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import streamlit as st
import pdfplumber
import openai
from dotenv import load_dotenv
import os
load_dotenv() # Load environment variables from .env file
# Safe retrieval of API key from environment variables
openai.api_key = os.getenv("OPENAI_API_KEY")
# Streamlit UI setup for the application
st.title("Advanced PDF-Based Application")
st.markdown("Select the functionality you want to use from the sidebar.")
# Sidebar for mode selection and file uploading
with st.sidebar:
mode = st.radio("Choose a mode:", ["PDF Summarizer", "Question Answering"])
uploaded_files = st.file_uploader("Upload PDF files", accept_multiple_files=True, type=['pdf'], on_change=lambda: st.experimental_rerun())
# Initializing documents list
documents = []
# Progress bar for file processing
if uploaded_files:
with st.spinner('Processing PDF files...'):
progress_bar = st.progress(0)
total_files = len(uploaded_files)
for i, uploaded_file in enumerate(uploaded_files):
with pdfplumber.open(uploaded_file) as pdf:
full_text = ""
for page in pdf.pages[:50]: # Process each page up to a limit of 50 pages
full_text += page.extract_text() or ""
documents.append(full_text)
progress_bar.progress((i + 1) / total_files)
st.success("PDFs processed successfully. Proceed based on the selected mode.")
progress_bar.empty()
# Using tabs to separate features
tab1, tab2 = st.tabs(["Question Answering", "PDF Summarizer"])
with tab1:
if mode == "Question Answering":
question = st.text_input("Enter your question here:")
if question and documents:
combined_text = "\n".join(documents[:3])
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question},
{"role": "system", "content": combined_text}
]
response = openai.ChatCompletion.create(
model="gpt-4",
messages=messages,
max_tokens=500
)
st.write("Answer:", response.choices[0].message['content'])
with tab2:
if mode == "PDF Summarizer" and documents:
summaries = []
for doc in documents[:3]:
messages = [
{"role": "system", "content": "You are a helpful assistant tasked to summarize documents."},
{"role": "user", "content": "Summarize the following text brifly:\n" + doc}
]
response = openai.ChatCompletion.create(
model="gpt-4",
messages=messages,
max_tokens=1024
)
summaries.append(response.choices[0].message['content'].strip())
for idx, summary in enumerate(summaries):
st.write(f"Summary {idx+1}:", summary)
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