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| import os | |
| import openai | |
| import streamlit as st | |
| import PyPDF2 | |
| # Set up OpenAI API key | |
| openai.api_key = os.getenv("OPENAI_KEY") | |
| # Streamlit app | |
| def main(): | |
| st.title("EB2-NIW Petition Draft Generator") | |
| st.write("Upload a research paper PDF and get a drafted petition addressing the three prongs for EB2-NIW.") | |
| # File upload | |
| uploaded_file = st.file_uploader("Upload PDF", type="pdf") | |
| if uploaded_file is not None: | |
| # Extract text from PDF | |
| pdf_text = extract_text_from_pdf(uploaded_file) | |
| # Button to extract and show text from PDF | |
| if st.button("Extract Text"): | |
| if pdf_text: | |
| st.subheader("Extracted Text:") | |
| st.write(pdf_text) | |
| else: | |
| st.error("Failed to extract text from the PDF.") | |
| # Button to generate petition draft | |
| if st.button("Generate Petition Draft"): | |
| if pdf_text.strip() == "": | |
| st.error("No text extracted from the PDF. Please check the file and try again.") | |
| else: | |
| # Generate the petition draft based on the extracted text | |
| petition_draft = generate_petition_draft(pdf_text) | |
| st.success("Petition Draft:") | |
| st.write(petition_draft) | |
| # Function to extract text from a PDF | |
| def extract_text_from_pdf(pdf_file): | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| # Function to generate EB2-NIW petition draft using GPT-3.5 | |
| def generate_petition_draft(research_text): | |
| # Prompt for GPT-3.5 | |
| prompt = ( | |
| "A researcher has asked you to draft an EB2-NIW petition. " | |
| "Based on the following research paper text, draft a 2-page petition that proves the following three prongs:\n\n" | |
| "Prong 1: The applicant's proposed endeavor has substantial merit and national importance.\n" | |
| "Prong 2: The applicant is well-positioned to advance the proposed endeavor.\n" | |
| "Prong 3: On balance, it would be beneficial to the United States to waive the requirements of the PERM labor certification.\n\n" | |
| "Research paper text:\n\n" + research_text | |
| ) | |
| # Generate response from GPT-3.5 | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| {"role": "system", "content": "You are an experienced immigration lawyer."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| max_tokens=10000, | |
| temperature=0 | |
| ) | |
| petition_draft = response.choices[0].message['content'].strip() | |
| return petition_draft | |
| if __name__ == "__main__": | |
| main() | |