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| import streamlit as st | |
| import fitz # PyMuPDF = reads the content of the pdf file uploaded | |
| import openai | |
| from fpdf import FPDF | |
| import os | |
| import tempfile | |
| # function to extract text from pdf file | |
| def extract_text_from_pdf(pdf_file): | |
| # Save the uploaded file to a temporary location | |
| temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") | |
| temp_file.write(pdf_file.read()) | |
| temp_file.close() # Close the file to ensure it is saved | |
| # Open the saved pdf file | |
| doc = fitz.open(temp_file.name) | |
| text = "" # Extracted info saved here | |
| for page_num in range(len(doc)): | |
| page = doc.load_page(page_num) | |
| text += page.get_text() # Saving in text | |
| # Delete the temp file after reading | |
| os.remove(temp_file.name) | |
| return text | |
| # function to ensure the summary ends with a full stop | |
| def ensure_full_stop(text): | |
| text = text.strip() | |
| if not text.endswith(('.', '!', '?')): # Corrected tuple syntax | |
| text += '.' | |
| return text | |
| # function to summarize | |
| def summarize_text(api_key, text): | |
| openai.api_key = api_key | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"Summarize the following text:\n\n{text}"} | |
| ], | |
| max_tokens=500, | |
| temperature=0.5 # Fixed incorrect `temp` key | |
| ) | |
| summary = response.choices[0].message['content'].strip() | |
| return ensure_full_stop(summary) | |
| # function to understand the main gist of the pdf | |
| def predict_topic(api_key, text): | |
| openai.api_key = api_key | |
| response = openai.ChatCompletion.create( | |
| model="gpt-4o-mini", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"What is the main topic of the following text:\n\n{text}"} | |
| ], | |
| max_tokens=500, | |
| temperature=0.5 | |
| ) | |
| topic = response.choices[0].message['content'].strip() | |
| return ensure_full_stop(topic) | |
| def create_pdf(summary, topic, original_file_name): | |
| base_name = os.path.splitext(original_file_name)[0] | |
| pdf_file_name = f"{base_name} summary.pdf" | |
| pdf = FPDF() | |
| pdf.add_page() | |
| pdf.set_font("Arial", size=12) | |
| pdf.cell(200, 10, txt="Predicted Main Topic", ln=True, align='C') # Fixed `In=True` | |
| pdf.multi_cell(0, 10, txt=topic) | |
| pdf_file_path = f"/tmp/{pdf_file_name}" | |
| pdf.output(pdf_file_path) | |
| return pdf_file_path | |
| # Streamlit UI | |
| st.title("Research Paper Summarizer") | |
| api_key = st.text_input("Enter your API key:", type="password") | |
| # File upload | |
| uploaded_file = st.file_uploader("Upload your paper (PDF)", type=["pdf"]) | |
| if uploaded_file is not None: | |
| text = extract_text_from_pdf(uploaded_file) | |
| if len(text) > 1000: # Fixed missing colon | |
| summary = summarize_text(api_key, text) | |
| topic = predict_topic(api_key, text) | |
| st.subheader("Summary") | |
| st.write(summary) | |
| st.subheader("Predicted Topic") | |
| st.write(topic) | |
| if st.button("Get the Summary PDF"): | |
| pdf_path = create_pdf(summary, topic, uploaded_file.name) | |
| st.download_button( | |
| label="Download Summary PDF", | |
| data=open(pdf_path, "rb").read(), | |
| file_name=os.path.basename(pdf_path), | |
| mime="application/pdf" | |
| ) | |
| else: | |
| st.warning("The document is too short.") | |
| else: | |
| st.info("Please upload a PDF after entering your OpenAI API key.") | |