import gradio as gr import openai # import base64 from PIL import Image import io import fitz # PyMuPDF for PDF handling # Function to extract text from PDF files def extract_text_from_pdf(pdf_file): try: text = "" pdf_document = fitz.open(pdf_file) for page_num in range(len(pdf_document)): page = pdf_document[page_num] text += page.get_text() pdf_document.close() return text except Exception as e: return f"Error extracting text from PDF: {str(e)}" # Function to generate MCQ quiz from PDF content def generate_mcq_quiz(pdf_content, num_questions, openai_api_key, model_choice): if not openai_api_key: return "Error: No API key provided." openai.api_key = openai_api_key limited_content = pdf_content[:8000] if len(pdf_content) > 8000 else pdf_content prompt = f"""Based on the following document content, generate {num_questions} multiple-choice quiz questions. For each question: 1. Create a clear question based on key concepts in the document 2. Provide 4 possible answers (A, B, C, D) 3. Indicate the correct answer 4. Briefly explain why the answer is correct Format the output clearly with each question numbered and separated. Document content: {limited_content} """ try: response = openai.ChatCompletion.create( model=model_choice, messages=[{"role": "user", "content": prompt}] ) return response.choices[0].message.content except Exception as e: return f"Error generating quiz: {str(e)}" # Function to handle image inputs def generate_image_response(input_text, image, openai_api_key, model_choice): if not openai_api_key: return "Error: No API key provided." openai.api_key = openai_api_key # Convert image to base64 buffered = io.BytesIO() image.save(buffered, format="PNG") base64_str = base64.b64encode(buffered.getvalue()).decode("utf-8") try: response = openai.ChatCompletion.create( model=model_choice, messages=[ { "role": "user", "content": [ {"type": "text", "text": input_text}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_str}"} } ] } ], max_completion_tokens=2000 ) return response.choices[0].message.content except Exception as e: return f"Error processing image: {str(e)}" # Main chatbot function def chatbot(input_text, image, pdf_file, openai_api_key, model_choice, pdf_content, num_quiz_questions, pdf_quiz_mode, history): if history is None: history = [] new_pdf_content = pdf_content if pdf_file is not None: new_pdf_content = extract_text_from_pdf(pdf_file) if pdf_quiz_mode: if new_pdf_content: quiz_response = generate_mcq_quiz(new_pdf_content, int(num_quiz_questions), openai_api_key, model_choice) history.append((f"👤: [PDF Quiz - {num_quiz_questions} questions]", f"🤖: {quiz_response}")) else: history.append(("👤: [PDF Quiz]", "🤖: Please upload a PDF file to generate questions.")) else: if image is not None: response = generate_image_response(input_text, image, openai_api_key, model_choice) if input_text.strip(): history.append((f"👤: {input_text}", f"🤖: {response}")) else: history.append((f"👤: [Image]", f"🤖: {response}")) return "", None, None, new_pdf_content, history def clear_history(): return "", None, None, "", [] def update_input_type(choice): if choice == "Image": return ( gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(value=False) ) elif choice == "PDF(QUIZ)": return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(value=True) ) # Custom CSS styling custom_css = """ .gradio-container { font-family: 'Arial', sans-serif; background-color: #f0f4f8; } .gradio-header { background: linear-gradient(135deg, #4a00e0 0%, #8e2de2 100%); color: white; padding: 20px; border-radius: 8px; text-align: center; } .gradio-chatbot { background-color: white; border-radius: 10px; padding: 20px; box-shadow: 0 6px 18px rgba(0, 0, 0, 0.1); } #submit-btn { background: linear-gradient(135deg, #4a00e0 0%, #8e2de2 100%); color: white; border-radius: 8px; } #clear-history { background: linear-gradient(135deg, #e53e3e 0%, #f56565 100%); color: white; border-radius: 8px; } """ def create_interface(): with gr.Blocks(css=custom_css) as demo: gr.Markdown("""

Multimodal Chatbot (Image + PDF Quiz)

Analyze images or generate quizzes from PDFs

""") with gr.Accordion("Instructions", open=False): gr.Markdown(""" - **Image Chat**: Upload an image and ask questions about it - **PDF Quiz**: Upload a PDF and generate multiple-choice questions - Always provide your OpenAI API key - Choose appropriate model (o1 for images, o3-mini for text) """) pdf_content = gr.State("") with gr.Row(): openai_api_key = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...") with gr.Row(): input_type = gr.Radio(["Image", "PDF(QUIZ)"], label="Input Type", value="Image") with gr.Row(): input_text = gr.Textbox(label="Question (for images)", visible=True) image_input = gr.Image(label="Upload Image", type="pil", visible=True) pdf_input = gr.File(label="Upload PDF", visible=False) quiz_slider = gr.Slider(1, 20, value=5, step=1, label="Number of Questions", visible=False) quiz_mode = gr.Checkbox(visible=False) with gr.Row(): model_choice = gr.Dropdown(["o1", "o3-mini"], label="Model", value="o1") submit_btn = gr.Button("Submit", elem_id="submit-btn") clear_btn = gr.Button("Clear History", elem_id="clear-history") chat_history = gr.Chatbot() input_type.change( update_input_type, inputs=[input_type], outputs=[input_text, image_input, pdf_input, quiz_slider, quiz_mode] ) submit_btn.click( chatbot, inputs=[input_text, image_input, pdf_input, openai_api_key, model_choice, pdf_content, quiz_slider, quiz_mode, chat_history], outputs=[input_text, image_input, pdf_input, pdf_content, chat_history] ) clear_btn.click( clear_history, outputs=[input_text, image_input, pdf_input, pdf_content, chat_history] ) return demo if __name__ == "__main__": demo = create_interface() demo.launch()