Update app.py

app.py

@@ -1,8 +1,29 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
+import spacy
+from spacy import displacy
+
 from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
 
+def linkify():
+  import pandas as pd
+  import streamlit as st
+  link1 = "https://stackoverflow.com/questions/71641666/hyperlink-in-streamlit-dataframe"
+  link2 = "https://stackoverflow.com/questions/71731937/how-to-plot-comparison-in-streamlit-dynamically-with-multiselect"
+  df = pd.DataFrame(
+    {
+        "url": [
+            f'<a target="_blank" href="{link1}">Hyperlink in Streamlit dataframe</a>',
+            f'<a target="_blank" href="{link2}">How to plot comparison in Streamlit dynamically with multiselect?</a>'
+        ],
+        "label": ["question", "question"]
+    }
+  )
+  doc=df.to_html(escape=False, index=False)
+  html = displacy.render(doc, style="dep", page=True)
+  return html
+
 
 # summary function - test for single gradio function interfrace
 def bulk_function(filename):
@@ -17,6 +38,9 @@ def bulk_function(filename):
       def __getitem__(self, idx):
           return {k: v[idx] for k, v in self.tokenized_texts.items()}
 
+  html = linkify()
+  
+  
   # load tokenizer and model, create trainer
   model_name = "j-hartmann/emotion-english-distilroberta-base"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -106,11 +130,12 @@ def bulk_function(filename):
   # return dataframe for space output
   return YOUR_FILENAME
   
-gr.Interface(bulk_function, inputs=[gr.inputs.File(file_count="single", type="file", label="Upload file", optional=False),],
-             outputs=[gr.outputs.File(label="Output file")],
-            # examples=[["YOUR_FILENAME.csv"]], # computes, doesn't export df so far
-             theme="huggingface",
+gr.Interface(bulk_function, 
+  inputs=[gr.inputs.File(file_count="single", type="file", label="Upload file", optional=False),],
+  outputs=[gr.outputs.File(label="Output file")],
+  # examples=[["YOUR_FILENAME.csv"]], # computes, doesn't export df so far
+  theme="huggingface",
              title="Emotion Classification from CSV",
-             description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column. The script returns a new file that includes both the ID column and text column together with the emotion predictions using this model: https://huggingface.co/j-hartmann/emotion-english-distilroberta-base.",
+             description="Upload csv file with 2 columns (in order): (a) ID column, (b) text column. Model: https://huggingface.co/j-hartmann/emotion-english-distilroberta-base.",
              allow_flagging=False,
              ).launch(debug=True)