app.py

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()