app.py

import streamlit as st
import openai
import nltk
nltk.download('punkt')

# st.write("ok")
# Set up OpenAI API credentials
openai.api_key = "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"

# Define function to generate keywords
def generate_keywords(text):
    num_keywords = 6
    cleaned_text = text.strip()
  
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=f"What are {num_keywords} highly related keywords for the following text?\n{cleaned_text}\n\nKeywords:",
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
        best_of=num_keywords,
    )

    generated_text = response.choices[0].text.strip()

    keywords = generated_text.split(',')

    st.write("Top Keywords:")
    for i, keyword in enumerate(keywords[:num_keywords]):
        st.write(f"{i+1}. {keyword.strip()}")

# Define function to generate summary
def generate_summary(text):
    summary_length = 2

    cleaned_text = text.strip()

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=f"Please summarize the following text in {summary_length} sentences:\n{cleaned_text}\n\nSummary:",
        max_tokens=100,
        n=1,
        stop=None,
        temperature=0.5,
    )

    generated_text = response.choices[0].text.strip()
    st.write("Description:")
    # st.write(generated_text)
    sentences = nltk.sent_tokenize(generated_text)
    for sentence in sentences:
      st.write(sentence)

# Set up Streamlit app
st.title("Text Summarization and Keyword Extraction")

text = st.text_area("Enter some text:")

if st.button("Generate Keywords"):
    generate_keywords(text)

if st.button("Generate Summary"):
    generate_summary(text)