feat: Remove SHAP

app.py

@@ -5,8 +5,6 @@ from numba.core.errors import NumbaDeprecationWarning
 warnings.filterwarnings("ignore", category=NumbaDeprecationWarning)
 import gradio as gr
 from transformers import pipeline
-from shap import Explainer
-import numpy as np
 from typing import Tuple, Dict, List
 
 
@@ -30,47 +28,13 @@ def main():
 
     def classification(text) -> Tuple[Dict[str, float], dict]:
         output: List[dict] = pipe(text)[0]
-        print(output)
+        print(text, output)
+        return {output["label"]: output["score"]}
 
-        explainer = Explainer(pipe)
-        explanation = explainer([text])
-        shap_values = explanation.values[0].sum(axis=1)
-
-        # Find the SHAP boundary
-        boundary = 0.03
-        if np.abs(shap_values).max() <= boundary:
-            boundary = np.abs(shap_values).max() - 1e-6
-
-        words: List[str] = explanation.data[0]
-        records = list()
-        char_idx = 0
-        for word, shap_value in zip(words, shap_values):
-
-            if abs(shap_value) <= boundary:
-                entity = 'O'
-            else:
-                entity = output['label'].lower().replace(' ', '-')
-
-            if len(word):
-                start = char_idx
-                char_idx += len(word)
-                end = char_idx
-                records.append(dict(
-                    entity=entity,
-                    word=word,
-                    score=abs(shap_value),
-                    start=start,
-                    end=end,
-                ))
-        print(records)
-
-        return ({output["label"]: output["score"]}, dict(text=text, entities=records))
-
-    color_map = {"offensive": "red", "not-offensive": "green", 'O': 'white'}
     demo = gr.Interface(
         fn=classification,
         inputs=gr.Textbox(placeholder="Enter sentence here...", value=examples[0]),
-        outputs=[gr.Label(), gr.HighlightedText().style(color_map=color_map)],
+        outputs=gr.Label(),
         examples=examples,
         title="Danish Offensive Text Detection",
         description="""

requirements.txt

@@ -88,7 +88,6 @@ rfc3986==1.5.0
 scikit-learn==1.2.2
 scipy==1.10.1
 semantic-version==2.10.0
-shap==0.41.0
 six==1.16.0
 slicer==0.0.7
 sniffio==1.3.0