Update my_model/KBVQA.py

my_model/KBVQA.py

@@ -3,30 +3,67 @@ import torch
 import copy
 import os
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from typing import Optional
+from typing import Tuple, Optional
 from my_model.gen_utilities import free_gpu_resources
 from my_model.captioner.image_captioning import ImageCaptioningModel
 from my_model.object_detection import ObjectDetector
+import my_model.config.kbvqa_config as config
 
 
 class KBVQA():
+    """
+    The KBVQA class encapsulates the functionality for the Knowledge-Based Visual Question Answering (KBVQA) model. 
+    It integrates various components such as an image captioning model, object detection model, and a fine-tuned 
+    language model (LLAMA2) on OK-VQA dataset for generating answers to visual questions.
+
+    Attributes:
+        kbvqa_model_name (str): Name of the fine-tuned language model used for KBVQA.
+        quantization (str): The quantization setting for the model (e.g., '4bit', '8bit').
+        max_context_window (int): The maximum number of tokens allowed in the model's context window.
+        add_eos_token (bool): Flag to indicate whether to add an end-of-sentence token to the tokenizer.
+        trust_remote (bool): Flag to indicate whether to trust remote code when using the tokenizer.
+        use_fast (bool): Flag to indicate whether to use the fast version of the tokenizer.
+        low_cpu_mem_usage (bool): Flag to optimize model loading for low CPU memory usage.
+        kbvqa_tokenizer (Optional[AutoTokenizer]): The tokenizer for the KBVQA model.
+        captioner (Optional[ImageCaptioningModel]): The model used for generating image captions.
+        detector (Optional[ObjectDetector]): The object detection model.
+        detection_model (Optional[str]): The name of the object detection model.
+        detection_confidence (Optional[float]): The confidence threshold for object detection.
+        kbvqa_model (Optional[AutoModelForCausalLM]): The fine-tuned language model for KBVQA.
+        bnb_config (BitsAndBytesConfig): Configuration for BitsAndBytes optimized model.
+        access_token (str): Access token for Hugging Face API.
+
+    Methods:
+        create_bnb_config: Creates a BitsAndBytes configuration based on the quantization setting.
+        load_caption_model: Loads the image captioning model.
+        get_caption: Generates a caption for a given image.
+        load_detector: Loads the object detection model.
+        detect_objects: Detects objects in a given image.
+        load_fine_tuned_model: Loads the fine-tuned KBVQA model along with its tokenizer.
+        all_models_loaded: Checks if all the required models are loaded.
+        force_reload_model: Forces a reload of all models, freeing up GPU resources.
+        format_prompt: Formats the prompt for the KBVQA model.
+        generate_answer: Generates an answer to a given question using the KBVQA model.
+    """
 
     def __init__(self):
-        self.kbvqa_model_name = "m7mdal7aj/fine_tunned_llama_2_merged"
-        self.quantization='4bit'
-        self.bnb_config = self.create_bnb_config()
-        self.max_context_window = 4000
-        self.add_eos_token = False
-        self.trust_remote = False
-        self.use_fast = True
+        
+        self.kbvqa_model_name = config.KBVQA_MODEL_NAME
+        self.quantization = config.QUANTIZATION
+        self.max_context_window = config.MAX_CONTEXT_WINDOW
+        self.add_eos_token = config.ADD_EOS_TOKEN
+        self.trust_remote = config.TRUST_REMOTE
+        self.use_fast = config.USE_FAST
+        self.low_cpu_mem_usage=config.LOW_CPU_MEM_USAGE
         self.kbvqa_tokenizer = None
         self.captioner = None
         self.detector = None
         self.detection_model = None
         self.detection_confidence = None 
         self.kbvqa_model = None
-        self.access_token = os.getenv("HUGGINGFACE_TOKEN")
-      #  self.kbvqa_model_loaded = self.all_models_loaded() 
+        self.bnb_config = self.create_bnb_config()
+        self.access_token = config.HUGGINGFACE_TOKEN
+ 
 
  
     def create_bnb_config(self) -> BitsAndBytesConfig:
@@ -51,27 +88,59 @@ class KBVQA():
             )
 
 
-    def load_caption_model(self):
+    def load_caption_model(self) -> None:
+        """
+        Loads the image captioning model into the KBVQA instance.
+        """
+        
         self.captioner = ImageCaptioningModel()
         self.captioner.load_model()
 
-    def get_caption(self, img):
+    def get_caption(self, img: Image.Image) -> str:
+        """
+        Generates a caption for a given image using the image captioning model.
+
+        Args:
+            img (PIL.Image.Image): The image for which to generate a caption.
+
+        Returns:
+            str: The generated caption for the image.
+        """
 
         return self.captioner.generate_caption(img)
 
-    def load_detector(self, model):
+    def load_detector(self, model: str) -> None:
+        """
+        Loads the object detection model.
+
+        Args:
+            model (str): The name of the object detection model to load.
+        """
 
         self.detector = ObjectDetector()
         self.detector.load_model(model)
 
