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f372a5b ab5a702 c69a0b4 f372a5b 1224539 ef3a9ce f372a5b 1224539 f372a5b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import streamlit as st
import pandas as pd
from huggify_data.scrape_modules import PDFQnAGenerator
import tempfile
def main():
st.set_page_config(layout="wide")
st.title("PDF Question-Answer Generator using Huggify-Data Package")
# Expander in the sidebar for instruction
with st.sidebar.expander("Instruction"):
st.write('''
🎉 Introducing Huggify-Data: Your Ultimate PDF Data Scraping and Uploading Tool! 🎉
👋 I'm thrilled to present the new user-friendly interface for my Python package, huggify-data. This powerful tool simplifies the process of scraping data from PDFs and generating question and answer pairs using OpenAI, making it perfect for building conversational chatbots. 🤖✨
🚀 Key Features:
1. Easy PDF Data Extraction: Quickly scrape text content from PDFs and convert it into a structured data frame.
2. Automated Question-Answer Pair Generation: Extract meaningful question-answer pairs from your PDF content, ideal for training chatbots.
3. User-Friendly Interface: Interact with the package without any programming experience, making information accessibility easier and more efficient.
🔧 How It Works:
- API Key: Add your OpenAI API Key.
- Load Your PDF: Easily load any PDF file into the library.
- Just wait: Wait and download the `.csv` from the app.
📈 Why Huggify-Data?
Whether you're a data scientist, developer, or AI enthusiast, Huggify-Data streamlines the process of preparing your PDF data for AI applications. It's never been easier to transform your PDFs into valuable datasets for building conversational AI models.
🔗 Links:
- **GitHub Repository**: [https://lnkd.in/eJEJebcw](https://lnkd.in/eJEJebcw)
- **Documentation**: [https://lnkd.in/eF9JFXAP](https://lnkd.in/eF9JFXAP)
- **Notebook**: [https://lnkd.in/eaA2qaPt](https://lnkd.in/eaA2qaPt)
- **App**: [https://huggingface.co/spaces/eagle0504/huggify-data](https://huggingface.co/spaces/eagle0504/huggify-data)
Don't forget to like, comment, and subscribe for more updates and tutorials on AI and data science! 👍🔔
#HuggifyData #PythonLibrary #AI #DataScience #HuggingFace #PDFScraping #Chatbot #OpenSource #Yiqiao
''')
# Sidebar for uploading the PDF file
st.sidebar.title("Upload PDF")
uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type="pdf")
# Text input for OpenAI API key
openai_api_key = st.sidebar.text_input("Enter your OpenAI API key", type="password")
# Embed YouTube video in the sidebar
st.sidebar.video("https://youtu.be/CfMcw4OTLCQ")
if uploaded_file is not None and openai_api_key:
# Save the uploaded PDF to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
temp_pdf.write(uploaded_file.read())
temp_pdf_path = temp_pdf.name
# Show a spinner while processing the PDF
with st.spinner('Processing the PDF and generating questions and answers...'):
# Process the PDF and generate the questions and answers
generator = PDFQnAGenerator(temp_pdf_path, openai_api_key)
generator.process_scraped_content()
generator.generate_questions_answers()
df = generator.convert_to_dataframe()
# Display the resulting DataFrame
st.subheader("Generated Question-Answer Pairs")
st.write(df)
# Option to download the DataFrame as a CSV
csv = df.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download as CSV",
data=csv,
file_name='questions_answers.csv',
mime='text/csv',
)
if __name__ == "__main__":
main()
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