Update app.py

app.py

@@ -10,18 +10,30 @@ import pandas as pd
 import os
 import psutil
 import gc
-import plotly.express as px
-import plotly.graph_objects as go
-from datetime import datetime
+import numpy as np
+from functools import lru_cache
+import threading
+from concurrent.futures import ThreadPoolExecutor
+import torch.nn.functional as F
 
 # Set environment variables to optimize CPU performance
 os.environ["OMP_NUM_THREADS"] = str(psutil.cpu_count(logical=False))
 os.environ["MKL_NUM_THREADS"] = str(psutil.cpu_count(logical=False))
+torch.set_num_threads(psutil.cpu_count(logical=False))
 
 # Set device globally
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
-@st.cache_resource()
+# Cache for model outputs
+@lru_cache(maxsize=1000)
+def cached_classify_claim(claim, evidence, model_name, is_bc=False):
+    tokenizer, model = load_model(model_name, ClaimModelForClassification, is_bc=is_bc, device=DEVICE)
+    with torch.no_grad():
+        prob, pred = classify_claim(claim, evidence, model, tokenizer, DEVICE)
+    return prob, pred
+
+# Optimized model loading with caching
+@st.cache_resource(ttl=3600)  # Cache for 1 hour
 def load_model(model_name, model_class, is_bc=False, device=None):
     if device is None:
         device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -30,18 +42,44 @@ def load_model(model_name, model_class, is_bc=False, device=None):
     model = model_class.from_pretrained(model_name, num_labels=3 if not is_bc else 2)
     model.eval()
     
+    # Optimize model for inference
+    if device == "cuda":
+        model = model.half()  # Use FP16 for faster inference
+        torch.cuda.empty_cache()
+    
     model.to(device)
     return tokenizer, model
 
-@st.cache_data
+# Optimized text preprocessing
+@st.cache_data(ttl=3600)
 def preprocess_text(text):
     # Add any text cleaning or normalization here
     return text.strip()
 
-# Optimized function for evidence extraction and classification with better CPU performance
+# Batch processing for evidence extraction
+def batch_extract_evidence(claims, contexts, model_qatc, tokenizer_qatc, batch_size=4):
+    results = []
+    for i in range(0, len(claims), batch_size):
+        batch_claims = claims[i:i + batch_size]
+        batch_contexts = contexts[i:i + batch_size]
+        
+        with torch.no_grad():
+            batch_results = [
+                extract_evidence_tfidf_qatc(
+                    claim, context, model_qatc, tokenizer_qatc,
+                    DEVICE,
+                    confidence_threshold=0.5,
+                    length_ratio_threshold=0.5
+                )
+                for claim, context in zip(batch_claims, batch_contexts)
+            ]
+        results.extend(batch_results)
+    return results
+
+# Optimized verification function with parallel processing
 def perform_verification(claim, context, model_qatc, tokenizer_qatc, model_tc, tokenizer_tc, 
-                          model_bc, tokenizer_bc, tfidf_threshold, length_ratio_threshold):
-    # Extract evidence
+                        model_bc, tokenizer_bc, tfidf_threshold, length_ratio_threshold):
+    # Extract evidence with optimized settings
     evidence_start_time = time.time()
     evidence = extract_evidence_tfidf_qatc(
         claim, context, model_qatc, tokenizer_qatc,
@@ -51,25 +89,25 @@ def perform_verification(claim, context, model_qatc, tokenizer_qatc, model_tc, t
     )
     evidence_time = time.time() - evidence_start_time
     
-    # Explicit garbage collection after evidence extraction
+    # Clear memory after evidence extraction
+    if DEVICE == "cuda":
+        torch.cuda.empty_cache()
     gc.collect()
     
-    # Classify the claim
+    # Parallel classification using ThreadPoolExecutor
+    with ThreadPoolExecutor(max_workers=2) as executor:
+        future_tc = executor.submit(cached_classify_claim, claim, evidence, tc_model_name, False)
+        future_bc = executor.submit(cached_classify_claim, claim, evidence, bc_model_name, True)
+        
+        prob3class, pred_tc = future_tc.result()
+        prob2class, pred_bc = future_bc.result()
+    
     verdict_start_time = time.time()
     with torch.no_grad():
         verdict = "NEI"
-        prob3class, pred_tc = classify_claim(
-            claim, evidence, model_tc, tokenizer_tc, DEVICE
-        )
-        
-        # Only run binary classifier if needed
-        prob2class, pred_bc = 0, 0
         if pred_tc != 0:
-            prob2class, pred_bc = classify_claim(
-                claim, evidence, model_bc, tokenizer_bc, DEVICE
-            )
             verdict = "SUPPORTED" if pred_bc == 0 else "REFUTED" if prob2class > prob3class else ["NEI", "SUPPORTED", "REFUTED"][pred_tc]
-            
+    
     verdict_time = time.time() - verdict_start_time
 
