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import streamlit as st
import os
from groq import Groq
from dotenv import load_dotenv
from PyPDF2 import PdfReader, PdfWriter
from io import BytesIO
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from PIL import Image
# Load environment variables
load_dotenv()
# Initialize Groq API client
client = Groq(
api_key=os.environ.get("GROQ_API_KEY"), # Ensure this is defined in your .env file
)
# Function to summarize text using Groq API
def summarize_text_groq(input_text, model="llama-3.3-70b-versatile", max_tokens=150):
try:
response = client.chat.completions.create(
messages=[
{
"role": "system",
"content": "You are a helpful assistant.",
},
{
"role": "user",
"content": f"Summarize the following text:\n\n{input_text}",
},
],
model=model,
)
return response.choices[0].message.content.strip()
except Exception as e:
raise RuntimeError(f"API call failed: {e}")
# Function to extract text from a PDF file
def extract_text_from_pdf(uploaded_pdf):
try:
pdf_reader = PdfReader(uploaded_pdf)
if pdf_reader.is_encrypted:
st.error("❌ The uploaded PDF is encrypted and cannot be processed.")
return ""
text = ""
for page in pdf_reader.pages:
text += page.extract_text() or "" # Handle pages with no text gracefully
if not text.strip():
raise RuntimeError("No extractable text found in the PDF.")
return text
except Exception as e:
raise RuntimeError(f"Failed to extract text from PDF: {e}")
# Function to save summary as a PDF
def save_summary_to_pdf(summary_text):
try:
# Use BytesIO to create an in-memory PDF
summary_stream = BytesIO()
c = canvas.Canvas(summary_stream, pagesize=letter)
c.drawString(100, 750, "Summary:")
text_object = c.beginText(100, 730) # Start the text object at this position
text_object.setFont("Helvetica", 10)
# Split text into lines for better formatting
lines = summary_text.splitlines()
for line in lines:
text_object.textLine(line)
c.drawText(text_object)
c.save()
# Seek to the start of the BytesIO stream
summary_stream.seek(0)
return summary_stream
except Exception as e:
raise RuntimeError(f"Failed to save summary to PDF: {e}")
# Streamlit App Setup
st.set_page_config(page_title="Text Summarization App", page_icon="📚", layout="wide")
st.title("📚 Text Summarization App with Groq API")
# Custom CSS styling
st.markdown("""
<style>
.main {
background-color: #f4f7fc;
padding: 20px;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border: none;
padding: 15px 32px;
font-size: 16px;
cursor: pointer;
border-radius: 5px;
margin-top: 20px;
}
.stButton>button:hover {
background-color: #45a049;
}
.stTextInput>div>div>input {
font-size: 16px;
padding: 10px;
border-radius: 5px;
border: 1px solid #ccc;
}
</style>
""", unsafe_allow_html=True)
# Instructions or greeting
st.markdown("""
<div style="font-size: 18px; color: #444;">
Welcome to the Text Summarization App! You can enter text or upload a PDF to get a concise summary using Groq API. Feel free to explore the tabs below.
</div>
""", unsafe_allow_html=True)
# Tabs for manual text and PDF upload
tab1, tab2, tab3 = st.tabs(["Manual Text Input", "PDF Upload", "🗣️ Chat with Bot"])
# Manual Text Input Tab
with tab1:
st.subheader("📝 Enter Your Text")
input_text = st.text_area("Enter the text to summarize", height=200, max_chars=2000)
if st.button("🔍 Summarize Text"):
if input_text:
with st.spinner("Summarizing your text..."):
try:
summary = summarize_text_groq(input_text)
st.success("✅ Summary:")
st.write(summary)
except Exception as e:
st.error(f"❌ An error occurred: {e}")
else:
st.warning("⚠️ Please enter some text to summarize!")
# PDF Upload Tab
with tab2:
st.subheader("📤 Upload a PDF for Summarization")
uploaded_pdf = st.file_uploader("Upload PDF", type=["pdf"])
if uploaded_pdf is not None:
with st.spinner("Extracting text from PDF..."):
try:
extracted_text = extract_text_from_pdf(uploaded_pdf)
st.success("✅ Text extracted from PDF.")
st.text_area("📄 Extracted Text:", extracted_text, height=200)
if st.button("🔍 Summarize PDF"):
with st.spinner("Summarizing the extracted text..."):
try:
summary = summarize_text_groq(extracted_text)
st.success("✅ PDF Summary:")
st.write(summary)
# Save the summary to a new PDF
summary_pdf = save_summary_to_pdf(summary)
st.download_button(
label="💾 Download Summary PDF",
data=summary_pdf,
file_name="summary.pdf",
mime="application/pdf",
)
except Exception as e:
st.error(f"❌ An error occurred: {e}")
except RuntimeError as e:
st.error(f"❌ {e}")
# Chat with Bot Tab
with tab3:
st.subheader("🗣️ Chat with the Bot")
if "messages" not in st.session_state:
st.session_state.messages = [{"role": "system", "content": "You are a helpful assistant."}]
# Display chat history
for message in st.session_state.messages:
if message["role"] == "user":
st.write(f"**User**: {message['content']}")
else:
st.write(f"**Bot**: {message['content']}")
user_input = st.text_input("Type your message:", "")
if st.button("Send Message"):
if user_input:
# Add user input to chat history
st.session_state.messages.append({"role": "user", "content": user_input})
# Get bot's response
with st.spinner("Bot is typing..."):
try:
response = client.chat.completions.create(
messages=st.session_state.messages,
model="llama-3.3-70b-versatile", # Groq model
)
bot_message = response.choices[0].message.content.strip()
# Add bot response to chat history
st.session_state.messages.append({"role": "assistant", "content": bot_message})
st.write(f"**Bot**: {bot_message}")
except Exception as e:
st.error(f"❌ An error occurred: {e}")
else:
st.warning("⚠️ Please enter a message to send.")
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