Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Load pipelines
+pipe1 = pipeline("translation", model="DunnBC22/opus-mt-zh-en-Chinese_to_English")
+pipe3 = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")
+
+# Load model and tokenizer for pipe2
+tokenizer = AutoTokenizer.from_pretrained("huimanho/CustomModel_Amazon")
+model = AutoModelForSequenceClassification.from_pretrained("huimanho/CustomModel_Amazon")
+
+# Streamlit app
+st.title("Chinese Review Analysis")
+
+# Input text
+chinese_text = st.text_area("Enter Chinese Review:")
+
+if st.button("Analyze"):
+    # Translation
+    english_text = pipe1(chinese_text)[0]['translation_text']
+    st.write("Translated Text:", english_text)
+
+    # Rating Prediction
+    inputs = tokenizer(english_text, return_tensors="pt")
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+    st.write("Estimated Amazon Rating:", prediction + 1)
+
+    # Sentiment Classification
+    sentiment = pipe3(english_text)[0]['label']
+    st.write("Sentiment:", sentiment)