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| import streamlit as st | |
| import time | |
| from datetime import datetime | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import numpy as np | |
| import torch | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| html_temp= """ | |
| <div style="background-color:tomato;padding:10px"> | |
| <h2 style="color:white;text-align:centre;"> Shakespeare-AI.</h2> | |
| </div> | |
| """ | |
| st.markdown(html_temp,unsafe_allow_html=True) | |
| st.markdown( | |
| """ | |
| This is an AI tool. This AI chatbot is able to talk to you like Shakespeare. | |
| """ | |
| ) | |
| output_path = "SurendraKumarDhaka/output" | |
| # Load the trained model and tokenizer | |
| tokenizer = GPT2Tokenizer.from_pretrained(output_path) | |
| model = GPT2LMHeadModel.from_pretrained(output_path) | |
| input_text = st.text_area("Type your text..") | |
| st.button("Submit") | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| # Generate text using the model | |
| output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2) | |
| # Decode the generated text | |
| generated_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| st.write(generated_text) | |
| st.text("Thanks for using") | |
| if st.button("About"): | |
| st.text("Created by Surendra Kumar") | |
| ## footer | |
| from htbuilder import HtmlElement, div, ul, li, br, hr, a, p, img, styles, classes, fonts | |
| from htbuilder.units import percent, px | |
| from htbuilder.funcs import rgba, rgb | |
| def image(src_as_string, **style): | |
| return img(src=src_as_string, style=styles(**style)) | |
| def link(link, text, **style): | |
| return a(_href=link, _target="_blank", style=styles(**style))(text) | |
| def layout(*args): | |
| style = """ | |
| <style> | |
| # MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| .stApp { bottom: 105px; } | |
| </style> | |
| """ | |
| style_div = styles( | |
| position="fixed", | |
| left=0, | |
| bottom=0, | |
| margin=px(0, 0, 0, 0), | |
| width=percent(100), | |
| color="black", | |
| text_align="center", | |
| height="auto", | |
| opacity=1 | |
| ) | |
| style_hr = styles( | |
| display="block", | |
| margin=px(8, 8, "auto", "auto"), | |
| border_style="solid", | |
| border_width=px(0.5) | |
| ) | |
| body = p() | |
| foot = div( | |
| style=style_div | |
| )( | |
| hr( | |
| style=style_hr | |
| ), | |
| body | |
| ) | |
| st.markdown(style,unsafe_allow_html=True) | |
| for arg in args: | |
| if isinstance(arg, str): | |
| body(arg) | |
| elif isinstance(arg, HtmlElement): | |
| body(arg) | |
| st.markdown(str(foot), unsafe_allow_html=True) | |
| def footer(): | |
| myargs = [ | |
| "©️ surendraKumar", | |
| br(), | |
| link("https://www.linkedin.com/in/surendra-kumar-51802022b", image('https://icons.getbootstrap.com/assets/icons/linkedin.svg') ), | |
| br(), | |
| link("https://www.instagram.com/im_surendra_dhaka/",image('https://icons.getbootstrap.com/assets/icons/instagram.svg')), | |
| ] | |
| layout(*myargs) | |
| if __name__ == "__main__": | |
| footer() |