Update app.py

app.py

@@ -1,48 +1,26 @@
-# import sentencepiece
-
-
-# import streamlit as st
-# from transformers import pipeline
-
-# sentiment_analysis = pipeline("sentiment-analysis")
-# translation = pipeline("translation_en_to_ar", model="anibahug/marian-finetuned-kde4-en-to-ar")
-
-# text = st.text_input("Enter some text")
-
-# if text:
-#     result = sentiment_analysis(text)
-#     st.json(result)
-
-# if text:
-#     result = translation(text)[0]
-#     st.write(f"Translated text: {result['translation_text']}")
-
-
-
-
-
 import streamlit as st
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline
 
-# model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-# model = AutoModelForSequenceClassification.from_pretrained(model_name)
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# , tokenizer=tokenizer
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+st.title("Sentiment Analysis And Translation App")
+text = st.text_input("Enter a sentence:")
 
-# Streamlit UI
-st.title("Sentiment Analysis App")
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 
-# User input
-user_input = st.text_input("Enter a sentence:")
+translation = pipeline("translation_en_to_ar", model="anibahug/marian-finetuned-kde4-en-to-ar")
 
-if user_input:
-    # Perform sentiment analysis
-    results = sentiment_analyzer(user_input)
+if text:
+    results = sentiment_analyzer(text)
     
-    # Display sentiment and confidence
     sentiment = results[0]['label']
     confidence = results[0]['score']
     
     st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")
+    st.write(f"Confidence: {confidence:.2f}")
+
+# if text:
+#     result = sentiment_analysis(text)
+#     st.json(result)
+
+if text:
+    result = translation(text)[0]
+    st.write(f"Translated text: {result['translation_text']}")