Delete app.py

import streamlit as st
import pandas as pd
import plotly.express as px
from transformers import pipeline

def main():
st.set_page_config(page_title="Sentiment Analysis App")
st.sidebar.title("Sentiment Analysis App")
st.sidebar.write("Choose a pre-trained model for sentiment analysis")
model_name = st.sidebar.selectbox("Select Model", ["bert-base-uncased", "distilbert-base-uncased", "roberta-base"])
text_input = st.text_input("Enter text for sentiment analysis")

if st.button("Analyze"):
if text_input:
sentiment_classifier = pipeline("sentiment-analysis", model=model_name)
result = sentiment_classifier(text_input)[0]
st.write(f"Sentiment: {result['label']}")
st.write(f"Score: {round(result['score'], 4)}")

# create a dataframe to hold the sentiment distribution
df_sentiments = pd.DataFrame({"sentiment": ["Positive", "Negative", "Neutral"], "count": [0, 0, 0]})

# get the sentiment distribution of the text using the selected model
results = sentiment_classifier(text_input, task="sentiment-analysis")
for r in results:
if r["label"] == "POSITIVE":
df_sentiments.loc[0, "count"] = r["score"]
elif r["label"] == "NEGATIVE":
df_sentiments.loc[1, "count"] = r["score"]
elif r["label"] == "NEUTRAL":
df_sentiments.loc[2, "count"] = r["score"]

# plot the sentiment distribution as a pie chart
fig = px.pie(df_sentiments, values='count', names='sentiment', hole=.4)
st.plotly_chart(fig)

else:
st.warning("Please enter some text for sentiment analysis")

if __name__ == "__main__":
main()

app.py

@@ -1,22 +0,0 @@
-import streamlit as st
-import pandas as pd
-from transformers import pipeline
-
-def main():
-    st.set_page_config(page_title="Sentiment Analysis App")
-    st.sidebar.title("Sentiment Analysis App")
-    st.sidebar.write("Choose a pre-trained model for sentiment analysis")
-    model_name = st.sidebar.selectbox("Select Model", ["bert-base-uncased", "distilbert-base-uncased", "roberta-base"])
-    text_input = st.text_input("Enter text for sentiment analysis")
-
-    if st.button("Analyze"):
-        if text_input:
-            sentiment_classifier = pipeline("sentiment-analysis", model=model_name)
-            result = sentiment_classifier(text_input)[0]
-            st.write(f"Sentiment: {result['label']}")
-            st.write(f"Score: {round(result['score'], 4)}")
-        else:
-            st.warning("Please enter some text for sentiment analysis")
-
-if __name__ == "__main__":
-    main()