Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
from transformers import pipeline
|
3 |
+
|
4 |
+
# Summarization
|
5 |
+
def summarization(text):
|
6 |
+
text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
|
7 |
+
summary = text_model(text, max_length=100, do_sample=False)[0]["generated_text"]
|
8 |
+
return summary
|
9 |
+
|
10 |
+
# Sentiment Classification
|
11 |
+
def sentiment_classification(summary):
|
12 |
+
sentiment_model = pipeline("text-classification", model="wxrrrrrrr/finetuned_sentiment_analysis")
|
13 |
+
result = sentiment_model(summary, max_length=100, do_sample=False)[0]['label']
|
14 |
+
return result
|
15 |
+
|
16 |
+
def main():
|
17 |
+
st.set_page_config(page_title="Your Text Analysis", page_icon="🦜")
|
18 |
+
st.header("Tell me your comments!")
|
19 |
+
text_input = st.text_input("Enter your text here:")
|
20 |
+
|
21 |
+
if text_input:
|
22 |
+
# Stage 1: Summarization
|
23 |
+
st.text('Processing text...')
|
24 |
+
summary = summarization(text_input)
|
25 |
+
# st.write(summary)
|
26 |
+
|
27 |
+
# Stage 2: Sentiment Classification
|
28 |
+
st.text('Analyzing sentiment...')
|
29 |
+
sentiment = sentiment_classification(summary)
|
30 |
+
st.write(sentiment)
|
31 |
+
|
32 |
+
# Display the classification result
|
33 |
+
st.write("Sentiment:", sentiment)
|
34 |
+
|
35 |
+
if __name__ == '__main__':
|
36 |
+
main()
|