-    def detect_objects(self, img):
+    def detect_objects(self, img: Image.Image) -> Tuple[Image.Image, str]:
+        """
+        Detects objects in a given image using the loaded object detection model.
+
+        Args:
+            img (PIL.Image.Image): The image in which to detect objects.
+
+        Returns:
+            tuple: A tuple containing the image with detected objects drawn and a string representation of detected objects.
+        """
+        
         image = self.detector.process_image(img)
         detected_objects_string, detected_objects_list = self.detector.detect_objects(image, threshold=self.detection_confidence)
         image_with_boxes = self.detector.draw_boxes(img, detected_objects_list)
         return image_with_boxes, detected_objects_string
 
-    def load_fine_tuned_model(self):
-
+    def load_fine_tuned_model(self) -> None:
+        """
+        Loads the fine-tuned KBVQA model along with its tokenizer.
+        """
+        
         self.kbvqa_model = AutoModelForCausalLM.from_pretrained(self.kbvqa_model_name, 
                                                                 device_map="auto", 
                                                                 low_cpu_mem_usage=True, 
@@ -88,9 +157,20 @@ class KBVQA():
 
     @property
     def all_models_loaded(self):
+        """
+        Checks if all the required models (KBVQA, captioner, detector) are loaded.
+
+        Returns:
+            bool: True if all models are loaded, False otherwise.
+        """
+        
         return self.kbvqa_model is not None and self.captioner is not None and self.detector is not None
 
     def force_reload_model(self):
+        """
+        Forces a reload of all models, freeing up GPU resources. This method deletes the current models and calls `free_gpu_resources`.
+        """
+        
         free_gpu_resources()
         if self.kbvqa_model is not None:
             del self.kbvqa_model
@@ -101,17 +181,24 @@ class KBVQA():
 
         free_gpu_resources()
 
-        
-
-        
-            
 
+    def format_prompt(self, current_query: str, history: Optional[str] = None, sys_prompt: Optional[str] = None, caption: str = None, objects: Optional[str] = None) -> str:
+        """
+        Formats the prompt for the KBVQA model based on the provided parameters.
 
+        Args:
+            current_query (str): The current question to be answered.
+            history (str, optional): The history of previous interactions.
+            sys_prompt (str, optional): The system prompt or instructions for the model.
+            caption (str, optional): The caption of the image.
+            objects (str, optional): The detected objects in the image.
 
-    def format_prompt(self, current_query, history = None , sys_prompt=None, caption=None, objects=None):
+        Returns:
+            str: The formatted prompt for the KBVQA model.
+        """
 
         if sys_prompt is None:
-            sys_prompt = "You are a helpful, respectful and honest assistant for visual question answering. you are provided with a caption of an image and a list of objects detected in the image along with their bounding boxes and level of certainty, you will output an answer to the given questions in no more than one sentence. Use logical reasoning to reach to the answer, but do not output your reasoning process unless asked for it. If provided, you will use the [CAP] and [/CAP] tags to indicate the begining and end of the caption respectively. If provided you will use the [OBJ] and [/OBJ] tags to indicate the begining and end of the list of detected objects in the image along with their bounding boxes respectively.if provided, you will use [QES] and [/QES] tags to indicate the begining and end of the question respectively."
+            sys_prompt = config.SYSTEM_PROMPT 
     
         B_SENT = '<s>'
         E_SENT = '</s>'
@@ -126,7 +213,6 @@ class KBVQA():
         B_OBJ = '[OBJ]'
         E_OBJ = '[/OBJ]'
     
-    
         current_query = current_query.strip()
         sys_prompt = sys_prompt.strip()
         
@@ -138,11 +224,21 @@ class KBVQA():
         else:
             p = f"""{history}\n{B_SENT}{B_INST} {B_QES}{current_query}{E_QES}{E_INST}"""
         
-        
         return p
        
 
-    def generate_answer(self, question, caption, detected_objects_str,):
+    def generate_answer(self, question: str, caption: str, detected_objects_str: str) -> str:
+        """
+        Generates an answer to a given question using the KBVQA model.
+
+        Args:
+            question (str): The question to be answered.
+            caption (str): The caption of the image related to the question.
+            detected_objects_str (str): The string representation of detected objects in the image.
+
+        Returns:
+            str: The generated answer to the question.
+        """
         
         prompt = self.format_prompt(question, caption=caption, objects=detected_objects_str)
         num_tokens = len(self.kbvqa_tokenizer.tokenize(prompt))
@@ -159,7 +255,17 @@ class KBVQA():
 
         return output_text.capitalize()
 
-def prepare_kbvqa_model(only_reload_detection_model=False):
+def prepare_kbvqa_model(only_reload_detection_model: bool = False) -> KBVQA:
+    """
+    Prepares the KBVQA model for use, including loading necessary sub-models.
+
+    Args:
+        only_reload_detection_model (bool): If True, only the object detection model is reloaded.
+
+    Returns:
+        KBVQA: An instance of the KBVQA model ready for inference.
+    """
+    
     free_gpu_resources()
     kbvqa = KBVQA()
     kbvqa.detection_model = st.session_state.detection_model
@@ -177,12 +283,11 @@ def prepare_kbvqa_model(only_reload_detection_model=False):
             kbvqa.load_fine_tuned_model()
             free_gpu_resources()
             progress_bar.progress(100)
-
         else:
             progress_bar = st.progress(0)
             kbvqa.load_detector(kbvqa.detection_model)
             progress_bar.progress(100)
-    
+            
     if kbvqa.all_models_loaded:
         st.success('Model loaded successfully and ready for inferecne!')
         kbvqa.kbvqa_model.eval()