feat: updating visualization again

.github/workflows/hgf-sync-main.yml

@@ -54,4 +54,4 @@ jobs:
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         # run git push
-        run: git push https://LennardZuendorf:[email protected]/spaces/LennardZuendorf/thesis-webapp-docker main
+        run: git push https://LennardZuendorf:[email protected]/spaces/LennardZuendorf/thesis main

explanation/visualize.py

@@ -1,6 +1,10 @@
 # visualization module that creates an attention visualization using BERTViz
 
 
+# external imports
+from bertviz import neuron_view as nv
+
+
 # internal imports
 from utils import formatting as fmt
 from .markup import markup_text
@@ -34,6 +38,30 @@ def chat_explained(model, prompt):
 
     # create the response text and marked text for ui
     response_text = fmt.format_output_text(decoder_text)
+    xai_graphic = attention_graphic(encoder_text, decoder_text, model)
     marked_text = markup_text(encoder_text, averaged_attention, variant="visualizer")
 
-    return response_text, "", marked_text
+    return response_text, xai_graphic, marked_text
+
+
+def attention_graphic(encoder_text, decoder_text, model):
+
+    # set model type to BERT (to fake out BERTViz)
+    model_type = "bert"
+
+    # create sentence a and b from list of strings
+    sentence_a = " ".join(encoder_text)
+    sentence_b = " ".join(decoder_text)
+
+    # display neuron view
+    return nv.show(
+        model.MODEL,
+        model_type,
+        model.TOKENIZER,
+        sentence_a,
+        sentence_b,
+        display_mode="light",
+        layer=2,
+        head=0,
+        html_action="return",
+    )

main.py

@@ -200,10 +200,10 @@ with gr.Blocks(
             xai_interactive = iFrame(
                 label="Interactive Explanation",
                 value=(
-                    '<div style="text-align: center"><h4>No Graphic to Display'
+                    '<div style="text-align: center; font-family:arial;"><h4>No Graphic to Display'
                     " (Yet)</h4></div>"
                 ),
-                height="600px",
+                height="1500px",
                 show_label=True,
             )
 

utils/formatting.py

@@ -75,5 +75,5 @@ def flatten_attention(values: ndarray, axis: int = 0):
 
 
 def avg_attention(attention_values):
-    attention = attention_values.cross_attentions[0][0].detach().numpy()
+    attention = attention_values.output_attentions[0][0].detach().numpy()
     return np.mean(attention, axis=0)