     return {
@@ -83,69 +121,19 @@ def perform_verification(claim, context, model_qatc, tokenizer_qatc, model_tc, t
         "pred_bc": pred_bc
     }
 
-# Add new functions for analysis
-def analyze_verdict_distribution(history):
-    if not history:
-        return None
-    
-    df = pd.DataFrame(history)
-    verdict_counts = df['verdict'].value_counts()
-    
-    fig = px.pie(
-        values=verdict_counts.values,
-        names=verdict_counts.index,
-        title='Phân bố Kết quả Kiểm chứng',
-        color_discrete_sequence=['#2ecc71', '#e74c3c', '#f1c40f']
-    )
-    return fig
-
-def analyze_processing_time(history):
-    if not history:
-        return None
-    
-    df = pd.DataFrame(history)
-    df['timestamp'] = pd.to_datetime(df['timestamp'])
-    
-    fig = px.line(
-        df,
-        x='timestamp',
-        y=['evidence_time', 'verdict_time', 'total_time'],
-        title='Thời gian Xử lý theo Thời gian',
-        labels={'value': 'Thời gian (giây)', 'timestamp': 'Thời điểm'}
-    )
-    return fig
-
-def generate_report(result):
-    report = f"""
-    BÁO CÁO KIỂM CHỨNG THÔNG TIN
-    Thời gian: {datetime.now().strftime('%d/%m/%Y %H:%M:%S')}
-    
-    1. THÔNG TIN CƠ BẢN
-    -------------------
-    Câu khẳng định: {result['claim']}
-    Kết luận: {result['verdict']}
-    
-    2. BẰNG CHỨNG
-    -------------
-    {result['evidence']}
-    
-    3. THỐNG KÊ THỜI GIAN
-    ---------------------
-    - Thời gian trích xuất bằng chứng: {result['evidence_time']:.2f} giây
-    - Thời gian phân loại: {result['verdict_time']:.2f} giây
-    - Tổng thời gian xử lý: {result['total_time']:.2f} giây
-    
-    4. CHI TIẾT KỸ THUẬT
-    -------------------
-    {result['details']}
-    
-    5. MÔ HÌNH SỬ DỤNG
-    ------------------
-    - QATC Model: {result['qatc_model']}
-    - Binary Classification Model: {result['bc_model']}
-    - 3-Class Classification Model: {result['tc_model']}
-    """
-    return report
+# Add performance monitoring
+def monitor_performance():
+    if DEVICE == "cuda":
+        return {
+            "gpu_memory_used": torch.cuda.memory_allocated() / 1024**2,
+            "gpu_memory_cached": torch.cuda.memory_reserved() / 1024**2,
+            "cpu_percent": psutil.cpu_percent(),
+            "memory_percent": psutil.virtual_memory().percent
+        }
+    return {
+        "cpu_percent": psutil.cpu_percent(),
+        "memory_percent": psutil.virtual_memory().percent
+    }
 
 # Set page configuration
 st.set_page_config(
@@ -160,295 +148,195 @@ st.markdown("""
     <style>
     /* Main theme colors */
     :root {
-        --primary-color: #2c3e50;
-        --secondary-color: #3498db;
+        --primary-color: #1f77b4;
+        --secondary-color: #2c3e50;
         --accent-color: #e74c3c;
-        --success-color: #2ecc71;
-        --warning-color: #f1c40f;
         --background-color: #f8f9fa;
         --text-color: #2c3e50;
         --border-color: #e0e0e0;
-        --gradient-start: #2c3e50;
-        --gradient-end: #3498db;
     }
     
     /* General styling */
     .stApp {
         background-color: var(--background-color);
         color: var(--text-color);
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
     }
     
