debugging 2

Untitled.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "307086c9",
+   "id": "bdae1670",
    "metadata": {},
    "outputs": [
     {
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "d27100b6",
+   "id": "0e096b7a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,8 +37,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "fa1ac990",
+   "execution_count": 23,
+   "id": "6969c62c",
    "metadata": {},
    "outputs": [
     {
@@ -52,7 +52,7 @@
        "  {'label': 'LABEL_3', 'score': 0.04354238137602806}]]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,7 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "fd9fc0a2",
+   "id": "db263028",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "68a7f0da",
+   "id": "bdb59ed1",
    "metadata": {},
    "outputs": [
     {

app.py

@@ -19,7 +19,7 @@ labels = {'LABEL_0': 'toxic', 'LABEL_1': 'severe_toxic', 'LABEL_2': 'obscene', '
 # make a dictionary of the labels with keys like "LABEL_0" and values like "toxic"
 
 #text insert
-input = st.text_area("insert text to be analyzed", value="Nice to see you today.", 
+input = st.text_area("Insert text to be analyzed", value="Nice to see you today.", 
                      height=None, max_chars=None, key=None, help=None, on_change=None, 
                      args=None, kwargs=None, placeholder=None, disabled=False, 
                      label_visibility="visible")
@@ -36,7 +36,7 @@ if option == 'Fine-Tuned':
 elif option == 'Roberta':
     model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
     tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
-    classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+    classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer, top_k=None)
 else:
     classifier = pipeline('sentiment-analysis')
 
@@ -46,6 +46,7 @@ if st.button('Analyze'):
     print(result)
     print(type(result))
     output = None
+    result = result[0]
     if option == 'Fine-Tuned':
         output = {'Toxic': result['LABEL_0']}
         del result['LABEL_0']