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# import streamlit as st
# from transformers import pipeline
# # x = st.slider('Select a value')
# # st.write(x, 'squared is', x * x)
# classifier = pipeline("sentiment-analysis")
# sentiment = classifier("I've been waiting for HuggingFace course my whole life.")
# import streamlit as st
# from transformers import pipeline
# # Initialize the sentiment-analysis pipeline
# classifier = pipeline("sentiment-analysis")
# # Streamlit app layout
# st.title("Sentiment Analysis with Hugging Face")
# st.write("Enter a sentence to analyze its sentiment:")
# # Text input for the user
# user_input = st.text_input("Sentence", "")
# # Perform sentiment analysis when the user provides input
# if user_input:
# sentiment = classifier(user_input)
# label = sentiment[0]['label']
# score = sentiment[0]['score']
# # Display the result
# st.write(f"**Sentiment:** {label}")
# st.write(f"**Confidence Score:** {score:.4f}")
import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the tokenizer and model
# tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
# model = GPT2LMHeadModel.from_pretrained("gpt2-large")
tokenizer = AutoTokenizer.from_pretrained("gpt2-large")
model = AutoModelForCausalLM.from_pretrained("gpt2-large")
def generate_blog(title):
prompt = f"write a blog about {title}"
# Encode the input text
inputs = tokenizer.encode(prompt, return_tensors='pt')
# Generate the output
# outputs = model.generate(inputs, max_length=500, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
outputs = model.generate(inputs, max_length=500, num_return_sequences=1, do_sample=True, top_p=0.95, top_k=60)
# Decode the output text
blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)
return blog_post
# Streamlit app
st.title("Blog Post Generator")
title = st.text_input("Enter the blog title")
if st.button("Generate Blog"):
if title:
blog_post = generate_blog(title)
st.subheader("Generated Blog Post")
st.write(blog_post)
else:
st.warning("Please enter a blog title.")
# Optional: Add a slider example (unrelated to sentiment analysis)
# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x) |