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55c11e6
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Parent(s):
aa752b7
Update app.py
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app.py
CHANGED
@@ -142,7 +142,6 @@
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import streamlit as st
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import transformers
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import torch
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# Load the model and tokenizer
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model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
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@@ -158,7 +157,10 @@ def predict_sentiment(text):
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sentiment = prediction["label"]
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score = prediction["score"]
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# Setting the page configurations
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st.set_page_config(
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@@ -182,14 +184,13 @@ predict_button = st.button("Predict")
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# Show sentiment output
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if predict_button and text:
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sentiment,
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st.write(f"The sentiment is {sentiment} with a score of {
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# Display individual percentages
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st.write("Sentiment Breakdown:")
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st.write(f"- Neutral: {score[2]*100:.2f}%")
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# Define the CSS style for the app
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st.markdown(
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@@ -206,3 +207,4 @@ h1 {
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""",
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unsafe_allow_html=True
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)
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import streamlit as st
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import transformers
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# Load the model and tokenizer
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model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
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sentiment = prediction["label"]
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score = prediction["score"]
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# Convert scores to a dictionary with labels as keys and percentages as values
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scores_dict = {label: score * 100 for label, score in zip(prediction["labels"], prediction["scores"])}
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return sentiment, scores_dict
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# Setting the page configurations
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st.set_page_config(
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# Show sentiment output
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if predict_button and text:
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sentiment, scores_dict = predict_sentiment(text)
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st.write(f"The sentiment is {sentiment} with a score of {scores_dict[sentiment]:.2f}% for each category.")
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# Display individual percentages
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st.write("Sentiment Breakdown:")
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for label, score in scores_dict.items():
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st.write(f"- {label}: {score:.2f}%")
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# Define the CSS style for the app
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st.markdown(
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""",
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unsafe_allow_html=True
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)
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