dishathokal/MetaMagic

1import streamlit as st2import openai3import nltk4nltk.download('punkt')5 6# st.write("ok")7# Set up OpenAI API credentials8openai.api_key = "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"9 10# Define function to generate keywords11def generate_keywords(text):12    num_keywords = 613    cleaned_text = text.strip()14  15    response = openai.Completion.create(16        engine="text-davinci-002",17        prompt=f"What are {num_keywords} highly related keywords for the following text?\n{cleaned_text}\n\nKeywords:",18        max_tokens=50,19        n=1,20        stop=None,21        temperature=0.5,22        best_of=num_keywords,23    )24 25    generated_text = response.choices[0].text.strip()26 27    keywords = generated_text.split(',')28 29    st.write("Top Keywords:")30    for i, keyword in enumerate(keywords[:num_keywords]):31        st.write(f"{i+1}. {keyword.strip()}")32 33# Define function to generate summary34def generate_summary(text):35    summary_length = 236 37    cleaned_text = text.strip()38 39    response = openai.Completion.create(40        engine="text-davinci-002",41        prompt=f"Please summarize the following text in {summary_length} sentences:\n{cleaned_text}\n\nSummary:",42        max_tokens=100,43        n=1,44        stop=None,45        temperature=0.5,46    )47 48    generated_text = response.choices[0].text.strip()49    st.write("Description:")50    # st.write(generated_text)51    sentences = nltk.sent_tokenize(generated_text)52    for sentence in sentences:53      st.write(sentence)54 55# Set up Streamlit app56st.title("Text Summarization and Keyword Extraction")57 58text = st.text_area("Enter some text:")59 60if st.button("Generate Keywords"):61    generate_keywords(text)62 63if st.button("Generate Summary"):64    generate_summary(text)65