     /* Header styling */
     .main-header {
-        background: linear-gradient(135deg, var(--gradient-start), var(--gradient-end));
+        background: linear-gradient(135deg, var(--primary-color), var(--secondary-color));
         color: white;
-        padding: 3rem 2rem;
-        border-radius: 15px;
+        padding: 2rem;
+        border-radius: 10px;
         margin-bottom: 2rem;
-        box-shadow: 0 10px 20px rgba(0, 0, 0, 0.1);
-        position: relative;
-        overflow: hidden;
-    }
-    
-    .main-header::before {
-        content: '';
-        position: absolute;
-        top: 0;
-        left: 0;
-        right: 0;
-        bottom: 0;
-        background: url('data:image/svg+xml,<svg width="20" height="20" viewBox="0 0 20 20" xmlns="http://www.w3.org/2000/svg"><rect width="1" height="1" fill="rgba(255,255,255,0.05)"/></svg>');
-        opacity: 0.1;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
     }
     
     .main-title {
-        font-size: 3rem;
-        font-weight: 800;
+        font-size: 2.5rem;
+        font-weight: bold;
         text-align: center;
         margin-bottom: 1rem;
-        text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2);
-        letter-spacing: 1px;
     }
     
     .sub-title {
-        font-size: 1.4rem;
+        font-size: 1.2rem;
         text-align: center;
         opacity: 0.9;
-        font-weight: 300;
     }
     
     /* Input styling */
     .stTextArea textarea {
         border: 2px solid var(--border-color);
-        border-radius: 12px;
-        padding: 1.2rem;
-        font-size: 1.1rem;
+        border-radius: 8px;
+        padding: 1rem;
+        font-size: 1rem;
         min-height: 150px;
         background-color: white;
-        transition: all 0.3s ease;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-    }
-    
-    .stTextArea textarea:focus {
-        border-color: var(--secondary-color);
-        box-shadow: 0 4px 8px rgba(52, 152, 219, 0.1);
     }
     
     /* Button styling */
     .stButton>button {
-        background: linear-gradient(135deg, var(--secondary-color), var(--primary-color));
+        background: linear-gradient(135deg, var(--primary-color), var(--secondary-color));
         color: white;
         border: none;
-        border-radius: 12px;
-        padding: 1rem 2.5rem;
-        font-size: 1.2rem;
-        font-weight: 600;
+        border-radius: 8px;
+        padding: 0.8rem 2rem;
+        font-size: 1.1rem;
+        font-weight: bold;
         transition: all 0.3s ease;
-        text-transform: uppercase;
-        letter-spacing: 1px;
     }
     
     .stButton>button:hover {
         transform: translateY(-2px);
-        box-shadow: 0 8px 16px rgba(52, 152, 219, 0.2);
+        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
     }
     
     /* Result box styling */
     .result-box {
         background-color: white;
-        border-radius: 15px;
-        padding: 2.5rem;
-        margin: 1.5rem 0;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
-        transition: all 0.3s ease;
-        border: 1px solid rgba(0, 0, 0, 0.05);
+        border-radius: 12px;
+        padding: 2rem;
+        margin: 1rem 0;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
     }
     
-    .result-box:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 12px 24px rgba(0, 0, 0, 0.15);
+    /* Info section styling */
+    .info-section {
+        background-color: white;
+        border-radius: 12px;
+        padding: 2rem;
+        margin: 1rem 0;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+    }
+    
+    .info-section h3 {
+        color: var(--primary-color);
+        font-size: 1.8rem;
+        margin-bottom: 1.5rem;
+        border-bottom: 2px solid var(--border-color);
+        padding-bottom: 0.5rem;
+    }
+    
+    .info-section h4 {
+        color: var(--secondary-color);
+        font-size: 1.4rem;
+        margin: 1.5rem 0 1rem 0;
+    }
+    
+    .info-section p {
+        font-size: 1.1rem;
+        line-height: 1.6;
+        color: var(--text-color);
+        margin-bottom: 1.5rem;
+    }
+    
+    .info-section ol, .info-section ul {
+        margin-left: 1.5rem;
+        margin-bottom: 1.5rem;
+    }
+    
+    .info-section li {
+        font-size: 1.1rem;
+        line-height: 1.6;
+        margin-bottom: 0.5rem;
+    }
+    
+    .info-section strong {
+        color: var(--primary-color);
     }
     
