Upload 2 files

color issues fixed

app.py

@@ -122,12 +122,12 @@ def xai_attributions_html(input_text: str):
     html = html.replace("#/s", "")
     html = sub("<th.*?/th>", "", html, 4, DOTALL)
     html = sub("<td.*?/td>", "", html, 4, DOTALL)
-    return word_attributions, html
+    return word_attributions, html+"<br>"
 
 
 def explanation_intro(prediction_label: str):
     """
-    generates model explanaiton markdown from prediction label of the model.
+    generates model explanaiton html markdown from prediction label of the model.
 
     Args:
       prediction_label (str): The label that the model predicted.
@@ -135,10 +135,11 @@ def explanation_intro(prediction_label: str):
     Returns:
       A string
     """
-    return f"""The model predicted the given sentence as **:blue['{prediction_label}']**.
+    return f"""<div style="background-color: lightblue;
+  color: rgb(0, 66, 128);">The model predicted the given sentence as <span style="color: black"><strong>'{prediction_label}'</strong></span>.
     The figure below shows the contribution of each token to this decision.
-    **:green[Green]** tokens indicate a **positive contribution**, while **:red[red]** tokens indicate a **negative** contribution.
-    The **bolder** the color, the greater the value."""
+    <span style="color: darkgreen"><strong> Green </strong></span> tokens indicate a <strong>positive </strong> contribution, while <span style="color: red"><strong> red </strong></span> tokens indicate a <strong>negative</strong> contribution.
+    The <strong>bolder</strong> the color, the greater the value.</div><br>"""
 
 
 def explanation_viz(prediction_label: str, word_attributions):
@@ -154,7 +155,7 @@ def explanation_viz(prediction_label: str, word_attributions):
       A string
     """
     top_attention_word = max(word_attributions, key=itemgetter(1))[0]
-    return f"""The token **_'{top_attention_word}'_** is the biggest driver for the decision of the model as **:blue['{prediction_label}']**."""
+    return f"""The token **_'{top_attention_word}'_** is the biggest driver for the decision of the model as **'{prediction_label}'**"""
 
 
 def word_attributions_dict_creater(word_attributions):
@@ -237,7 +238,7 @@ if submit:
     label_probs_figure, prediction_label = label_probs_figure_creater(input_text)
     st.plotly_chart(label_probs_figure, config=hide_plotly_bar)
     explanation_general = explanation_intro(prediction_label)
-    st.info(explanation_general)
+    st.markdown(explanation_general, unsafe_allow_html=True)
     with st.spinner():
       word_attributions, html = xai_attributions_html(input_text)
       st.markdown(html, unsafe_allow_html=True)

requirements.txt

@@ -1,6 +1,5 @@
 --find-links https://download.pytorch.org/whl/torch_stable.html
 torch==1.13.1+cpu
-streamlit==1.16.0
 accelerate
 plotly
 transformers