import streamlit as st import torch import bitsandbytes import accelerate import scipy from PIL import Image import torch.nn as nn from transformers import Blip2Processor, Blip2ForConditionalGeneration, InstructBlipProcessor, InstructBlipForConditionalGeneration from my_model.object_detection import detect_and_draw_objects from my_model.captioner.image_captioning import get_caption from my_model.utilities import free_gpu_resources # Placeholder for undefined functions def load_caption_model(): st.write("Placeholder for load_caption_model function") return None, None def answer_question(image, question, model, processor): return "Placeholder answer for the question" def detect_and_draw_objects(image, model_name, threshold): return image, "Detected objects" def get_caption(image): return "Generated caption for the image" def free_gpu_resources(): pass # Sample images (assuming these are paths to your sample images) sample_images = ["path/to/sample1.jpg", "path/to/sample2.jpg", "path/to/sample3.jpg"] # Main function def main(): st.sidebar.title("Navigation") selection = st.sidebar.radio("Go to", ["Home", "Dataset Analysis", "Evaluation Results", "Run Inference", "Dissertation Report", "Object Detection"]) if selection == "Home": st.title("MultiModal Learning for Knowledg-Based Visual Question Answering") st.write("Home page content goes here...") elif selection == "Dissertation Report": st.title("Dissertation Report") st.write("Click the link below to view the PDF.") # Example to display a link to a PDF st.download_button( label="Download PDF", data=open("Files/Dissertation Report.pdf", "rb"), file_name="example.pdf", mime="application/octet-stream" ) elif selection == "Evaluation Results": st.title("Evaluation Results") st.write("This is a Place Holder until the contents are uploaded.") elif selection == "Dataset Analysis": st.title("OK-VQA Dataset Analysis") st.write("This is a Place Holder until the contents are uploaded.") elif selection == "Run Inference": run_inference() elif selection == "Object Detection": run_object_detection() # Other display functions... def run_inference(): st.title("Run Inference") # Image-based Q&A and Object Detection functionality image_qa_and_object_detection() def image_qa_and_object_detection(): # Image-based Q&A functionality st.subheader("Image-based Q&A") image_qa_app() # Object Detection functionality st.subheader("Object Detection") object_detection_app() def image_qa_app(): # Initialize session state for storing images and their Q&A histories if 'images_qa_history' not in st.session_state: st.session_state['images_qa_history'] = [] # Button to clear all data if st.button('Clear All'): st.session_state['images_qa_history'] = [] st.experimental_rerun() # Display sample images st.write("Or choose from sample images:") for idx, sample_image_path in enumerate(sample_images): if st.button(f"Use Sample Image {idx+1}", key=f"sample_{idx}"): uploaded_image = Image.open(sample_image_path) process_uploaded_image(uploaded_image) # Image uploader uploaded_image = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"]) if uploaded_image is not None: image = Image.open(uploaded_image) process_uploaded_image(image) def process_uploaded_image(image): current_image_key = image.filename # Use image filename as a unique key # ... rest of the image processing code ... # Object Detection App def object_detection_app(): # ... Implement your code for object detection ... pass # Other functions... if __name__ == "__main__": main()