     .verdict {
-        font-size: 2rem;
-        font-weight: 800;
-        padding: 1.5rem;
-        border-radius: 12px;
-        margin: 1.5rem 0;
+        font-size: 1.8rem;
+        font-weight: bold;
+        padding: 1rem;
+        border-radius: 8px;
+        margin: 1rem 0;
         text-align: center;
-        transition: all 0.3s ease;
     }
     
     .verdict-supported {
-        background: linear-gradient(135deg, #2ecc71, #27ae60);
-        color: white;
-        box-shadow: 0 4px 8px rgba(46, 204, 113, 0.2);
+        background-color: #d4edda;
+        color: #155724;
     }
     
     .verdict-refuted {
-        background: linear-gradient(135deg, #e74c3c, #c0392b);
-        color: white;
-        box-shadow: 0 4px 8px rgba(231, 76, 60, 0.2);
+        background-color: #f8d7da;
+        color: #721c24;
     }
     
     .verdict-nei {
-        background: linear-gradient(135deg, #f1c40f, #f39c12);
-        color: white;
-        box-shadow: 0 4px 8px rgba(241, 196, 15, 0.2);
+        background-color: #fff3cd;
+        color: #856404;
     }
     
     /* Sidebar styling */
     .css-1d391kg {
         background-color: white;
-        padding: 2.5rem;
-        border-radius: 15px;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
+        padding: 2rem;
+        border-radius: 12px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
     }
     
     /* Stats box styling */
     .stats-box {
-        background: linear-gradient(135deg, #f8f9fa, #e9ecef);
-        border-radius: 12px;
-        padding: 1.5rem;
-        margin: 1rem 0;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05);
+        background-color: white;
+        border-radius: 8px;
+        padding: 1rem;
+        margin: 0.5rem 0;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
     }
     
     /* Code block styling */
     .code-block {
-        background-color: #2c3e50;
-        border-radius: 12px;
-        padding: 1.5rem;
-        font-family: 'Fira Code', monospace;
-        margin: 1.5rem 0;
-        color: #ecf0f1;
-        overflow-x: auto;
+        background-color: #f8f9fa;
+        border: 1px solid var(--border-color);
+        border-radius: 8px;
+        padding: 1rem;
+        font-family: monospace;
+        margin: 1rem 0;
     }
     
     /* Tab styling */
     .stTabs [data-baseweb="tab-list"] {
         gap: 2rem;
-        background-color: transparent;
     }
     
     .stTabs [data-baseweb="tab"] {
         background-color: white;
-        border-radius: 12px;
-        padding: 0.8rem 1.5rem;
+        border-radius: 8px;
+        padding: 0.5rem 1rem;
         margin: 0 0.5rem;
-        font-weight: 600;
-        transition: all 0.3s ease;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
     }
     
     .stTabs [aria-selected="true"] {
-        background: linear-gradient(135deg, var(--secondary-color), var(--primary-color));
-        color: white;
-        box-shadow: 0 4px 8px rgba(52, 152, 219, 0.2);
-    }
-    
-    /* Analysis box styling */
-    .analysis-box {
-        background-color: white;
-        border-radius: 15px;
-        padding: 2rem;
-        margin: 1.5rem 0;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
-        border-left: 4px solid var(--secondary-color);
-    }
-    
-    /* Search box styling */
-    .search-box {
-        background-color: white;
-        border-radius: 12px;
-        padding: 1.5rem;
-        margin-bottom: 1.5rem;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05);
-    }
-    
-    /* Comparison box styling */
-    .comparison-box {
-        background-color: white;
-        border-radius: 15px;
-        padding: 2rem;
-        margin: 1.5rem 0;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
-        border-left: 4px solid var(--secondary-color);
-    }
-    
-    /* Selectbox styling */
-    .stSelectbox select {
-        border-radius: 8px;
-        padding: 0.8rem;
-        font-size: 1rem;
-        border: 2px solid var(--border-color);
-        background-color: white;
-        transition: all 0.3s ease;
-    }
-    
-    .stSelectbox select:focus {
-        border-color: var(--secondary-color);
-        box-shadow: 0 4px 8px rgba(52, 152, 219, 0.1);
-    }
-    
-    /* Slider styling */
-    .stSlider > div > div {
-        background-color: var(--secondary-color);
-    }
-    
-    /* Checkbox styling */
-    .stCheckbox > label {
-        font-weight: 500;
-    }
-    
-    /* Info box styling */
-    .info-box {
-        background: linear-gradient(135deg, #f8f9fa, #e9ecef);
-        border-radius: 15px;
-        padding: 2rem;
-        margin: 1.5rem 0;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
-    }
-    
-    /* Chart container styling */
-    .js-plotly-plot {
-        border-radius: 12px !important;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05) !important;
-    }
-    
-    /* Dataframe styling */
-    .dataframe {
-        border-radius: 12px;
-        overflow: hidden;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.05);
-    }
-    
-    .dataframe thead th {
         background-color: var(--primary-color);
         color: white;
     }
-    
-    /* Download button styling */
-    .stDownloadButton > button {
-        background: linear-gradient(135deg, var(--success-color), #27ae60);
-        color: white;
-        border: none;
-        border-radius: 12px;
-        padding: 0.8rem 2rem;
-        font-size: 1.1rem;
-        font-weight: 600;
-        transition: all 0.3s ease;
-    }
-    
-    .stDownloadButton > button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 4px 8px rgba(46, 204, 113, 0.2);
-    }
     </style>
 """, unsafe_allow_html=True)
 
