app.py

import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
from PIL import Image
import torch.nn as nn
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
from my_model.KBVQA import KBVQA, prepare_kbvqa_model



def answer_question(image, question, model):

    answer = model.generate_answer(question, image)
    return answer

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 = ["Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg", 
                 "Files/sample4.jpg", "Files/sample5.jpg", "Files/sample6.jpg", 
                 "Files/sample7.jpg"]

def run_inference():
    st.title("Run Inference")

    # Initialize session state for the model
    if 'kbvqa' not in st.session_state:
        st.session_state['kbvqa'] = None

    # Button to load KBVQA models
    if st.button('Load KBVQA Model'):
        if st.session_state['kbvqa'] is not None:
            st.write("Model already loaded.")
        else:
            # Call the function to load models and show progress
            st.session_state['kbvqa'] = prepare_kbvqa_model('yolov5')  # Replace with your model

        if st.session_state['kbvqa']:
            st.write("Model is ready for inference.")
        else:
            st.write("Please load the model first")

    if st.session_state['kbvqa']:
        image_qa_app(st.session_state['kbvqa'])


def image_qa_app(kbvqa):
    # Initialize session state for storing the current image and its Q&A history
    if 'current_image' not in st.session_state:
        st.session_state['current_image'] = None
    if 'qa_history' not in st.session_state:
        st.session_state['qa_history'] = []

    # Display sample images as clickable thumbnails
    st.write("Choose from sample images:")
    cols = st.columns(len(sample_images))
    for idx, sample_image_path in enumerate(sample_images):
        with cols[idx]:
            image = Image.open(sample_image_path)
            # Use button for each image
            if st.button(f'Sample Image {idx + 1}', key=f'sample_{idx}'):
                st.session_state['current_image'] = image
                st.session_state['qa_history'] = []

    # Image uploader
    uploaded_image = st.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
    if uploaded_image is not None:
        image = Image.open(uploaded_image)
        st.session_state['current_image'] = image
        st.session_state['processed_image'] = copy.deepcopy(image)  # Create a copy for processing
        st.session_state['qa_history'] = []

    # Display the current image (unaltered)
    if st.session_state.get('current_image') is not None:
        st.image(st.session_state['current_image'], caption='Uploaded Image.', use_column_width=True)

        # Question input and processing
        question = st.text_input("Ask a question about this image:")
        if st.button('Get Answer'):
            # Use the processed image for object detection and other processing
            processed_image = st.session_state.get('processed_image')
            if processed_image:
                # Perform object detection or other processing on processed_image
                # For example: processed_image = perform_object_detection(processed_image)
                answer = answer_question(processed_image, question, model=kbvqa)
                st.session_state['qa_history'].append((question, answer))

            # Display all Q&A
            for q, a in st.session_state['qa_history']:
                st.text(f"Q: {q}\nA: {a}\n")

            # Update the processed image in session state
            st.session_state['processed_image'] = processed_image

            
# 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()

if __name__ == "__main__":
    main()