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import streamlit as st
import os
import google.generativeai as genai
from PyPDF2 import PdfReader
from fpdf import FPDF
genai.configure(api_key="AIzaSyCHtsaOe5k-MXYO0RDnytc6iX7FpSKCPmE")
#genai.configure(api_key=os.environ.get("GEMINI_API_KEY"))
generation_config = {
"temperature": 2,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": 65536,
"response_mime_type": "text/plain",
}
model = genai.GenerativeModel(
model_name="gemini-2.0-flash-thinking-exp-01-21",
generation_config=generation_config,
)
def extract_text_from_pdf(file):
"""Extract text from an uploaded PDF file."""
reader = PdfReader(file)
text = ""
for page in reader.pages:
text += page.extract_text() + "\n"
return text
def stream_response(prompt):
"""Stream response from the generative model."""
chat_session = model.start_chat(
history=[{"role": "user", "parts": [prompt]}]
)
for response_chunk in chat_session.send_message("Processing input...", stream=True):
if response_chunk.text:
yield response_chunk.text
def create_pdf(content):
"""Create a well-formatted PDF file from the given content."""
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
lines = content.split("\n")
for line in lines:
formatted_line = line.replace("**", "") # Remove markdown-style bolding
pdf.multi_cell(0, 10, formatted_line)
return pdf
# Streamlit UI
st.title("Text Summarization and Insight Extraction")
# Session state to persist generated text
if "generated_text" not in st.session_state:
st.session_state.generated_text = ""
uploaded_file = st.file_uploader("Upload a document (PDF) or paste text below:", type=["pdf"])
input_text = st.text_area("Or paste your article here:", placeholder="Paste your article or text here...")
if st.button("Generate Summary and Insights"):
if uploaded_file:
input_text = extract_text_from_pdf(uploaded_file)
if input_text.strip():
st.markdown("### Generating Results...")
response_container = st.empty()
st.session_state.generated_text = "" # Clear previous text
for chunk in stream_response(f"Summarize the following text and extract key insights:\n{input_text}"):
st.session_state.generated_text += chunk
response_container.markdown(st.session_state.generated_text)
else:
st.warning("Please upload a file or enter text to process.")
if st.session_state.generated_text:
st.markdown("### Summary and Insights")
st.text_area("Results:", st.session_state.generated_text, height=300)
# PDF Download
pdf = create_pdf(st.session_state.generated_text)
pdf_file = "summary_insights.pdf"
pdf.output(pdf_file)
with open(pdf_file, "rb") as f:
st.download_button(
label="Download PDF",
data=f,
file_name="summary_insights.pdf",
mime="application/pdf"
)
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