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app-4.py

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

requirements-4.txt

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+streamlit
+torch
+transformers
+pandas
+altair<5