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src/exception.py

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+"""
+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

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+# 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,
+)

src/modernbert/classifier.py

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+# Necessary imports
+import sys
+from typing import Dict
+from src.logger import logging
+from src.exception import CustomExceptionHandling
+from transformers import pipeline
+import gradio as gr
+
+
+# Load the zero-shot classification model
+classifier = pipeline(
+    "zero-shot-classification",
+    model="MoritzLaurer/ModernBERT-large-zeroshot-v2.0",
+)
+
+
+def ZeroShotTextClassification(
+    text_input: str, candidate_labels: str, multi_label: bool
+) -> Dict[str, float]:
+    """
+    Performs zero-shot classification on the given text input.
+
+    Args:
+        - text_input: The input text to classify.
+        - candidate_labels: A comma-separated string of candidate labels.
+        - multi_label: A boolean indicating whether to allow the model to choose multiple classes.
+
+    Returns:
+        Dictionary containing label-score pairs.
+    """
+    try:
+        # Check if the input and candidate labels are valid
+        if not text_input or not candidate_labels:
+            gr.Warning("Please provide valid input and candidate labels")
+
+        # Split and clean the candidate labels
+        labels = [label.strip() for label in candidate_labels.split(",")]
+
+        # Log the classification attempt
+        logging.info(f"Attempting classification with {len(labels)} labels")
+
+        # Perform zero-shot classification
+        hypothesis_template = "This text is about {}"
+        prediction = classifier(
+            text_input,
+            labels,
+            hypothesis_template=hypothesis_template,
+            multi_label=multi_label,
+        )
+
+        # Return the classification results
+        logging.info("Classification completed successfully")
+        return {
+            prediction["labels"][i]: prediction["scores"][i]
+            for i in range(len(prediction["labels"]))
+        }
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e