richardorama
commited on
Commit
•
2c5fe61
1
Parent(s):
5402860
Update app.py
Browse files
app.py
CHANGED
@@ -55,26 +55,34 @@ DEFAULT_SENTIMENT = "I'm so happy today!"
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# Create a text area for user input
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SENTIMENT = st.sidebar.text_area('Enter Sentiment', DEFAULT_SENTIMENT, height=150)
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# Define the summarization function
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def summarize(txt):
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txt_converted = ast.literal_eval(txt) #convert string to actual content, e.g. list
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-
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st.write('\n\n')
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#st.write(txt[:100]) # Display the first 100 characters of the article
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st.write('--------------------------------------------------------------')
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-
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# Perform Hugging sentiment analysis on multiple texts
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results = sentiment_pipeline(txt_converted)
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# Display the results
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#if type(txt_converted) == 'list':
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if isinstance(txt_converted, list):
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for i, text in enumerate(txt_converted):
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st.write(f"Text: {text}")
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st.write(f"Sentiment: {results[i]['label']}, Score: {results[i]['score']:.2f}\n")
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else:
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-
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st.write(f"Sentiment: {results[0]['label']}, Score: {results[0]['score']:.2f}\n")
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# Create a button and trigger the summarize function when clicked
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# Create a text area for user input
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SENTIMENT = st.sidebar.text_area('Enter Sentiment', DEFAULT_SENTIMENT, height=150)
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def is_valid_list_string(string):
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try:
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result = ast.literal_eval(string)
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return isinstance(result, list)
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except (ValueError, SyntaxError):
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return False
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# Define the summarization function
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def summarize(txt):
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st.write('\n\n')
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#st.write(txt[:100]) # Display the first 100 characters of the article
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st.write('--------------------------------------------------------------')
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# Display the results
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#if type(txt_converted) == 'list':
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#if isinstance(txt_converted, list):
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if is_valid_list_string(txt):
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txt_converted = ast.literal_eval(txt) #convert string to actual content, e.g. list
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# Perform Hugging sentiment analysis on multiple texts
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results = sentiment_pipeline(txt_converted)
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for i, text in enumerate(txt_converted):
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st.write(f"Text: {text}")
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st.write(f"Sentiment: {results[i]['label']}, Score: {results[i]['score']:.2f}\n")
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else:
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# Perform Hugging sentiment analysis on multiple texts
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results = sentiment_pipeline(txt)
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st.write(f"Text: {txt}")
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st.write(f"Sentiment: {results[0]['label']}, Score: {results[0]['score']:.2f}\n")
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# Create a button and trigger the summarize function when clicked
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