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 = self.resize_image(sample_image_path, 80, 80)
                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
        with self.col2:
            for image_key, image_data in self.get_images_data().items():
                with st.container():
                    nested_col21, nested_col22 = st.columns([0.65, 0.35])
                    image_for_display = self.resize_image(image_data['image'], 600)
                    nested_col21.image(image_for_display, caption=f'Uploaded Image: {image_key[-11:]}')
                    if not image_data['analysis_done']:
                        nested_col22.text("Please click 'Analyze Image'..")
                        with nested_col22:
                            if st.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 = nested_col22.text_input(f"Ask a question about this image ({image_key[-11:]}):", key=f'question_{image_key}')
                        if nested_col22.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)
                            else: nested_col22.warning("This questions has already been answered.")
        
                    # Display Q&A history for each image
                    for q, a in qa_history:
                        nested_col22.text(f"Q: {q}\nA: {a}\n")

    def display_message(self, message, warning=False, write=False, text=False):
        pass

        
    def run_inference(self):
        
        self.set_up_widgets()
        load_fine_tuned_model = False
        fine_tuned_model_already_loaded = False
        reload_detection_model = False
        force_reload_full_model = False
        
        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 .. ")
           

        st.session_state.button_label = "Reload Model" if self.is_model_loaded() and self.settings_changed else "Load Model"

        with self.col1:
            
            if self.is_model_loaded():
                free_gpu_resources()
                self.image_qa_app(self.get_model())
                
            if st.session_state.method == "Fine-Tuned Model":
                
                with st.container():
                    nested_col11, nested_col12 = st.columns([0.5, 0.5])
                    if nested_col11.button(st.session_state.button_label):
                        
                        if st.session_state.button_label == "Load Model":
                            st.session_state['load_button_clicked'] = True
                            if self.is_model_loaded():
                                free_gpu_resources()
                                fine_tuned_model_already_loaded = True
                            else:
                                load_fine_tuned_model = True
                        else:
                            reload_detection_model = True
                            
                    if nested_col12.button("Force Reload"):
                        force_reload_full_model = True

                if load_fine_tuned_model:
                    free_gpu_resources()
                    self.load_model()
                    
                elif fine_tuned_model_already_loaded:
                    free_gpu_resources()
                    self.col1.text("Model already loaded and no settings were changed:)")      
                    
                elif reload_detection_model:
                    free_gpu_resources()
                    self.reload_detection_model()
                elif force_reload_full_model:
                    free_gpu_resources()
                    self.force_reload_model()

                
                
            elif st.session_state.method == "In-Context Learning (n-shots)":
                self.col1.warning(f'Model using {st.session_state.method} is not deployed yet, will be ready later.')