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app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer
+from semviqa.SER.qatc_model import QATCForQuestionAnswering
+
+# Load QATC Model
+@st.cache_resource()
+def load_qatc_model():
+    tokenizer = AutoTokenizer.from_pretrained("xuandin/semviqa-qatc-vimrc-viwikifc")
+    model = QATCForQuestionAnswering.from_pretrained("xuandin/semviqa-qatc-vimrc-viwikifc")
+    return tokenizer, model
+
+# Streamlit UI Configuration
+st.set_page_config(page_title="SemViQA Demo", layout="wide")
+
+# Improved UI Design
+st.markdown("""
+    <style>
+        .big-title {
+            font-size: 36px;
+            font-weight: bold;
+            color: #4A90E2;
+            text-align: center;
+        }
+        .sub-title {
+            font-size: 20px;
+            color: #666;
+            text-align: center;
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            font-size: 16px;
+            width: 100%;
+            border-radius: 8px;
+            padding: 10px;
+        }
+        .stTextArea textarea {
+            font-size: 16px;
+        }
+        .result-box {
+            background-color: #f9f9f9;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1);
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown("<p class='big-title'>🔍 SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking</p>", unsafe_allow_html=True)
+st.markdown("<p class='sub-title'>Enter a claim and context to verify its accuracy</p>", unsafe_allow_html=True)
+
+# Sidebar - Configuration Settings
+st.sidebar.header("⚙️ Settings")
+tfidf_threshold = st.sidebar.slider("🔧 TF-IDF Threshold", 0.0, 1.0, 0.5, 0.01)
+length_ratio_threshold = st.sidebar.slider("📏 Length Ratio Threshold", 0.1, 1.0, 0.5, 0.01)
+qatc_model = st.sidebar.selectbox("🤖 Select QATC Model", ["xuandin/semviqa-qatc-vimrc-viwikifc"])
+
+# User Input Fields
+claim = st.text_area("✍️ Enter Claim", "Vietnam is a country in Southeast Asia.")
+context = st.text_area("📖 Enter Context", "Vietnam is a country located in Southeast Asia, covering an area of over 331,000 km² with a population of more than 98 million people.")
+
+if st.button("🔎 Verify"):
+    tokenizer, model = load_qatc_model()
+    inputs = tokenizer(claim, context, return_tensors="pt", truncation=True, max_length=512)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    start_idx = torch.argmax(outputs.start_logits)
+    end_idx = torch.argmax(outputs.end_logits)
+    tokens = inputs["input_ids"][0][start_idx : end_idx + 1]
+    evidence_result = tokenizer.decode(tokens, skip_special_tokens=True)
+    
+    st.markdown("""
+        <div class='result-box'>
+            <h3>📌 Result</h3>
+            <p><strong>🔍 Evidence:</strong> {}</p>
+        </div>
+    """.format(evidence_result), unsafe_allow_html=True)

requirements.txt

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+streamlit
+torch
+transformers
+semviqa