Update app.py

app.py

@@ -11,17 +11,22 @@ from semviqa.tvc.tvc_eval import classify_claim
 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 and custom CSS for a modern, clean look
+# Page Configuration
 st.set_page_config(page_title="SemViQA Demo", layout="wide")
 
+# Custom CSS for improved UI
 st.markdown("""
     <style>
+        body {
+            font-family: 'Arial', sans-serif;
+        }
         .big-title { 
             font-size: 36px; 
             font-weight: bold; 
-            color: #4A90E2; 
+            color: #0078D4; 
             text-align: center; 
             margin-top: 20px;
         }
@@ -38,9 +43,10 @@ st.markdown("""
             width: 100%; 
             border-radius: 8px; 
             padding: 10px; 
+            transition: 0.3s;
         }
-        .stTextArea textarea { 
-            font-size: 16px; 
+        .stButton>button:hover {
+            background-color: #45a049;
         }
         .result-box { 
             background-color: #f9f9f9; 
@@ -52,7 +58,6 @@ st.markdown("""
         .verdict { 
             font-size: 24px; 
             font-weight: bold; 
-            margin: 0;
             display: flex;
             align-items: center;
         }
@@ -62,11 +67,12 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-st.markdown("<p class='big-title'>SemViQA: Semantic Question Answering System for Vietnamese Fact-Checking</p>", unsafe_allow_html=True)
+# Page Header
+st.markdown("<p class='big-title'>SemViQA: Vietnamese Fact-Checking System</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: Settings and additional features
-with st.sidebar.expander("⚙️ Settings", expanded=False):
+# Sidebar: 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", [
@@ -93,71 +99,55 @@ with st.sidebar.expander("⚙️ Settings", expanded=False):
     ])
     show_details = st.checkbox("Show probability details", value=False)
 
-# Initialize verification history in session state
-if 'history' not in st.session_state:
-    st.session_state.history = []
-
-# Load the selected models
+# Load 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)
 
-# 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.")
-
-# Define icon mapping for each verdict label
+# Define verdict icons
 verdict_icons = {
     "SUPPORTED": "✅",
     "REFUTED": "❌",
     "NEI": "⚠️"
 }
 
-if st.button("Verify"):
-    with st.spinner("Verifying..."):
-        # Extract evidence
-        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
-        )
-        
-        # Classify the claim
-        verdict = "NEI"
-        prob3class, pred_tc = classify_claim(claim, evidence, model_tc, tokenizer_tc, "cuda" if torch.cuda.is_available() else "cpu")
-        
-        details = ""
-        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:.2f} - <strong>2-Class Probability:</strong> {prob2class:.2f}</p>"
-        
-        # Save the verification record in session history
-        st.session_state.history.append({
-            "claim": claim,
-            "evidence": evidence,
-            "verdict": verdict
-        })
-        
-        # Display the results with icon and label (without extra "Verdict:" text)
-        st.markdown(f"""
-            <div class='result-box'>
-                <h3>Result</h3>
-                <p><strong>Evidence:</strong> {evidence}</p>
-                <p class='verdict'><span class='verdict-icon'>{verdict_icons.get(verdict, '')}</span>{verdict}</p>
-                {details}
-            </div>
-        """, unsafe_allow_html=True)
+# Tabs for functionalities
+tabs = st.tabs(["Verify", "History", "About"])
+
+# --- Verify Tab ---
+with tabs[0]:
+    st.subheader("Verify a Claim")
+    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.")
+    
+    if st.button("Verify", key="verify_button"):
+        with st.spinner("Verifying..."):
+            with torch.no_grad():
+                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
+                )
+                verdict = "NEI"
+                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")
+                    verdict = "SUPPORTED" if pred_bc == 0 else "REFUTED" if prob2class > prob3class else ["NEI", "SUPPORTED", "REFUTED"][pred_tc]
+                
+                # Display result
+                st.markdown(f"""
+                    <div class='result-box'>
+                        <h3>Result</h3>
+                        <p><strong>Evidence:</strong> {evidence}</p>
+                        <p class='verdict'><span class='verdict-icon'>{verdict_icons.get(verdict, '')}</span>{verdict}</p>
+                    </div>
+                """, unsafe_allow_html=True)
+                
+                if torch.cuda.is_available():
+                    torch.cuda.empty_cache()
 
-# Display verification history in the sidebar
-with st.sidebar.expander("Verification History", expanded=False):
-    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.")
+# --- About Tab ---
+with tabs[2]:
+    st.subheader("About SemViQA")
+    st.markdown("""SemViQA is a semantic fact-checking system for Vietnamese information verification.""")