Update classytext/classifier/predict.py

classytext/classifier/predict.py

@@ -1,56 +1,61 @@
-# Necessary imports
-import sys
-from typing import Dict
-from classytext.logger import logging
-from classytext.exception import CustomExceptionHandling
-from transformers import pipeline
-
-
-# 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:
-        # 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
+# Necessary imports
+import sys
+from typing import Dict
+from classytext.logger import logging
+from classytext.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