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



# 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 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")
    image_qa_and_object_detection()

def image_qa_and_object_detection():
    # Image-based Q&A functionality
    st.subheader("Talk to your image")
    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()

    # Image uploader
    uploaded_image = st.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])

    # 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)

    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
    # 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 = answer_question(image_info['image'], question, None, None)  # Implement this function
                image_info['qa_history'].append((question, answer))
                st.experimental_rerun()  # Rerun to update the display

# Object Detection App
def object_detection_app():
    # ... Implement your code for object detection ...
    pass

# Main function and other display functions...

if __name__ == "__main__":
    main()