my_model/state_manager.py

import pandas as pd
import copy
from PIL import Image
import streamlit as st
from my_model.utilities.gen_utilities import free_gpu_resources
from my_model.KBVQA import KBVQA, prepare_kbvqa_model




class StateManager:

    def __init__(self):
        # Create three columns with different widths
        self.col1, self.col2, self.col3 = st.columns([0.2, 0.6, 0.2])  

    def initialize_state(self):
        if 'images_data' not in st.session_state:
            st.session_state['images_data'] = {}
        if 'kbvqa' not in st.session_state:
            st.session_state['kbvqa'] = None
        if "button_label" not in st.session_state: 
            st.session_state['button_label'] = "Load Model"
        if "previous_state" not in st.session_state:
            st.session_state['previous_state'] = {}
        if "settings_changed" not in st.session_state:
            st.session_state['settings_changed'] = self.settings_changed
            


    def set_up_widgets(self):
        """
        Sets up user interface widgets for selecting models, settings, and displaying model settings conditionally.
        """

        self.col1.selectbox("Choose a method:", ["Fine-Tuned Model", "In-Context Learning (n-shots)"], index=0, key='method')
        detection_model = self.col1.selectbox("Choose a model for objects detection:", ["yolov5", "detic"], index=1, key='detection_model')
        default_confidence = 0.2 if st.session_state.detection_model == "yolov5" else 0.4
        self.set_slider_value(text="Select minimum detection confidence level", min_value=0.1, max_value=0.9, value=default_confidence, step=0.1, slider_key_name='confidence_level', col=self.col1)

        # Conditional display of model settings

 
        show_model_settings = self.col3.checkbox("Show Model Settings", False)
        if show_model_settings:
            self.display_model_settings()


        
    def set_slider_value(self, text, min_value, max_value, value, step, slider_key_name, col=None):
        """
        Creates a slider widget with the specified parameters, optionally placing it in a specific column.

        Args:
            text (str): Text to display next to the slider.
            min_value (float): Minimum value for the slider.
            max_value (float): Maximum value for the slider.
            value (float): Initial value for the slider.
            step (float): Step size for the slider.
            slider_key_name (str): Unique key for the slider.
            col (streamlit.columns.Column, optional): Column to place the slider in. Defaults to None (displayed in main area).
        """
        
        if col is None:
            return st.slider(text, min_value, max_value, value, step, key=slider_key_name)
        else:
            return col.slider(text, min_value, max_value, value, step, key=slider_key_name)

        
    @property
    def settings_changed(self):
        """
        Checks if any model settings have changed compared to the previous state.
    
        Returns:
            bool: True if any setting has changed, False otherwise.
        """
        return self.has_state_changed()

    
    def display_model_settings(self):
        """
        Displays a table of current model settings in the third column.
    
        Uses formatted HTML to style the table for better readability.
        """
        self.col3.write("##### Current Model Settings:")
        data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items() if key in ["confidence_level", 'detection_model', 'method', 'kbvqa', 'previous_state', 'settings_changed', ]]
        df = pd.DataFrame(data)
        styled_df = df.style.set_properties(**{'background-color': 'black', 'color': 'white', 'border-color': 'white'}).set_table_styles([{'selector': 'th','props': [('background-color', 'black'), ('font-weight', 'bold')]}])
        self.col3.table(styled_df)

    
    def display_session_state(self):
        """
        Displays a table of the complete application state..
        """
  
        st.write("Current Model:")
        data = [{'Key': key, 'Value': str(value)} for key, value in st.session_state.items()]
        df = pd.DataFrame(data)
        st.table(df)
        

    def load_model(self):
        """
        Loads the KBVQA model based on the chosen method and settings.
    
        - Frees GPU resources before loading.
        - Calls `prepare_kbvqa_model` to create the model.
        - Sets the detection confidence level on the model object.
        - Updates previous state with current settings for change detection.
        - Updates the button label to "Reload Model".
        """
        
        try:
            free_gpu_resources()
            st.session_state['kbvqa'] = prepare_kbvqa_model()
            st.session_state['kbvqa'].detection_confidence = st.session_state.confidence_level
            
            # Update the previous state with current session state values
            st.session_state['previous_state'] = {'method': st.session_state.method, 'detection_model': st.session_state.detection_model, 'confidence_level': st.session_state.confidence_level}
            
            st.session_state['button_label'] = "Reload Model"
            #st.text('button changed')
            #self.has_state_changed()
            free_gpu_resources()
        except Exception as e:
            st.error(f"Error loading model: {e}")

            
    # Function to check if any session state values have changed
    def has_state_changed(self):
        """
        Compares current session state with the previous state to identify changes.
    
        Returns:
            bool: True if any change is found, False otherwise.
        """
        for key in st.session_state['previous_state']:
            if st.session_state[key] != st.session_state['previous_state'][key]:
                return True  # Found a change
        else: return False  # No changes found   

    
    def get_model(self):
        """
        Retrieve the KBVQA model from the session state.
           
