Delete src/app

src/app/model.py

@@ -1,53 +0,0 @@
-# Necessary imports
-import os
-import sys
-from dotenv import load_dotenv
-from typing import Any
-import torch
-from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
-
-# Local imports
-from src.logger import logging
-from src.exception import CustomExceptionHandling
-
-
-# Load the Environment Variables from .env file
-load_dotenv()
-
-# Access token for using the model
-access_token = os.environ.get("ACCESS_TOKEN")
-
-
-def load_model_and_processor(model_name: str, device: str) -> Any:
-    """
-    Load the model and processor.
-
-    Args:
-        - model_name (str): The name of the model to load.
-        - device (str): The device to load the model onto.
-
-    Returns:
-        - model: The loaded model.
-        - processor: The loaded processor.
-    """
-    try:
-        # Load the model and processor
-        model = (
-            PaliGemmaForConditionalGeneration.from_pretrained(
-                model_name, torch_dtype=torch.bfloat16, token=access_token
-            )
-            .eval()
-            .to(device)
-        )
-        processor = PaliGemmaProcessor.from_pretrained(model_name, token=access_token)
-
-        # Log the successful loading of the model and processor
-        logging.info("Model and processor loaded successfully.")
-
-        # Return the model and processor
-        return model, processor
-
-    # Handle exceptions that may occur during model and processor loading
-    except Exception as e:
-        # Custom exception handling
-        raise CustomExceptionHandling(e, sys) from e

src/app/response.py

@@ -1,77 +0,0 @@
-# Necessary imports
-import sys
-import PIL.Image
-import torch
-import gradio as gr
-import spaces
-
-# Local imports
-from src.config import device, model_name
-from src.app.model import load_model_and_processor
-from src.logger import logging
-from src.exception import CustomExceptionHandling
-
-
-# Language dictionary
-language_dict = {
-    "English": "en",
-    "Spanish": "es",
-    "French": "fr",
-}
-
-# Model and processor
-model, processor = load_model_and_processor(model_name, device)
-
-
-@spaces.GPU
-def caption_image(
-    image: PIL.Image.Image, max_new_tokens: int, language: str, sampling: bool
-) -> str:
-    """
-    Generates a caption based on the given image using the model.
-
-    Args:
-        - image (PIL.Image.Image): The input image to be processed.
-        - max_new_tokens (int): The maximum number of new tokens to generate.
-        - language (str): The language of the generated caption.
-        - sampling (bool): Whether to use sampling or not.
-
-    Returns:
-        str: The generated caption text.
-    """
-    try:
-        # Check if image is None
-        if not image:
-            gr.Warning("Please provide an image.")
-
-        # Prepare the inputs
-        print(language)
-        language = language_dict[language]
-        print(language)
-        prompt = f"<image>caption {language}"
-        print(prompt)
-        model_inputs = (
-            processor(text=prompt, images=image, return_tensors="pt")
-            .to(torch.bfloat16)
-            .to(device)
-        )
-        input_len = model_inputs["input_ids"].shape[-1]
-
-        # Generate the response
-        with torch.inference_mode():
-            generation = model.generate(
-                **model_inputs, max_new_tokens=max_new_tokens, do_sample=sampling
-            )
-            generation = generation[0][input_len:]
-            decoded = processor.decode(generation, skip_special_tokens=True)
-
-        # Log the successful generation of the caption
-        logging.info("Caption generated successfully.")
-
-        # Return the generated caption
-        return decoded
-
-    # Handle exceptions that may occur during caption generation
-    except Exception as e:
-        # Custom exception handling
-        raise CustomExceptionHandling(e, sys) from e