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c2a70ce a3d1a03 c2a70ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import streamlit as st
import pandas as pd
from transformers import pipeline
from collections import Counter
model = pipeline('text-classification', model='SentimentAnalysis')
st.title("Sentiment Analysis")
uploaded_file = st.file_uploader('Upload CSV file', type='csv')
def get_predictions(filename):
try:
df = pd.read_csv(filename)
first_hu = df.head(1000)
if 'text' not in df.columns:
st.error("The uploaded CSV must contain a 'text' column.")
return
with st.spinner("Generating predictions..."):
predictions = []
for text in first_hu['text']:
output = model(text)
if output[0]["score"]>=0.50 and output[0]['score']<=0.60 and output[0]['label']=="LABEL_1":
predictions.append('Neutral')
elif output[0]['score']>0.60 and output[0]['label']=="LABEL_1":
predictions.append('Positive')
else:
predictions.append('Negative')
sentiment_counts = Counter(predictions)
sentiment_df = pd.DataFrame.from_dict(sentiment_counts, orient='index', columns=['Count'])
st.bar_chart(sentiment_df)
st.success("Predictions generated successfully!")
except Exception as e:
st.error(f"An error occurred: {str(e)}")
if uploaded_file is not None:
get_predictions(uploaded_file)
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