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| import streamlit as st | |
| import torch | |
| from transformers import AutoTokenizer | |
| from semviqa.ser.qatc_model import QATCForQuestionAnswering | |
| from semviqa.tvc.model import ClaimModelForClassification | |
| from semviqa.ser.ser_eval import extract_evidence_tfidf_qatc | |
| from semviqa.tvc.tvc_eval import classify_claim | |
| import time # Thêm thư viện time để đo thời gian inference | |
| # Load models with caching | |
| def load_model(model_name, model_class, is_bc=False): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = model_class.from_pretrained(model_name, num_labels=3 if not is_bc else 2) | |
| model.eval() | |
| return tokenizer, model | |
| # Set up page configuration | |
| st.set_page_config(page_title="SemViQA Demo", layout="wide") | |
| # Custom CSS: fixed header and tabs, dynamic height, result box formatting | |
| st.markdown( | |
| """ | |
| <style> | |
| html, body { | |
| height: 100%; | |
| margin: 0; | |
| overflow: hidden; | |
| } | |
| .main-container { | |
| height: calc(100vh - 55px); /* Browser height - 55px */ | |
| overflow-y: auto; | |
| padding: 20px; | |
| } | |
| .big-title { | |
| font-size: 36px; | |
| font-weight: bold; | |
| color: #4A90E2; | |
| text-align: center; | |
| margin-top: 20px; | |
| position: sticky; /* Pin the header */ | |
| top: 0; | |
| background-color: white; /* Ensure the header covers content when scrolling */ | |
| z-index: 100; /* Ensure it's above other content */ | |
| } | |
| .sub-title { | |
| font-size: 20px; | |
| color: #666; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| } | |
| .stButton>button { | |
| background-color: #4CAF50; | |
| color: white; | |
| font-size: 16px; | |
| width: 100%; | |
| border-radius: 8px; | |
| padding: 10px; | |
| } | |
| .stTextArea textarea { | |
| font-size: 16px; | |
| min-height: 120px; | |
| } | |
| .result-box { | |
| background-color: #f9f9f9; | |
| padding: 20px; | |
| border-radius: 10px; | |
| box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.1); | |
| margin-top: 20px; | |
| } | |
| .verdict { | |
| font-size: 24px; | |
| font-weight: bold; | |
| margin: 0; | |
| display: flex; | |
| align-items: center; | |
| } | |
| .verdict-icon { | |
| margin-right: 10px; | |
| } | |
| /* Fix the tabs at the top */ | |
| div[data-baseweb="tab-list"] { | |
| position: sticky; | |
| top: 55px; /* Height of the header */ | |
| background-color: white; | |
| z-index: 99; | |
| } | |
| .stSidebar .sidebar-content { | |
| background-color: #f0f2f6; | |
| padding: 20px; | |
| border-radius: 10px; | |
| } | |
| .stSidebar .st-expander { | |
| background-color: #ffffff; | |
| border-radius: 10px; | |
| padding: 10px; | |
| margin-bottom: 10px; | |
| } | |
| .stSidebar .stSlider { | |
| margin-bottom: 20px; | |
| } | |
| .stSidebar .stSelectbox { | |
| margin-bottom: 20px; | |
| } | |
| .stSidebar .stCheckbox { | |
| margin-bottom: 20px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Container for the whole content with dynamic height | |
| with st.container(): | |
| st.markdown("<p class='big-title'>SemViQA: Vietnamese Semantic QA for Fact Verification</p>", unsafe_allow_html=True) | |
| st.markdown("<p class='sub-title'>Enter the claim and context to verify its accuracy</p>", unsafe_allow_html=True) | |
| # Sidebar: Global Settings | |
| with st.sidebar.expander("⚙️ Settings", expanded=True): | |
| tfidf_threshold = st.slider("TF-IDF Threshold", 0.0, 1.0, 0.5, 0.01) | |
