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

# 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":
        display_home()
    elif selection == "Dissertation Report":
        display_dissertation_report()
    elif selection == "Evaluation Results":
        display_evaluation_results()
    elif selection == "Dataset Analysis":
        display_dataset_analysis()
    elif selection == "Run Inference":
        run_inference()
    elif selection == "Object Detection":
        run_object_detection()

def display_home():
    st.title("MultiModal Learning for Knowledge-Based Visual Question Answering")
    st.write("Home page content goes here...")

def display_dissertation_report():
    st.title("Dissertation Report")
    st.write("Click the link below to view the PDF.")
    st.download_button(
        label="Download PDF",
        data=open("Files/Dissertation Report.pdf", "rb"),
        file_name="example.pdf",
        mime="application/octet-stream"
    )

def display_evaluation_results():
    st.title("Evaluation Results")
    st.write("This is a Place Holder until the contents are uploaded.")

def display_dataset_analysis():
    st.title("OK-VQA Dataset Analysis")
    st.write("This is a Place Holder until the contents are uploaded.")

def run_inference():
    st.title("Image-based Q&A App")
    # Image-based Q&A functionality
    image_qa_app()

def run_object_detection():
    st.title("Object Detection")
    # Object Detection functionality
    # ... Implement your code for this section ...

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()

    # 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)
        current_image_key = uploaded_image.name  # Use image name as a unique key

        # Check if the image is already in the history
        if not any(info['image_key'] == current_image_key for info in st.session_state['images_qa_history']):
            st.session_state['images_qa_history'].append({
                'image_key': current_image_key,
                'image': image,
                'qa_history': []
            })

        # Display all images and their Q&A histories
        for image_info in st.session_state['images_qa_history']:
            st.image(image_info['image'], caption='Uploaded Image.', use_column_width=True)
            for q, a in image_info['qa_history']:
                st.text(f"Q: {q}\nA: {a}\n")

            # If the current image is being processed
            if image_info['image_key'] == current_image_key:
                # Unique keys for each widget
                question_key = f"question_{current_image_key}"
                button_key = f"button_{current_image_key}"

                # Question input for the current image
                question = st.text_input("Ask a question about this image:", key=question_key)

                # Get Answer button for the current image
                if st.button('Get Answer', key=button_key):
                    # Process the image and question
                    answer = get_answer(image_info['image'], question)  # Implement this function
                    image_info['qa_history'].append((question, answer))
                    st.experimental_rerun()  # Rerun to update the display

def get_answer(image, question):
    # Implement the logic to process the image and question, and return the answer
    return "Sample answer based on the image and question."

if __name__ == "__main__":
    main()