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  1. app-4.py +31 -0
  2. requirements-4.txt +5 -0
app-4.py ADDED
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+ import streamlit as st
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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+
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+ # Load the tokenizer and model
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+ tokenizer = AutoTokenizer.from_pretrained("OatNapat/finetuned_yelp")
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+ model = AutoModelForSequenceClassification.from_pretrained("OatNapat/finetuned_yelp")
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+
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+ # Create a sentiment analysis pipeline with the explicit tokenizer
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+ nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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+
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+ st.title("Sentiment Analysis App")
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+ user_input = st.text_input("ป้อนประโยคเพื่อวิเคราะห์ความรู้สึก:")
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+ if user_input:
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+ result = nlp(user_input)
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+ sentiment_label = result[0]["label"]
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+ sentiment_score = result[0]["score"]
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+
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+ # Define explanations for sentiment labels
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+ sentiment_explanations = {
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+ "LABEL_0": "Very negative",
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+ "LABEL_1": "Negative",
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+ "LABEL_2": "Neutral",
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+ "LABEL_3": "Positive",
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+ "LABEL_4": "Very positive"
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+ }
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+
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+ # Get the explanation for the sentiment label
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+ sentiment_explanation = sentiment_explanations.get(sentiment_label, "Unknown")
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+
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+ st.write(f"Sentiment: {sentiment_explanation}")
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+ st.write(f"Confidence: {sentiment_score:.4f}")
requirements-4.txt ADDED
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+ streamlit
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+ torch
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+ transformers
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+ pandas
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+ altair<5