app.py

import streamlit as st
import torch
import bitsandbytes
import accelerate
import scipy
import copy
from PIL import Image
import torch.nn as nn
import pandas as pd
from my_model.object_detection import detect_and_draw_objects
from my_model.captioner.image_captioning import get_caption
from my_model.gen_utilities import free_gpu_resources
from my_model.KBVQA import KBVQA, prepare_kbvqa_model



def answer_question(caption, detected_objects_str, question, model):

    answer = model.generate_answer(question, caption, detected_objects_str)
    return answer


# 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 analyze_image(image, model):
    
    img = copy.deepcopy(image)  # we dont wanna apply changes to the original image
    caption = model.get_caption(img)
    image_with_boxes, detected_objects_str = model.detect_objects(img)
    st.text("I am ready, let's talk!")
    free_gpu_resources()
    
    return caption, detected_objects_str, image_with_boxes
    

def image_qa_app(kbvqa):
    if 'images_data' not in st.session_state:
        st.session_state['images_data'] = {}

    # 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)
            st.image(image, use_column_width=True)
            if st.button(f'Select Sample Image {idx + 1}', key=f'sample_{idx}'):
                process_new_image(sample_image_path, image, kbvqa)

    # Image uploader
    uploaded_image = st.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
    if uploaded_image is not None:
        process_new_image(uploaded_image.name, Image.open(uploaded_image), kbvqa)

    # Display and interact with each uploaded/selected image
    for image_key, image_data in st.session_state['images_data'].items():
        st.image(image_data['image'], caption=f'Uploaded Image: {image_key[-11:]}', use_column_width=True)
        if not image_data['analysis_done']:
            st.text("Cool image, please click 'Analyze Image'..")
            if st.button('Analyze Image', key=f'analyze_{image_key}'):
                caption, detected_objects_str, image_with_boxes = analyze_image(image_data['image'], kbvqa)  # we can use the image_with_boxes later if we want to show it. 
                image_data['caption'] = caption
                image_data['detected_objects_str'] = detected_objects_str
                image_data['analysis_done'] = True

        # Initialize qa_history for each image
        qa_history = image_data.get('qa_history', [])

        if image_data['analysis_done']:
            question = st.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}')
            if st.button('Get Answer', key=f'answer_{image_key}'):
                if question not in [q for q, _ in qa_history]:
                    answer = answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa)
                    qa_history.append((question, answer))
                    image_data['qa_history'] = qa_history
                else:
                    st.info("This question has already been asked.")

        # Display Q&A history for each image
        for q, a in qa_history:
            st.text(f"Q: {q}\nA: {a}\n")


def process_new_image(image_key, image, kbvqa):
    """Process a new image and update the session state."""
    if image_key not in st.session_state['images_data']:
        st.session_state['images_data'][image_key] = {
            'image': image,
            'caption': '',
            'detected_objects_str': '',
            'qa_history': [],
            'analysis_done': False
        }

def run_inference():
    st.title("Run Inference")
    st.write("Please note that this is not a general purpose model, it is specifically trained on OK-VQA dataset and is designed to give direct and short answers to the given questions.")

    method = st.selectbox(
        "Choose a method:",
        ["Fine-Tuned Model", "In-Context Learning (n-shots)"],
        index=0
    )

    detection_model = st.selectbox(
        "Choose a model for objects detection:",
        ["yolov5", "detic"],
        index=1  # "detic" is selected by default
    )

    default_confidence = 0.2 if detection_model == "yolov5" else 0.4
    confidence_level = st.slider(
        "Select minimum detection confidence level",
        min_value=0.1,
        max_value=0.9,
        value=default_confidence,
        step=0.1
    )

    if 'model_settings' not in st.session_state:
        st.session_state['model_settings'] = {'detection_model': detection_model, 'confidence_level': confidence_level}

    settings_changed = (st.session_state['model_settings']['detection_model'] != detection_model or
                        st.session_state['model_settings']['confidence_level'] != confidence_level)

    need_model_reload = settings_changed and 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None
    
    if need_model_reload:
        st.text("Model Settings have changed, please reload the model, this will take no time :)")

    button_label = "Reload Model" if need_model_reload else "Load Model"

    if method == "Fine-Tuned Model":
        if 'kbvqa' not in st.session_state:
            st.session_state['kbvqa'] = None

        if st.button(button_label):
            
            free_gpu_resources()
            if st.session_state['kbvqa'] is not None:
                if not settings_changed:
                    st.write("Model already loaded.")
                else: 
                    free_gpu_resources()
                    detection_model = st.session_state['model_settings']['detection_model']
                    confidence_level = st.session_state['model_settings']['confidence_level']
                    prepare_kbvqa_model(detection_model, only_reload_detection_model=True)  # only reload detection model with new settings
                    st.session_state['kbvqa'].detection_confidence = confidence_level
                    free_gpu_resources()
            else:
                st.text("Loading the model will take no more than a few minutes . .")
                st.session_state['kbvqa'] = prepare_kbvqa_model(detection_model)
                st.session_state['kbvqa'].detection_confidence = confidence_level
                st.session_state['model_settings'] = {'detection_model': detection_model, 'confidence_level': confidence_level}
                st.write("Model is ready for inference.")
                free_gpu_resources()

                

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

    else:
        st.write('Model is not ready yet, will be updated later.')


def display_model_settings():
    st.write("### Current Model Settings:")
    st.table(pd.DataFrame(st.session_state['model_settings'], index=[0]))

def display_session_state():
    st.write("### Current Session State:")
    # Convert session state to a list of dictionaries, each representing a row
    data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items()]
    # Create a DataFrame from the list
    df = pd.DataFrame(data)
    st.table(df)




            
# Main function
def main():

        
    st.sidebar.title("Navigation")
    selection = st.sidebar.radio("Go to", ["Home", "Dataset Analysis", "Finetuning and Evaluation Results", "Run Inference", "Dissertation Report", "Code"])
    st.sidebar.write("More Pages will follow .. ")

    if selection == "Home":
        st.title("MultiModal Learning for Knowledg-Based Visual Question Answering")
        st.write("""This application is an interactive element of the project and prepared by Mohammed Alhaj as part of the dissertation for Masters degree in Artificial Intelligence at the University of Bath. 
                    Further details will be updated later""")
        
    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 == "Finetuning and Evaluation Results":
        st.title("Finetuning and Evaluation Results")
        st.write("This is a Place Holder until the contents are uploaded.")


    elif selection == "Run Inference":
        run_inference()

    elif selection == "Code":
        st.title("Code")
        st.markdown("You can view the code for this project on the Hugging Face Space file page.")
        st.markdown("[View Code](https://huggingface.co/spaces/m7mdal7aj/Mohammed_Alhaj_PlayGround/tree/main)", unsafe_allow_html=True)

    elif selection == "More Pages will follow .. ":
        st.title("Staye Tuned")
        st.write("This is a Place Holder until the contents are uploaded.")


            


if __name__ == "__main__":
    main()