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.utilities.gen_utilities import free_gpu_resources
from my_model.state_manager import StateManager


class InferenceRunner(StateManager):
    def __init__(self):
        
        super().__init__()
        self.initialize_state()
        self.sample_images = [
            "Files/sample1.jpg", "Files/sample2.jpg", "Files/sample3.jpg"]

    def answer_question(self, caption, detected_objects_str, question, model):
        free_gpu_resources()
        answer = model.generate_answer(question, caption, detected_objects_str)
        free_gpu_resources()
        return answer


    def image_qa_app(self, kbvqa):
        # Display sample images as clickable thumbnails
        self.col1.write("Choose from sample images:")
        cols = self.col1.columns(len(self.sample_images))
        for idx, sample_image_path in enumerate(self.sample_images):
            with cols[idx]:
                image = Image.open(sample_image_path)
                image_for_display = copy.deepcopy(image) # resize just for the display control without changing original image
                image_for_display = self.resize_image(image_for_display, 500, 500)
                st.image(image_for_display)
                if st.button(f'Select Sample Image {idx + 1}', key=f'sample_{idx}'):
                    self.process_new_image(sample_image_path, image, kbvqa)

        # Image uploader
        uploaded_image = self.col1.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
        if uploaded_image is not None:
            self.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 self.get_images_data().items():
            image_for_display = copy.deepcopy(image_data['image'])  # resize just for the display control without changing original image
            image_for_display = self.resize_image(image_for_display, 800, 800)
            self.col2.image(image_for_display, caption=f'Uploaded Image: {image_key[-11:]}')
            if not image_data['analysis_done']:
                self.col2.text("Cool image, please click 'Analyze Image'..")
                if self.col2.button('Analyze Image', key=f'analyze_{image_key}'):
                    caption, detected_objects_str, image_with_boxes = self.analyze_image(image_data['image'], kbvqa)
                    self.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 = self.col2.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}')
                if self.col2.button('Get Answer', key=f'answer_{image_key}'):
                    if question not in [q for q, _ in qa_history]:
                        answer = self.answer_question(image_data['caption'], image_data['detected_objects_str'], question, kbvqa)
                        self.add_to_qa_history(image_key, question, answer)

            # Display Q&A history for each image
            for q, a in qa_history:
                self.col2.text(f"Q: {q}\nA: {a}\n")


    def run_inference(self):
        self.set_up_widgets()
        st.session_state['settings_changed'] = self.has_state_changed()
        if st.session_state['settings_changed']:
            self.col1.warning("Model settings have changed, please reload the model, this will take a second .. ")






###################################
        self.col1.write("Choose from sample images:")
        cols = self.col1.columns(len(self.sample_images))
        for idx, sample_image_path in enumerate(self.sample_images):
            with cols[idx]:
                image = Image.open(sample_image_path)
                image_for_display = copy.deepcopy(image) # resize just for the display control without changing original image
                image_for_display = self.resize_image(image_for_display, 500, 500)
                st.image(image_for_display)


        # Image uploader
        uploaded_image = self.col1.file_uploader("Or upload an Image", type=["png", "jpg", "jpeg"])
        image_for_display = copy.deepcopy(uploaded_image)  # resize just for the display control without changing original image
        image_for_display = self.resize_image(image_for_display, 500, 500)

############################################







            

        st.session_state.button_label = "Reload Model" if self.is_model_loaded() and self.settings_changed else "Load Model"
        
        if st.session_state.method == "Fine-Tuned Model":
            if self.col1.button(st.session_state.button_label):
                if st.session_state.button_label == "Load Model":
                    if self.is_model_loaded():
                        free_gpu_resources()
                        self.col1.text("Model already loaded and no settings were changed:)")
                    else:
                        free_gpu_resources()
                        self.load_model()
                else:
                    free_gpu_resources()
                    self.reload_detection_model()

            if self.is_model_loaded() and st.session_state.kbvqa.all_models_loaded:
                free_gpu_resources()
                self.image_qa_app(self.get_model())
        else:
            self.col1.warning(f'Model using {st.session_state.method} is not deployed yet, will be ready later.')