update code

README.md

@@ -10,4 +10,6 @@ tags:
 pinned: true
 short_description: Powerful LLM Detection
 license: apache-2.0
----
+---
+
+To run the web app on your local device, please run the command: `streamlit run src/app.py`.

src/app.py

@@ -39,36 +39,118 @@ import time
 st.markdown(
     """
     <style>
-    /* Primary button (Detect) */
+    /* 控制 Streamlit columns 间距 */
+    div[data-testid="column"] {
+        padding-left: 3.0rem;
+        padding-right: 3.0rem;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown(
+    """
+    <style>
+    /* Text area & text input */
+    textarea, input[type="text"] {
+        background-color: #f8fafc !important;
+        border: 1px solid #e5e7eb !important;
+        color: #111827 !important;
+    }
+
+    textarea::placeholder {
+        color: #9ca3af !important;
+    }
+
+    /* Selectbox */
+    div[data-testid="stSelectbox"] > div {
+        background-color: #f8fafc !important;
+        border: 1px solid #e5e7eb !important;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+st.markdown(
+    """
+    <style>
+    /* Detect button */
     div.stButton > button[kind="primary"] {
-        background-color: #2a4f7c;      /* 学术蓝 */
-        border-color: #2a4f7c;
-        color: white;
+        background-color: #fdae6b;
+        border: white;
+        color: black;
         font-weight: 600;
+        height: 4.3rem;
+
+        font-size: 1.1rem;
+
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        gap: 0.55rem;
+    }
+
+    /* Icon inside Detect button */
+    div.stButton > button[kind="primary"] span {
+        font-size: 1.25rem;
+        line-height: 1;
     }
 
     div.stButton > button[kind="primary"]:hover {
-        background-color: #1f3a5f;      /* hover 深一点 */
-        border-color: #1f3a5f;
-        color: white;
+        background-color: #fd8d3c;
+        border-color: white;
     }
 
     div.stButton > button[kind="primary"]:active {
-        background-color: #1b2f4a;
-        border-color: #1b2f4a;
+        background-color: #fd8d3c;
+        border-color: white;
     }
     </style>
     """,
     unsafe_allow_html=True
 )
 
+st.markdown(
+    """
+    <style>
+    /* Significance slider – light purple (academic) */
+
+    /* Slider track (inactive) */
+    div[data-testid="stSlider"] > div > div > div > div {
+        background-color: #ebe7f2;
+    }
+
+    /* Slider active range */
+    div[data-testid="stSlider"] div[role="slider"] {
+        background-color: #bebada !important;
+        border-color: #bebada !important;
+    }
+
+    /* Slider thumb */
+    div[data-testid="stSlider"] div[role="slider"]::before {
+        background-color: #bc80bd;
+        box-shadow: 0 0 0 1px rgba(188,128,189,0.4);
+    }
+
+    /* α value text */
+    div[data-testid="stSlider"] span {
+        color: #bc80bd;
+        font-weight: 500;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+
 # -----------------
 # Page Configuration
 # -----------------
 st.set_page_config(
     page_title="DetectGPTPro",
-    page_icon="🔍",
-    layout="wide"
+    page_icon="🕵️",
 )
 
 # -----------------
@@ -194,17 +276,15 @@ if 'feedback_given' not in st.session_state:
 # -----------------
 # Streamlit Layout
 # -----------------
-_, col0, _ = st.columns((1, 5, 1))
-with col0:
-    st.markdown(
-        "<h1 style='text-align: center; color: #0072C3;'>DetectGPTPro: Super Adaptive LLM's Texts Detection</h1>",
-        unsafe_allow_html=True,
-    )
-
 st.markdown(
-    """Pasted the text to be detected below and click the 'Detect' button to get the p-value. Use a better option may improve detection."""
+    "<h1 style='text-align: center;'> 🕵️ Adaptive LLM-texts Detector</h1>",
+    unsafe_allow_html=True,
 )
 
+# st.markdown(
+#     """Pasted the text to be detected below and click the 'Detect' button to get the p-value. Use a better option may improve detection."""
+# )
+
 # Display model loading status
 if not model_loaded:
     st.error(f"❌ Failed to load model: {error_message}")
@@ -214,25 +294,37 @@ if not model_loaded:
 # Main Interface
 # -----------------
 # --- Two columns: Input text & button | Result displays ---
-col1, col2 = st.columns((1, 1))
-
-with col1:
-    text_input = st.text_area(
-        label="",
-        placeholder="Paste your text to be detected here",
-        help="Typically, providing text with a longer content would get a more reliable result.",
-        height=200,
-    )
-    
-    detect_clicked = st.button("Detect", type="primary", use_container_width=True)
+text_input = st.text_area(
+    label="",
+    placeholder="Paste your text to be detected here",
+    help="Typically, providing text with a longer content would get a more reliable result.",
+    height=200,
+)
 
-    selected_domain = st.selectbox(
-        label="⚙️ Domain (Optional)",
-        options=DOMAINS,
-        index=0,  # Default to General
-        help="💡 **Tip:** Select the domain that best matches your text for improving detection accuracy. Default is 'General' that means consider all domains."
-    )
+subcol11, subcol12, subcol13 = st.columns((1, 1, 1))
 
