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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):
# Define the CSS for the text container
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>
"""
# Create a placeholder for the text
placeholder = st.empty()
displayed_text = ""
# Iterate over each character and update the text incrementally
for char in text:
displayed_text += html.escape(char) # Escape HTML special characters
# Replace newlines with <br> tags to handle empty lines correctly
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)
# st.markdown(body=f"*Prediction*: :green-background[{res[0]['label']}]")
# st.markdown(f"*Score*: :green-background[{res[0]['score']}]")
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) |