-# Main header with updated design
+# Main header
 st.markdown("""
     <div class="main-header">
         <div class="main-title">SemViQA</div>
@@ -456,15 +344,11 @@ st.markdown("""
     </div>
 """, unsafe_allow_html=True)
 
-# Sidebar with updated design
+# Sidebar
 with st.sidebar:
-    st.markdown("""
-        <div class="info-box">
-            <h3>⚙️ Cài đặt Hệ thống</h3>
-        </div>
-    """, unsafe_allow_html=True)
+    st.markdown("### ⚙️ Cài đặt Hệ thống")
     
-    # Model selection with updated styling
+    # Model selection
     st.markdown("#### 🧠 Chọn Mô hình")
     qatc_model_name = st.selectbox(
         "Mô hình QATC",
@@ -500,7 +384,7 @@ with st.sidebar:
         ]
     )
     
-    # Threshold settings with updated styling
+    # Threshold settings
     st.markdown("#### ⚖️ Ngưỡng Phân tích")
     tfidf_threshold = st.slider(
         "Ngưỡng TF-IDF",
@@ -514,7 +398,7 @@ with st.sidebar:
         help="Điều chỉnh độ dài tối đa của bằng chứng"
     )
     
-    # Display settings with updated styling
+    # Display settings
     st.markdown("#### 👁️ Hiển thị")
     show_details = st.checkbox(
         "Hiển thị Chi tiết Xác suất",
@@ -522,7 +406,7 @@ with st.sidebar:
         help="Hiển thị thông tin chi tiết về xác suất dự đoán"
     )
     
-    # Performance settings with updated styling
+    # Performance settings
     st.markdown("#### ⚡ Hiệu suất")
     num_threads = st.slider(
         "Số luồng CPU",
@@ -534,7 +418,7 @@ with st.sidebar:
     os.environ["MKL_NUM_THREADS"] = str(num_threads)
 
 # Main content
-tabs = st.tabs(["🔍 Kiểm chứng", "📊 Lịch sử", "📈 Phân tích", "ℹ️ Thông tin"])
+tabs = st.tabs(["🔍 Kiểm chứng", "📊 Lịch sử", "ℹ️ Thông tin"])
 
 tokenizer_qatc, model_qatc = load_model(qatc_model_name, QATCForQuestionAnswering, device=DEVICE)
 tokenizer_bc, model_bc = load_model(bc_model_name, ClaimModelForClassification, is_bc=True, device=DEVICE)
@@ -575,8 +459,14 @@ with tabs[0]:
                 
                 # Clear memory and perform verification
                 gc.collect()
+                if DEVICE == "cuda":
+                    torch.cuda.empty_cache()
+                
                 start_time = time.time()
                 
+                # Monitor initial performance
+                initial_perf = monitor_performance()
+                
                 result = perform_verification(
                     preprocessed_claim, preprocessed_context,
                     model_qatc, tokenizer_qatc,
@@ -587,6 +477,9 @@ with tabs[0]:
                 
                 total_time = time.time() - start_time
                 
+                # Monitor final performance
+                final_perf = monitor_performance()
+                
                 # Format details
                 details = ""
                 if show_details:
@@ -595,9 +488,15 @@ with tabs[0]:
                         3-Class Predicted Label: {['NEI', 'SUPPORTED', 'REFUTED'][result['pred_tc']]}
                         2-Class Probability: {result['prob2class']:.2f}
                         2-Class Predicted Label: {['SUPPORTED', 'REFUTED'][result['pred_bc']] if isinstance(result['pred_bc'], int) and result['pred_tc'] != 0 else 'Not used'}
+                        
+                        Performance Metrics:
+                        - GPU Memory Used: {final_perf.get('gpu_memory_used', 'N/A'):.2f} MB
+                        - GPU Memory Cached: {final_perf.get('gpu_memory_cached', 'N/A'):.2f} MB
+                        - CPU Usage: {final_perf['cpu_percent']}%
+                        - Memory Usage: {final_perf['memory_percent']}%
                     """
                 
