Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoModelForSequenceClassification
+import torch
+
+st.set_page_config(page_title="ABSA App", layout="wide")
+st.title("Aspect-Based Sentiment Analysis (E2E-ABSA)")
+
+@st.cache_resource
+def load_models():
+    ner_tokenizer = AutoTokenizer.from_pretrained("hai2131/abte-bert")
+    ner_model = AutoModelForTokenClassification.from_pretrained("hai2131/abte-bert")
+
+    cls_tokenizer = AutoTokenizer.from_pretrained("hai2131/absa-bert")
+    cls_model = AutoModelForSequenceClassification.from_pretrained("hai2131/absa-bert")
+
+    return ner_tokenizer, ner_model, cls_tokenizer, cls_model
+
+ner_tokenizer, ner_model, cls_tokenizer, cls_model = load_models()
+id2label = ner_model.config.id2label
+label2sentiment = {0: "negative", 1: "neutral", 2: "positive"}
+
+def extract_aspect_terms(text):
+    inputs = ner_tokenizer(text, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        outputs = ner_model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=2)[0]
+    tokens = ner_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+    labels = [id2label[i.item()] for i in predictions]
+
+    aspects = []
+    current = ""
+    for token, label in zip(tokens, labels):
+        if label.startswith("B-"):
+            if current:
+                aspects.append(current)
+            current = token.replace("##", "")
+        elif label.startswith("I-") and current:
+            current += token.replace("##", "") if token.startswith("##") else " " + token
+        else:
+            if current:
+                aspects.append(current)
+                current = ""
+    if current:
+        aspects.append(current)
+    return list(set(aspects))
+
+def classify_polarity(text, aspect):
+    inputs = cls_tokenizer(text, aspect, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        logits = cls_model(**inputs).logits
+    prediction = torch.argmax(logits, dim=1).item()
+    return label2sentiment[prediction]
+
+text = st.text_area("✍️ Nhập một câu tiếng Anh để phân tích:", 
+                    "The food was amazing, but the service was terrible.")
+
+if st.button("🚀 Phân tích"):
+    with st.spinner("Đang phân tích..."):
+        aspects = extract_aspect_terms(text)
+        results = [(asp, classify_polarity(text, asp)) for asp in aspects]
+
+    if results:
+        st.markdown("## 🎯 Kết quả:")
+        for asp, polarity in results:
+            emoji = {"positive": "✅", "negative": "❌", "neutral": "😐"}.get(polarity, "🔹")
+            st.markdown(f"- {emoji} **{asp}** — *{polarity}*")
+    else:
+        st.warning("⚠️ Không tìm thấy khía cạnh nào.")