Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+import torch
+import torch.nn.functional as F
+
+@st.cache_resource
+def load_model():
+    absa_tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
+    absa_model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
+    token_classifier = pipeline(
+        model="thainq107/abte-restaurants-distilbert-base-uncased",
+        aggregation_strategy="simple"
+    )
+    return absa_model, absa_tokenizer, token_classifier
+
+absa_model, absa_tokenizer, token_classifier = load_model()
+
+def inference(review):
+    aspects = token_classifier(review)
+    aspects = [aspect['word'] for aspect in aspects]
+    results = {}
+    for aspect in aspects:
+        # Check if the aspect is mentioned in the review
+        if aspect.lower() in review.lower():
+            inputs = absa_tokenizer(f"[CLS] {review} [SEP] {aspect} [SEP]", return_tensors="pt")
+            outputs = absa_model(**inputs)
+            probs = F.softmax(outputs.logits, dim=1)
+            probs = probs.detach().numpy()[0]
+
+            # Extract the label with the highest probability
+            max_label, max_prob = max(zip(["negative", "neutral", "positive"], probs), key=lambda x: x[1])
+
+            results[aspect] = max_label
+
+    return results
+
+st.title("ABSA - Aspect-Based Sentiment Analysis")
+
+text = st.text_area("Nhập câu cần phân tích:", "The battery life is great, but the screen is dim.")
+
+if st.button("Phân tích cảm xúc"):
+    results = inference(text)
+    for aspect, label in results.items():
+        st.markdown(f"{aspect} ➝ {label}")