| length_ratio_threshold = st.slider("Length Ratio Threshold", 0.1, 1.0, 0.5, 0.01) | |
| qatc_model_name = st.selectbox("QATC Model", [ | |
| "SemViQA/qatc-infoxlm-viwikifc", | |
| "SemViQA/qatc-infoxlm-isedsc01", | |
| "SemViQA/qatc-vimrc-viwikifc", | |
| "SemViQA/qatc-vimrc-isedsc01" | |
| ]) | |
| bc_model_name = st.selectbox("Binary Classification Model", [ | |
| "SemViQA/bc-xlmr-viwikifc", | |
| "SemViQA/bc-xlmr-isedsc01", | |
| "SemViQA/bc-infoxlm-viwikifc", | |
| "SemViQA/bc-infoxlm-isedsc01", | |
| "SemViQA/bc-erniem-viwikifc", | |
| "SemViQA/bc-erniem-isedsc01" | |
| ]) | |
| tc_model_name = st.selectbox("3-Class Classification Model", [ | |
| "SemViQA/tc-xlmr-viwikifc", | |
| "SemViQA/tc-xlmr-isedsc01", | |
| "SemViQA/tc-infoxlm-viwikifc", | |
| "SemViQA/tc-infoxlm-isedsc01", | |
| "SemViQA/tc-erniem-viwikifc", | |
| "SemViQA/tc-erniem-isedsc01" | |
| ]) | |
| show_details = st.checkbox("Show Probability Details", value=False) | |
| # Store verification history | |
| if 'history' not in st.session_state: | |
| st.session_state.history = [] | |
| if 'latest_result' not in st.session_state: | |
| st.session_state.latest_result = None | |
| # Load the selected models | |
| tokenizer_qatc, model_qatc = load_model(qatc_model_name, QATCForQuestionAnswering) | |
| tokenizer_bc, model_bc = load_model(bc_model_name, ClaimModelForClassification, is_bc=True) | |
| tokenizer_tc, model_tc = load_model(tc_model_name, ClaimModelForClassification) | |
| # Icons for results | |
| verdict_icons = { | |
| "SUPPORTED": "✅", | |
| "REFUTED": "❌", | |
| "NEI": "⚠️" | |
| } | |
| # Tabs: Verify, History, About | |
| tabs = st.tabs(["Verify", "History", "About"]) | |
| # --- Tab Verify --- | |
| with tabs[0]: | |
| st.subheader("Verify a Claim") | |
| # 2-column layout: input on the left, results on the right | |
| col_input, col_result = st.columns([2, 1]) | |
| with col_input: | |
| 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.") | |
| verify_button = st.button("Verify", key="verify_button") | |
| with col_result: | |
| st.markdown("<h3>Verification Result</h3>", unsafe_allow_html=True) | |
| if verify_button: | |
| # Placeholder for displaying result/loading | |
| with st.spinner("Verifying..."): # Thêm spinner khi đang xử lý | |
| start_time = time.time() # Bắt đầu đo thời gian inference | |
| with torch.no_grad(): | |
| # Extract evidence | |
| evidence_start_time = time.time() | |
| evidence = extract_evidence_tfidf_qatc( | |
| claim, context, model_qatc, tokenizer_qatc, | |
| "cuda" if torch.cuda.is_available() else "cpu", | |
| confidence_threshold=tfidf_threshold, | |
| length_ratio_threshold=length_ratio_threshold | |
| ) | |
| evidence_time = time.time() - evidence_start_time | |
| # Hiển thị evidence trước | |
| st.markdown(f""" | |
| <div class='result-box'> | |
| <p><strong>Evidence:</strong> {evidence}</p> | |
| <p><strong>Evidence Inference Time:</strong> {evidence_time:.2f} seconds</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Classify the claim | |
| verdict_start_time = time.time() | |
| verdict = "NEI" | |
| details = "" | |
| prob3class, pred_tc = classify_claim( | |
| claim, evidence, model_tc, tokenizer_tc, | |
| "cuda" if torch.cuda.is_available() else "cpu" | |
| ) | |
| if pred_tc != 0: | |
| prob2class, pred_bc = classify_claim( | |
| claim, evidence, model_bc, tokenizer_bc, | |
| "cuda" if torch.cuda.is_available() else "cpu" | |
| ) | |
| if pred_bc == 0: | |
| verdict = "SUPPORTED" | |
| elif prob2class > prob3class: | |
| verdict = "REFUTED" | |
