Create app.py

app.py

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+import streamlit as st
+import torch
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModel, pipeline
+import faiss
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+DATASET_NAME = 'squad_v2'
+raw_datasets = load_dataset(DATASET_NAME, split="train+validation").shard(num_shards=40, index=0)
+
+raw_datasets = raw_datasets.filter(lambda x: len(x["answers"]["text"]) > 0)
+
+columns_to_keep = ['id', 'context', 'question', 'answers']
+raw_datasets = raw_datasets.remove_columns(set(raw_datasets.column_names) - set(columns_to_keep))
+
+MODEL_NAME = "distilbert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModel.from_pretrained(MODEL_NAME).to(device)
+
+def get_embeddings(text_list):
+    encoded_input = tokenizer(text_list, padding=True, truncation=True, return_tensors="pt")
+    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+    model_output = model(**encoded_input)
+    return model_output.last_hidden_state[:, 0]
+
+EMBEDDING_COLUMN = "question_embedding"
+embedding_dataset = raw_datasets.map(
+    lambda x: {EMBEDDING_COLUMN: get_embeddings(x["question"]).detach().cpu().numpy()[0]}
+)
+embedding_dataset.add_faiss_index(column=EMBEDDING_COLUMN)
+
+PIPELINE_NAME = "question-answering"
+QA_MODEL_NAME = "DoNotChoke/distilbert-finetuned-squadv2"
+qa_pipeline = pipeline(PIPELINE_NAME, model=QA_MODEL_NAME)
+
+st.title("Question Answering System")
+st.write("Nhập câu hỏi của bạn và hệ thống sẽ tìm kiếm câu trả lời phù hợp.")
+
+user_question = st.text_input("Nhập câu hỏi:", "When did Beyonce start becoming popular?")
+
+if st.button("Tìm kiếm câu trả lời"):
+    if user_question:
+        input_question_embedding = get_embeddings([user_question]).cpu().detach().numpy()
+        TOP_K = 5
+        scores, samples = embedding_dataset.get_nearest_examples(EMBEDDING_COLUMN, input_question_embedding, k=TOP_K)
+
+        st.subheader("Kết quả tìm kiếm:")
+        for idx, score in enumerate(scores):
+            context = samples["context"][idx]
+            answer = qa_pipeline(question=user_question, context=context)
+
+            st.write(f"**Top {idx + 1} (Score: {score:.4f})**")
+            st.write(f"**Context:** {context}")
+            st.write(f"**Answer:** {answer['answer']}")
+            st.write("---")