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import streamlit as st
from transformers import pipeline
# from PIL import Image
# import requests
# import torch
# from diffusers import DiffusionPipeline
from SeoKeywordResearch import SeoKeywordResearch
def main():
#Load models
text_model_name = "EleutherAI/gpt-neo-1.3B"
text_generator = pipeline("text-generation", model=text_model_name, tokenizer=text_model_name)
st.title("AI Blog Post Generator")
kws = st.checkbox('generate keywords')
if kws:
keyword_research = SeoKeywordResearch(
query='artificial intelligence',
api_key='1d86ba79731e5b3c038fb9f75715883cab027b2f7b41b61ba76d59ec3b9e252d',
lang='en',
country='us',
domain='google.com')
data = {
'auto_complete': keyword_research.get_auto_complete(),
'related_searches': keyword_research.get_related_searches(),
'related_questions': keyword_research.get_related_questions(depth_limit=1)
}
# keyword_research.save_to_txt(data)
keywords = keyword_research.print_data(data)
all_keywords = keywords["auto_complete"] + keywords["related_searches"] + keywords["related_questions"]
keywords = [kw for kw in all_keywords]
st.text_input("Enter the title :")
else :
keywords = st.text_input("Enter relevant keywords (comma-separated):")
keywords = [kw.strip() for kw in keywords.split(",")]
# Button to generate blog post
if st.button("Generate Blog"):
if keywords:
# Generate content based on keywords
for keyword in keywords:
generated_text = text_generator(keyword, max_length=150, num_return_sequences=1, temperature=0.7)[0]['generated_text']
st.subheader(keyword)
st.write(generated_text)
st.header('Conclusion')
generated_text = text_generator(keywords[0], max_length=150, num_return_sequences=1, temperature=0.7)[0]['generated_text']
st.subheader(keyword)
st.write(generated_text)
if __name__ == "__main__":
main()
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