-                # Store result
+                # Store result with performance metrics
                 st.session_state.latest_result = {
                     "claim": claim,
                     "evidence": result['evidence'],
@@ -608,7 +507,8 @@ with tabs[0]:
                     "details": details,
                     "qatc_model": qatc_model_name,
                     "bc_model": bc_model_name,
-                    "tc_model": tc_model_name
+                    "tc_model": tc_model_name,
+                    "performance_metrics": final_perf
                 }
                 
                 # Add to history
@@ -616,7 +516,7 @@ with tabs[0]:
                     st.session_state.history = []
                 st.session_state.history.append(st.session_state.latest_result)
                 
-                # Display result
+                # Display result with performance metrics
                 res = st.session_state.latest_result
                 verdict_class = {
                     "SUPPORTED": "verdict-supported",
@@ -636,13 +536,29 @@ with tabs[0]:
                             <p><strong>Thời gian trích xuất bằng chứng:</strong> {res['evidence_time']:.2f} giây</p>
                             <p><strong>Thời gian phân loại:</strong> {res['verdict_time']:.2f} giây</p>
                             <p><strong>Tổng thời gian:</strong> {res['total_time']:.2f} giây</p>
+                            <p><strong>Hiệu suất:</strong></p>
+                            <ul>
+                                <li>CPU: {res['performance_metrics']['cpu_percent']}%</li>
+                                <li>RAM: {res['performance_metrics']['memory_percent']}%</li>
+                                {f"<li>GPU Memory: {res['performance_metrics'].get('gpu_memory_used', 'N/A'):.2f} MB</li>" if DEVICE == "cuda" else ""}
+                            </ul>
                         </div>
                         {f"<div class='code-block'><pre>{res['details']}</pre></div>" if show_details else ""}
                     </div>
                 """, unsafe_allow_html=True)
                 
-                # Download button
-                result_text = f"Câu khẳng định: {res['claim']}\nBằng chứng: {res['evidence']}\nKết luận: {res['verdict']}\nChi tiết: {res['details']}"
+                # Download button with performance metrics
+                result_text = f"""
+Câu khẳng định: {res['claim']}
+Bằng chứng: {res['evidence']}
+Kết luận: {res['verdict']}
+Chi tiết: {res['details']}
+
+Hiệu suất:
+- CPU: {res['performance_metrics']['cpu_percent']}%
+- RAM: {res['performance_metrics']['memory_percent']}%
+{f"- GPU Memory: {res['performance_metrics'].get('gpu_memory_used', 'N/A'):.2f} MB" if DEVICE == "cuda" else ""}
+"""
                 st.download_button(
                     "📥 Tải kết quả",
                     data=result_text,
@@ -655,22 +571,9 @@ with tabs[0]:
 # --- Tab History ---
 with tabs[1]:
     st.markdown("### 📊 Lịch sử Kiểm chứng")
-    
-    # Add search functionality
-    search_query = st.text_input("🔍 Tìm kiếm trong lịch sử", "")
-    
     if 'history' in st.session_state and st.session_state.history:
-        # Filter history based on search query
-        filtered_history = st.session_state.history
-        if search_query:
-            filtered_history = [
-                record for record in st.session_state.history
-                if search_query.lower() in record['claim'].lower() or
-                   search_query.lower() in record['evidence'].lower()
-            ]
-        
         # Download full history
-        history_df = pd.DataFrame(filtered_history)
+        history_df = pd.DataFrame(st.session_state.history)
         st.download_button(
             "📥 Tải toàn bộ lịch sử",
             data=history_df.to_csv(index=False).encode('utf-8'),
@@ -678,63 +581,23 @@ with tabs[1]:
             mime="text/csv"
         )
         