        Returns: KBVQA object: The loaded KBVQA model, or None if not loaded.
        """
        return st.session_state.get('kbvqa', None)

    
    def is_model_loaded(self):
        """
        Checks if the KBVQA model is loaded in the session state.
    
        Returns:
            bool: True if the model is loaded, False otherwise.
        """
        return 'kbvqa' in st.session_state and st.session_state['kbvqa'] is not None

    
    def reload_detection_model(self):
        """
        Reloads only the detection model of the KBVQA model with updated settings.
    
        - Frees GPU resources before reloading.
        - Checks if the model is already loaded.
        - Calls `prepare_kbvqa_model` with `only_reload_detection_model=True`.
        - Updates detection confidence level on the model object.
        - Displays a success message if model is reloaded successfully.
        """
        
        try:
            free_gpu_resources()
            if self.is_model_loaded():
                prepare_kbvqa_model(only_reload_detection_model=True)
                st.session_state['kbvqa'].detection_confidence = st.session_state.confidence_level
                self.col1.success("Model reloaded with updated settings and ready for inference.")
            free_gpu_resources()
        except Exception as e:
            st.error(f"Error reloading detection model: {e}")

    
    def process_new_image(self, image_key, image, kbvqa):
        """
        Processes a new uploaded image by creating an entry in the `images_data` dictionary in the application session state.
    
        This dictionary stores information about each processed image, including:
            - `image`: The original image data.
            - `caption`: Generated caption for the image.
            - `detected_objects_str`: String representation of detected objects.
            - `qa_history`: List of questions and answers related to the image.
            - `analysis_done`: Flag indicating if analysis is complete.
    
        Args:
            image_key (str): Unique key for the image.
            image (obj): The uploaded image data.
            kbvqa (KBVQA object): The loaded KBVQA model.
        """
        
        if image_key not in st.session_state['images_data']:
            st.session_state['images_data'][image_key] = {
                'image': image,
                'caption': '',
                'detected_objects_str': '',
                'qa_history': [],
                'analysis_done': False
            }

    
    def analyze_image(self, image, kbvqa):
        """
        Analyzes the image using the KBVQA model.

        - Creates a copy of the image to avoid modifying the original.
        - Displays a "Analyzing the image .." message.
        - Calls KBVQA methods to generate a caption and detect objects.
        - Returns the generated caption, detected objects string, and image with bounding boxes.
    
        Args:
            image (obj): The image data to analyze.
            kbvqa (KBVQA object): The loaded KBVQA model.
    
        Returns:
            tuple: A tuple containing the generated caption, detected objects string, and image with bounding boxes.
        """
        img = copy.deepcopy(image)
        st.text("Analyzing the image .. ")
        caption = kbvqa.get_caption(img)
        image_with_boxes, detected_objects_str = kbvqa.detect_objects(img)
        return caption, detected_objects_str, image_with_boxes

    
    def add_to_qa_history(self, image_key, question, answer):
        """
        Adds a question-answer pair to the QA history of a specific image, to be used as hitory tracker.
    
        Args:
            image_key (str): Unique key for the image.
            question (str): The question asked about the image.
            answer (str): The answer generated by the KBVQA model.
        """
        if image_key in st.session_state['images_data']:
            st.session_state['images_data'][image_key]['qa_history'].append((question, answer))

    
    def get_images_data(self):
        """
        Returns the dictionary containing processed image data from the session state.
    
        Returns:
            dict: The dictionary storing information about processed images.
        """
        return st.session_state['images_data']

    
    def resize_image(self, image_input, new_width, new_height):
        """
        Resize an image given either a file path or a PIL Image object.
    
        Args:
        image_input (str or Image.Image): The image file path or a PIL Image object.
        new_width (int): The new width of the image.
        new_height (int): The new height of the image.
    
        Returns:
        Image.Image: The resized PIL Image object.
        """
        self.col2.write(type(image_input))
        if isinstance(image_input, str):
            # Open the image from a file path
            image = Image.open(image_input)
        elif isinstance(image_input, Image.Image):
            # Use the image directly if it's already a PIL Image object
            image = image_input
        else:
            raise ValueError("image_input must be a file path or a PIL Image object")
    
        resized_image = image.resize((new_width, new_height))
        return resized_image
    
    
    def update_image_data(self, image_key, caption, detected_objects_str, analysis_done):
        """
        Updates the information stored for a specific image in the `images_data` dictionary in the application session state.
    
        Args:
            image_key (str): Unique key for the image.
            caption (str): The generated caption for the image.
            detected_objects_str (str): String representation of detected objects.
            analysis_done (bool): Flag indicating if analysis of the image is complete.
        """
        if image_key in st.session_state['images_data']:
            st.session_state['images_data'][image_key].update({
                'caption': caption,
                'detected_objects_str': detected_objects_str,
                'analysis_done': analysis_done
            })