docs: add sphinx documentation for image_reco module

Refs: OPS-92 OPS-93

senju/image_reco.py

@@ -1,3 +1,40 @@
+"""
+Senju Image Recognition Module
+=============================
+
+A module providing image description generation capabilities for the Senju haiku application.
+
+This module leverages pre-trained vision-language models (specifically BLIP) to generate
+textual descriptions of uploaded images. These descriptions can then be used as input
+for the haiku generation process, enabling image-to-haiku functionality.
+
+Classes
+-------
+ImageDescriptionGenerator
+    The primary class responsible for loading the vision-language model
+    and generating descriptions from image data.
+
+Functions
+---------
+gen_response
+    A helper function that wraps the description generation process
+    for API integration.
+
+Dependencies
+------------
+* torch: Deep learning framework required for model operations
+* PIL.Image: Image processing capabilities
+* io: Utilities for working with binary data streams
+* transformers: Hugging Face's library providing access to pre-trained models
+
+Implementation Details
+---------------------
+The module initializes a BLIP model (Bootstrapped Language-Image Pre-training)
+which can understand visual content and generate natural language descriptions.
+The implementation handles image loading, preprocessing, model inference,
+and post-processing to return structured description data.
+"""
+
 import torch
 from PIL import Image
 import io
@@ -5,14 +42,28 @@ from transformers import BlipProcessor, BlipForConditionalGeneration
 
 
 class ImageDescriptionGenerator:
+    """
+    A class for generating textual descriptions of images using a vision-language model.
+
+    This class handles the loading of a pre-trained BLIP model, image preprocessing,
+    and caption generation. It provides an interface for converting raw image data
+    into natural language descriptions that can be used for haiku inspiration.
+
+    :ivar processor: The BLIP processor for handling image inputs
+    :type processor: BlipProcessor
+    :ivar model: The BLIP model for conditional text generation
+    :type model: BlipForConditionalGeneration
+    :ivar device: The computation device (CUDA or CPU)
+    :type device: str
+    """
+
     def __init__(self, model_name="Salesforce/blip-image-captioning-base"):
         """
         Initialize an image description generator using a vision-language
         model.
 
-        Args:
-            model_name: The name of the model to use
-                        (default: BLIP captioning model)
+        :param model_name: The name of the pre-trained model to use
+        :type model_name: str
         """
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         print(f"Using device: {self.device}")
@@ -24,13 +75,15 @@ class ImageDescriptionGenerator:
         """
         Generate a descriptive caption for the given image.
 
-        Args:
-            image_data: Raw image data (bytes)
-            max_length: Maximum length of the generated caption
+        This method processes the raw image data, runs inference with the BLIP model,
+        and returns a structured response with the generated description.
 
-        Returns:
-            dict: A dictionary containing the generated description
-            and confidence score
+        :param image_data: Raw binary image data
+        :type image_data: bytes
+        :param max_length: Maximum token length for the generated caption
+        :type max_length: int
+        :return: Dictionary containing the generated description and confidence score
+        :rtype: dict
         """
         # Convert uploaded bytes to image
         img = Image.open(io.BytesIO(image_data)).convert("RGB")
@@ -59,8 +112,21 @@ class ImageDescriptionGenerator:
         }
 
 
+# Global instance of the description generator
 g_descriptor: ImageDescriptionGenerator = ImageDescriptionGenerator()
 
 
 def gen_response(image_data) -> dict:
+    """
+    Generate a description for an image using the global description generator.
+
+    This function provides a simplified interface to the image description functionality
+    for use in API endpoints.
+
+    :param image_data: Raw binary image data
+    :type image_data: bytes
+    :return: Dictionary containing the image description and confidence information
+    :rtype: dict
+    :raises Exception: If image processing or description generation fails
+    """
     return g_descriptor.generate_description(image_data)