Update app.py

app.py

@@ -1,18 +1,35 @@
-
 import streamlit as st
-import pickle
+from transformers import pipeline
+
+# Load the sentiment analysis model
+model_name = "poom-sci/WangchanBERTa-finetuned-sentiment"
+sentiment_analyzer = pipeline('sentiment-analysis', model=model_name)
+
+# Streamlit app
+st.title("Thai Sentiment Analysis App")
+
+# Input text
+text_input = st.text_area("Enter Thai text for sentiment analysis", "ขอความเห็นหน่อย... ")
 
-# Load the model
-with open('sentiment_pipeline.pkl', 'rb') as file:
-    loaded_model = pickle.load(file)
+# Button to trigger analysis
+if st.button("Analyze Sentiment"):
+    # Analyze sentiment using the model
+    results = sentiment_analyzer([text_input])
 
-# Streamlit app title
-st.title("Sentiment Analysis App")
+    # Extract sentiment and score
+    sentiment = results[0]['label']
+    score = results[0]['score']
+    
 
-# Text input for user to enter a sentence
-user_input = st.text_input("Enter a sentence:")
+    # Display result as progress bars
+    st.subheader("Sentiment Analysis Result:")
 
-# Predict sentiment when user inputs text
-if user_input:
-    prediction = loaded_model.predict([user_input])[0]
-    st.write("Prediction:", "Positive" if prediction == 1 else "Negative")
+    if sentiment == 'pos':
+        st.success(f"Positive Sentiment (Score: {score:.2f})")
+        st.progress(score)
+    elif sentiment == 'neg':
+        st.error(f"Negative Sentiment (Score: {score:.2f})")
+        st.progress(score)
+    else:
+        st.warning(f"Neutral Sentiment (Score: {score:.2f})")
+        st.progress(score)