.sample(s) | |
| # df_syn.to_csv(OP) | |
| # c=getcwd() | |
| # c=c + '/' + OP | |
| # with open(c,"rb") as file: | |
| # st.download_button(label=':blue[Download]',data=file,file_name=OP,mime="image/png") | |
| # st.success("Thanks for using the app !!!") | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| torch.set_default_device("cuda") | |
| model = AutoModelForCausalLM.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", torch_dtype="auto", trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", trust_remote_code=True) | |
| inputs = tokenizer('''### Instruction: What Does Basic Homeowners Insurance Cover?\n### Response: ''', return_tensors="pt", return_attention_mask=False) | |
| outputs = model.generate(**inputs, max_length=1024) | |
| text = tokenizer.batch_decode(outputs)[0] | |
| print(text) | |