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import streamlit as st
import fitz # PyMuPDF
import openai
from fpdf import FPDF
import os
import tempfile
# Function to extract text from a 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's saved
# Open the saved PDF file
doc = fitz.open(temp_file.name)
text = ""
for page_num in range(len(doc)):
page = doc.load_page(page_num)
text += page.get_text()
# Delete the temporary file after reading (clean up)
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(('.', '!', '?')):
text += '.'
return text
# Function to summarize text using OpenAI GPT model
def summarize_text(api_key, text):
openai.api_key = api_key
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # Use "gpt-4" if you have access
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
)
summary = response.choices[0].message['content'].strip()
return ensure_full_stop(summary)
# Function to predict the main topic of the text
def predict_topic(api_key, text):
openai.api_key = api_key
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # Use "gpt-4" if you have access
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 topic
# Function to generate a PDF with summary and topic
def create_pdf(summary, topic, original_file_name):
base_name = os.path.splitext(original_file_name)[0] # Remove the .pdf extension
pdf_file_name = f"{base_name} summary.pdf" # Create the new filename
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
pdf.cell(200, 10, txt="Summary", ln=True, align='C')
pdf.multi_cell(0, 10, txt=summary)
pdf.cell(200, 10, txt="Predicted Main Topic", ln=True, align='C')
pdf.multi_cell(0, 10, txt=topic)
# Save the PDF to a file in memory
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 input
api_key = st.text_input("Enter your OpenAI API Key:", type="password")
# File upload
uploaded_file = st.file_uploader("Upload your research paper (PDF)", type=["pdf"])
if uploaded_file is not None:
# Extract text from the uploaded PDF
text = extract_text_from_pdf(uploaded_file)
if len(text) > 1000:
# Summarize the text
summary = summarize_text(api_key, text)
# Predict the main topic
topic = predict_topic(api_key, text)
# Display the results
st.subheader("Summary")
st.write(summary)
st.subheader("Predicted Main Topic")
st.write(topic)
# Button to download results as a PDF
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 for meaningful analysis.")
else:
st.info("Please upload a valid PDF file to proceed.")
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