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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" | |
| ) | |