some code
Browse files- app.py +12 -3
- requirements.txt +2 -1
app.py
CHANGED
@@ -1,4 +1,7 @@
|
|
1 |
import streamlit as st #Web App
|
|
|
|
|
|
|
2 |
|
3 |
#title
|
4 |
st.title("Sentiment Analysis")
|
@@ -8,11 +11,17 @@ def analyze(input, model):
|
|
8 |
return "This is a sample output"
|
9 |
|
10 |
#text insert
|
11 |
-
input = st.text_area("insert text to be analyzed", value="
|
12 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
13 |
|
14 |
if st.button('Analyze'):
|
15 |
-
st.write(
|
16 |
else:
|
17 |
st.write('Goodbye')
|
18 |
|
|
|
1 |
import streamlit as st #Web App
|
2 |
+
from transformers import pipeline
|
3 |
+
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
|
4 |
+
|
5 |
|
6 |
#title
|
7 |
st.title("Sentiment Analysis")
|
|
|
11 |
return "This is a sample output"
|
12 |
|
13 |
#text insert
|
14 |
+
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")
|
15 |
+
model_name = st.text_input("choose a transformer model", value="")
|
16 |
+
|
17 |
+
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
|
18 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
19 |
+
|
20 |
+
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
|
21 |
+
|
22 |
|
23 |
if st.button('Analyze'):
|
24 |
+
st.write(classifier(input))
|
25 |
else:
|
26 |
st.write('Goodbye')
|
27 |
|
requirements.txt
CHANGED
@@ -1 +1,2 @@
|
|
1 |
-
streamlit
|
|
|
|
1 |
+
streamlit
|
2 |
+
transformers
|