Delete src

src/app/model.py

@@ -1,60 +0,0 @@
-# Necessary imports
-import os
-import sys
-from dotenv import load_dotenv
-from typing import Any
-import torch
-from transformers import AutoModel, AutoTokenizer, AutoProcessor
-
-# 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_tokenizer_and_processor(model_name: str, device: str) -> Any:
-    """
-    Load the model, tokenizer 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.
-        - tokenizer: The loaded tokenizer.
-        - processor: The loaded processor.
-    """
-    try:
-        # Load the model, tokenizer and processor
-        model = AutoModel.from_pretrained(
-            model_name,
-            trust_remote_code=True,
-            attn_implementation="sdpa",
-            torch_dtype=torch.bfloat16,
-            token=access_token
-        )
-        model = model.eval().to(device=device)
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_name, trust_remote_code=True, token=access_token
-        )
-        processor = AutoProcessor.from_pretrained(
-            model_name, trust_remote_code=True, token=access_token
-        )
-
-        # Log the successful loading of the model, tokenizer and processor
-        logging.info("Model, tokenizer and processor loaded successfully.")
-
-        # Return the model, tokenizer and processor
-        return model, tokenizer, processor
-
-    # Handle exceptions that may occur during model, tokenizer and processor loading
-    except Exception as e:
-        # Custom exception handling
-        raise CustomExceptionHandling(e, sys) from e

src/app/response.py

@@ -1,78 +0,0 @@
-# Necessary imports
-import sys
-import gradio as gr
-import spaces
-
-# Local imports
-from src.config import (
-    device,
-    model_name,
-    sampling,
-    stream,
-    repetition_penalty,
-)
-from src.app.model import load_model_tokenizer_and_processor
-from src.logger import logging
-from src.exception import CustomExceptionHandling
-
-
-# Model, tokenizer and processor
-model, tokenizer, processor = load_model_tokenizer_and_processor(model_name, device)
-
-
-@spaces.GPU(duration=120)
-def describe_image(
-    image: str, 
-    question: str,
-    temperature: float,
-    top_p: float,
-    top_k: int,
-    max_new_tokens: int,
-) -> str:
-    """
-    Generates an answer to a given question based on the provided image and question.
-
-    Args:
-        - image (str): The path to the image file.
-        - question (str): The question text.
-        - temperature (float): The temperature parameter for the model.
-        - top_p (float): The top_p parameter for the model.
-        - top_k (int): The top_k parameter for the model.
-        - max_new_tokens (int): The max tokens to be generated by the model.
-
-    Returns:
-        str: The generated answer to the question.
-    """
-    try:
-        # Check if image or question is None
-        if not image or not question:
-            gr.Warning("Please provide an image and a question.")
-
-        # Message format for the model
-        msgs = [{"role": "user", "content": [image, question]}]
-
-        # Generate the answer
-        answer = model.chat(
-            image=None,
-            msgs=msgs,
-            tokenizer=tokenizer,
-            processor=processor,
-            sampling=sampling,
-            stream=stream,
-            top_p=top_p,
-            top_k=top_k,
-            temperature=temperature,
-            repetition_penalty=repetition_penalty,
-            max_new_tokens=max_new_tokens,
-        )
-
-        # Log the successful generation of the answer
-        logging.info("Answer generated successfully.")
-
-        # Return the answer
-        return "".join(answer)
-
-    # Handle exceptions that may occur during answer generation
-    except Exception as e:
-        # Custom exception handling
-        raise CustomExceptionHandling(e, sys) from e

src/config.py

@@ -1,8 +0,0 @@
-# Model settings
-device = "cuda"
-model_name = "openbmb/MiniCPM-o-2_6"
-
-# Decoding settings
-sampling = True
-stream = True
-repetition_penalty = 1.05

src/exception.py

@@ -1,50 +0,0 @@
-"""
-This module defines a custom exception handling class and a function to get error message with details of the error.
-"""
-
-# Standard Library
-import sys
-
-# Local imports
-from src.logger import logging
-
-
-# Function Definition to get error message with details of the error (file name and line number) when an error occurs in the program
-def get_error_message(error, error_detail: sys):
-    """
-    Get error message with details of the error.
-
-    Args:
-        - error (Exception): The error that occurred.
-        - error_detail (sys): The details of the error.
-
-    Returns:
-        str: A string containing the error message along with the file name and line number where the error occurred.
-    """
-    _, _, exc_tb = error_detail.exc_info()
-
-    # Get error details
-    file_name = exc_tb.tb_frame.f_code.co_filename
-    return "Error occured in python script name [{0}] line number [{1}] error message[{2}]".format(
-        file_name, exc_tb.tb_lineno, str(error)
-    )
-
-
-# Custom Exception Handling Class Definition
-class CustomExceptionHandling(Exception):
-    """
-    Custom Exception Handling:
-        This class defines a custom exception that can be raised when an error occurs in the program.
-        It takes an error message and an error detail as input and returns a formatted error message when the exception is raised.
-    """
-
-    # Constructor
-    def __init__(self, error_message, error_detail: sys):
-        """Initialize the exception"""
-        super().__init__(error_message)
-
-        self.error_message = get_error_message(error_message, error_detail=error_detail)
-
-    def __str__(self):
-        """String representation of the exception"""
-        return self.error_message

src/logger.py

@@ -1,21 +0,0 @@
-# Importing the required modules
-import os
-import logging
-from datetime import datetime
-
-# Creating a log file with the current date and time as the name of the file
-LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
-
-# Creating a logs folder if it does not exist
-logs_path = os.path.join(os.getcwd(), "logs", LOG_FILE)
-os.makedirs(logs_path, exist_ok=True)
-
-# Setting the log file path and the log level
-LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
-
-# Configuring the logger
-logging.basicConfig(
-    filename=LOG_FILE_PATH,
-    format="[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s",
-    level=logging.INFO,
-)