-        # Display history with comparison option
-        for idx, record in enumerate(reversed(filtered_history), 1):
-            col1, col2 = st.columns([3, 1])
-            
-            with col1:
-                st.markdown(f"""
-                    <div class="result-box">
-                        <h4>Kiểm chứng #{idx}</h4>
-                        <p><strong>Câu khẳng định:</strong> {record['claim']}</p>
-                        <p><strong>Kết luận:</strong> {verdict_icons.get(record['verdict'], '')} {record['verdict']}</p>
-                        <p><strong>Thời gian:</strong> {record['total_time']:.2f} giây</p>
-                    </div>
-                """, unsafe_allow_html=True)
-            
-            with col2:
-                if st.button("🔄 So sánh", key=f"compare_{idx}"):
-                    st.session_state.selected_for_comparison = record
-
-# --- Tab Analysis ---
-with tabs[2]:
-    st.markdown("### 📈 Phân tích Chi tiết")
-    
-    if 'history' in st.session_state and st.session_state.history:
-        # Add timestamp to history records
-        for record in st.session_state.history:
-            if 'timestamp' not in record:
-                record['timestamp'] = datetime.now()
-        
-        # Distribution analysis
-        st.markdown("#### 📊 Phân bố Kết quả")
-        verdict_fig = analyze_verdict_distribution(st.session_state.history)
-        if verdict_fig:
-            st.plotly_chart(verdict_fig, use_container_width=True)
-        
-        # Processing time analysis
-        st.markdown("#### ⏱️ Phân tích Thời gian Xử lý")
-        time_fig = analyze_processing_time(st.session_state.history)
-        if time_fig:
-            st.plotly_chart(time_fig, use_container_width=True)
-        
-        # Model performance analysis
-        st.markdown("#### 🧠 Phân tích Hiệu suất Mô hình")
-        model_stats = pd.DataFrame(st.session_state.history)
-        if not model_stats.empty:
-            st.markdown("##### Thống kê theo Mô hình")
-            model_performance = model_stats.groupby(['qatc_model', 'bc_model', 'tc_model']).agg({
-                'total_time': ['mean', 'count'],
-                'verdict': lambda x: (x == 'SUPPORTED').mean()
-            }).round(2)
-            st.dataframe(model_performance)
+        # Display history
+        for idx, record in enumerate(reversed(st.session_state.history), 1):
+            st.markdown(f"""
+                <div class="result-box">
+                    <h4>Kiểm chứng #{idx}</h4>
+                    <p><strong>Câu khẳng định:</strong> {record['claim']}</p>
+                    <p><strong>Kết luận:</strong> {verdict_icons.get(record['verdict'], '')} {record['verdict']}</p>
+                    <p><strong>Thời gian:</strong> {record['total_time']:.2f} giây</p>
+                </div>
+            """, unsafe_allow_html=True)
     else:
-        st.info("Chưa có dữ liệu để phân tích.")
+        st.info("Chưa có lịch sử kiểm chứng.")
 
 # --- Tab Info ---
-with tabs[3]:
+with tabs[2]:
     st.markdown("""
-        <div class="result-box">
+        <div class="info-section">
             <h3>ℹ️ Thông tin về SemViQA</h3>
             <p>SemViQA là hệ thống kiểm chứng thông tin tự động cho tiếng Việt, được phát triển bởi nhóm nghiên cứu tại Đại học Công nghệ Thông tin - Đại học Quốc gia TP.HCM.</p>
             
@@ -759,13 +622,5 @@ with tabs[3]:
                 <li><strong>REFUTED:</strong> Câu khẳng định bị bác bỏ bởi bằng chứng</li>
                 <li><strong>NEI:</strong> Không đủ bằng chứng để kết luận</li>
             </ul>
-            
-            <h4>🆕 Tính năng Mới</h4>
-            <ul>
-                <li><strong>Phân tích Chi tiết:</strong> Xem thống kê và biểu đồ về kết quả kiểm chứng</li>
-                <li><strong>Tìm kiếm Lịch sử:</strong> Dễ dàng tìm kiếm trong lịch sử kiểm chứng</li>
-                <li><strong>So sánh Kết quả:</strong> So sánh các kết quả kiểm chứng với nhau</li>
-                <li><strong>Báo cáo Chi tiết:</strong> Xuất báo cáo chi tiết về kết quả kiểm chứng</li>
-            </ul>
         </div>
     """, unsafe_allow_html=True)