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| import gradio as gr | |
| from PyPDF2 import PdfReader | |
| # Function to extract text from PDF | |
| def extract_text(pdf_file): | |
| reader = PdfReader(pdf_file) # filepath is directly passed now | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() or "" | |
| return text | |
| # Store extracted text | |
| pdf_text = "" | |
| def upload_pdf(file): | |
| global pdf_text | |
| pdf_text = extract_text(file) | |
| return "✅ PDF uploaded. You can now ask questions." | |
| # Very simple QA function (keyword-based) | |
| def ask_question(question): | |
| global pdf_text | |
| if not pdf_text: | |
| return "❌ Please upload a PDF first." | |
| # Simple search: find if keywords exist in PDF text | |
| question_words = question.lower().split() | |
| sentences = pdf_text.split("\n") | |
| for sentence in sentences: | |
| if all(word in sentence.lower() for word in question_words): | |
| return sentence | |
| return "🤔 Sorry, I couldn't find an exact match in the PDF." | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 📄 PDF Chatbot") | |
| with gr.Row(): | |
| pdf_upload = gr.File(label="Upload a PDF", type="filepath") | |
| upload_btn = gr.Button("Process PDF") | |
| chatbox = gr.Chatbot() | |
| question_input = gr.Textbox(label="Ask me anything about the PDF") | |
| ask_btn = gr.Button("Ask") | |
| upload_btn.click(upload_pdf, inputs=pdf_upload, outputs=chatbox) | |
| ask_btn.click( | |
| lambda q: ("You:", q), inputs=question_input, outputs=chatbox | |
| ).then( | |
| ask_question, inputs=question_input, outputs=chatbox | |
| ) | |
| demo.launch() |