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
from my_model.utilities.state_manager import StateManager

state_manager = StateManager()

def answer_question(caption, detected_objects_str, question, model):
    free_gpu_resources()
    answer = model.generate_answer(question, caption, detected_objects_str)
    free_gpu_resources()
    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 image_qa_app(kbvqa):
    # 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}'):
                state_manager.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:
        state_manager.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 state_manager.get_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 = state_manager.analyze_image(image_data['image'], kbvqa)
                state_manager.update_image_data(image_key, caption, detected_objects_str, 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)
                    state_manager.add_to_qa_history(image_key, question, answer)

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





def run_inference():
    
    st.title("Run Inference")
    state_manager.initialize_state()
    state_manager.set_up_widgets()
    st.session_state['settings_changed'] = state_manager.has_state_changed()
    if st.session_state['settings_changed']:
        st.warning("Model settings have changed, please reload the model, this will take a second .. ")

    st.session_state.button_label = "Reload Model" if state_manager.is_model_loaded() and state_manager.settings_changed else "Load Model"
   # state_manager.display_session_state()
    state_manager.display_model_settings()
    state_manager.display_session_state()
    
    
    if st.session_state.method == "Fine-Tuned Model":
        if st.button(st.session_state.button_label):
            if st.session_state.button_label == "Load Model":
                if state_manager.is_model_loaded():
                    st.text("Model already loaded and no settings were changed:)")
                    
                else: state_manager.load_model()
                
            else:
                state_manager.reload_detection_model()
                

        if state_manager.is_model_loaded() and st.session_state.kbvqa.all_models_loaded:
            image_qa_app(state_manager.get_model())

    else:
        st.write(f'Model using {st.session_state.method} is not deplyed yet, will be ready later.')