| import streamlit as st
|
| import pandas as pd
|
| import numpy as np
|
| from transformers import pipeline
|
| import time
|
| import html
|
|
|
| st.title('3 - *HuggingFace* :blue[Tutorial]')
|
|
|
| def slowly_display_text(text, delay=0.05):
|
|
|
| css = """
|
| <style>
|
| .text-container {
|
| width: 80%;
|
| max-width: 600px;
|
| white-space: pre-wrap; /* Ensure text wraps */
|
| word-wrap: break-word; /* Ensure long words wrap */
|
| font-family: 'Courier New', Courier, monospace;
|
| font-size: 1.1em;
|
| line-height: 1.5;
|
| }
|
| </style>
|
| """
|
|
|
|
|
| placeholder = st.empty()
|
| displayed_text = ""
|
|
|
|
|
| for char in text:
|
| displayed_text += html.escape(char)
|
|
|
| formatted_text = displayed_text.replace("\n", "<br>")
|
| placeholder.markdown(css + f'<div class="text-container">{formatted_text}</div>', unsafe_allow_html=True)
|
| time.sleep(delay)
|
|
|
|
|
| st.subheader('Pipe1 :- Sentiment Analysis',divider='orange')
|
|
|
| if st.checkbox(label='Show Pipe1'):
|
| classifier = pipeline('sentiment-analysis')
|
|
|
| x = st.text_input(label='Enter text', value="I've been waiting for a huggingface course my whoole life.")
|
| res = classifier(x)
|
|
|
|
|
| col1, col2 = st.columns(2)
|
| col1.metric(label='Prediction', value=res[0]['label'])
|
| col2.metric(label='Score', value=res[0]['score'])
|
| st.write(res)
|
|
|
|
|
| st.subheader('Pipe2 :- Text Generation',divider='orange')
|
|
|
| if st.checkbox(label='Show Pipe2'):
|
| generator = pipeline('text-generation', model='distilgpt2')
|
| sentence = "In this course we'll teach you how to"
|
| res2 = generator(
|
| sentence,
|
| max_length = 30,
|
| )
|
| x = st.text_input(label='Enter text', value="In this course we'll teach you how to")
|
| res2 = generator(x,max_length=70)
|
| st.write("Generated text is:")
|
| slowly_display_text(res2[0]['generated_text'])
|
| st.write(res2)
|
|
|
|
|
|
|
| st.subheader('Pipe3 :- Zero-shot classification', divider='orange')
|
|
|
| if st.checkbox(label='Show Pipe3'):
|
| clf2 = pipeline(
|
| task='zero-shot-classification',
|
| model = 'distilbert/distilbert-base-uncased-finetuned-sst-2-english',
|
| framework='pt'
|
| )
|
| x = st.text_input(label='Enter text', value="This is a course about python list comprehension")
|
| res3 = clf2(
|
| x,
|
| candidate_labels = ['education', 'politics', 'business']
|
| )
|
| st.write(res3) |