| else: | |
| verdict = ["NEI", "SUPPORTED", "REFUTED"][pred_tc] | |
| if show_details: | |
| details = f""" | |
| <p><strong>3-Class Probability:</strong> {prob3class.item():.2f}</p> | |
| <p><strong>3-Class Predicted Label:</strong> {['NEI', 'SUPPORTED', 'REFUTED'][pred_tc]}</p> | |
| <p><strong>2-Class Probability:</strong> {prob2class.item():.2f}</p> | |
| <p><strong>2-Class Predicted Label:</strong> {['SUPPORTED', 'REFUTED'][pred_bc]}</p> | |
| """ | |
| verdict_time = time.time() - verdict_start_time | |
| # Store verification history and the latest result | |
| st.session_state.history.append({ | |
| "claim": claim, | |
| "evidence": evidence, | |
| "verdict": verdict, | |
| "evidence_time": evidence_time, | |
| "verdict_time": verdict_time, | |
| "details": details | |
| }) | |
| st.session_state.latest_result = { | |
| "claim": claim, | |
| "evidence": evidence, | |
| "verdict": verdict, | |
| "evidence_time": evidence_time, | |
| "verdict_time": verdict_time, | |
| "details": details | |
| } | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| # Display the result after verification | |
| res = st.session_state.latest_result | |
| st.markdown(f""" | |
| <div class='result-box'> | |
| <p><strong>Claim:</strong> {res['claim']}</p> | |
| <p><strong>Evidence:</strong> {res['evidence']}</p> | |
| <p><strong>Evidence Inference Time:</strong> {res['evidence_time']:.2f} seconds</p> | |
| <p><strong>Verdict Inference Time:</strong> {res['verdict_time']:.2f} seconds</p> | |
| <p class='verdict'><span class='verdict-icon'>{verdict_icons.get(res['verdict'], '')}</span>{res['verdict']}</p> | |
| {res['details']} | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Download Verification Result Feature | |
| result_text = f"Claim: {res['claim']}\nEvidence: {res['evidence']}\nVerdict: {res['verdict']}\nDetails: {res['details']}" | |
| st.download_button("Download Result", data=result_text, file_name="verification_result.txt", mime="text/plain") | |
| else: | |
| st.info("No verification result yet.") | |
| # --- Tab History --- | |
| with tabs[1]: | |
| st.subheader("Verification History") | |
| if st.session_state.history: | |
| for idx, record in enumerate(reversed(st.session_state.history), 1): | |
| st.markdown(f"**{idx}. Claim:** {record['claim']} \n**Result:** {verdict_icons.get(record['verdict'], '')} {record['verdict']}") | |
| else: | |
| st.write("No verification history yet.") | |
| # --- Tab About --- | |
| with tabs[2]: | |
| st.subheader("About") | |
| st.markdown(""" | |
| <p align="center"> | |
| <a href="https://arxiv.org/abs/2503.00955"> | |
| <img src="https://img.shields.io/badge/arXiv-2411.00918-red?style=flat&label=arXiv"> | |
| </a> | |
| <a href="https://huggingface.co/SemViQA"> | |
| <img src="https://img.shields.io/badge/Hugging%20Face-Model-yellow?style=flat"> | |
| </a> | |
| <a href="https://pypi.org/project/SemViQA"> | |
| <img src="https://img.shields.io/pypi/v/SemViQA?color=blue&label=PyPI"> | |
| </a> | |
| <a href="https://github.com/DAVID-NGUYEN-S16/SemViQA"> | |
| <img src="https://img.shields.io/github/stars/DAVID-NGUYEN-S16/SemViQA?style=social"> | |
| </a> | |
| </p> | |
| """, unsafe_allow_html=True) | |
| st.markdown(""" | |
| **Description:** | |
| SemViQA is a Semantic QA system designed for fact verification in Vietnamese. | |
| The system extracts evidence from the provided context and classifies claims as **SUPPORTED**, **REFUTED**, or **NEI** (Not Enough Information) using advanced models. | |
| """) |