+selected_domain = subcol11.selectbox(
+    label="⚙️ Domain",
+    options=DOMAINS,
+    index=0,  # Default to General
+    # label_visibility="collapsed",
+    # label_visibility="hidden",
+    help="💡 **Tip:** Select the domain that best matches your text for improving detection accuracy. Default is 'General' that means consider all domains."
+)
+
+detect_clicked = subcol12.button("🔍 Detect", type="primary", use_container_width=True)
+
+selected_level = subcol13.slider(
+    label="Significance (α)",
+    min_value=0.01,
+    max_value=0.2,
+    value=0.05,
+    step=0.005,
+    # label_visibility="collapsed",
+    help="💡 **Tip:** Select the significance level for the detection test."
+)
+
+col2, col3 = st.columns((1, 1))
 with col2:
     statistics_ph = st.empty()
     statistics_ph.text_input(
@@ -241,7 +333,8 @@ with col2:
         disabled=True,
         help="Statistic will appear here after clicking the Detect button.",
     )
-    
+
+with col3:
     pvalue_ph = st.empty()
     pvalue_ph.text_input(
         label="p-value",
@@ -295,83 +388,82 @@ if detect_clicked:
             }
 
             # Update score displays
-            with col2:
-                statistics_ph.text_input(
-                    label="Statistics",
-                    value=f"{crit:.6f}",
-                    disabled=True,
-                    help="Detection statistics will appear here after clicking Detect.",
-                )
-                
-                pvalue_ph.text_input(
-                    label="p-value",
-                    value=f"{p_value:.6f}",
-                    disabled=True,
-                    help="p-value will appear here after clicking Detect.",
-                )
-
-                st.info(
-                    """
-                    **📊 p-value:**
-                    - **Lower p-value** (closer to 0) indicates text is **more likely AI-generated**
-                    - **Higher p-value** (closer to 1) indicates text is **more likely human-written**
-                    - Generally, p-value < 0.05 suggests the text may be LLM-generated
-                    """,
-                    icon="💡"
-                )
-
-                # ========== 🆕 Feedback buttons (moved here for better UX) ==========
-                st.markdown("**📝 Result Feedback**: Does this detection result meet your expectations?")
-                
-                current_text = text_input
-                current_domain = selected_domain
-                current_statistics = crit
-                current_pvalue = p_value
-                feedback_col1, feedback_col2 = st.columns(2)
-                
-                with feedback_col1:
-                    if st.button("✅ Expected", use_container_width=True, type="secondary", key=f"expected_btn_{hash(text_input[:50])}"):
-                        try:
-                            success, message = feedback_manager.save_feedback(
-                                current_text,
-                                current_domain,
-                                current_statistics,
-                                current_pvalue,
-                                'expected'
-                            )
-                            if success:
-                                st.success("✅ Thank you for your feedback!")
-                                st.caption(f"💾 {message}")
-                            else:
-                                st.error(f"Failed to save feedback: {message}")
-                        except Exception as e:
-                            st.error(f"Failed to save feedback: {str(e)}")
-                            import traceback
-                            st.code(traceback.format_exc())
-                
-                with feedback_col2:
-                    if st.button("❌ Unexpected", use_container_width=True, type="secondary", key=f"unexpected_btn_{hash(text_input[:50])}"):
-                        try:
-                            success, message = feedback_manager.save_feedback(
-                                current_text,
-                                current_domain,
-                                current_statistics,
-                                current_pvalue,
-                                'unexpected'
-                            )
-                            if success:
-                                st.warning("❌ Feedback recorded! This will help us improve.")
-                                st.caption(f"💾 {message}")
-                            else:
-                                st.error(f"Failed to save feedback: {message}")
-                        except Exception as e:
-                            st.error(f"Failed to save feedback: {str(e)}")
-                            import traceback
-                            st.code(traceback.format_exc())
-                
-                if st.session_state.feedback_given:
-                    st.success("✅ Feedback submitted successfully!")
-                # ============================================
+            statistics_ph.text_input(
+                label="Statistics",
+                value=f"{crit:.6f}",
+                disabled=True,
+                help="Detection statistics will appear here after clicking Detect.",
+            )
+            
+            pvalue_ph.text_input(
+                label="p-value",
+                value=f"{p_value:.6f}",
+                disabled=True,
+                help="p-value will appear here after clicking Detect.",
+            )
+
+            st.info(
+                """
+                **📊 p-value:**
+                - **Lower p-value** (closer to 0) indicates text is **more likely AI-generated**
+                - **Higher p-value** (closer to 1) indicates text is **more likely human-written**
+                - Generally, p-value < 0.05 suggests the text may be LLM-generated
+                """,
+                icon="💡"
+            )
+
+            # ========== 🆕 Feedback buttons (moved here for better UX) ==========
+            st.markdown("**📝 Result Feedback**: Does this detection result meet your expectations?")
+            
+            current_text = text_input
+            current_domain = selected_domain
+            current_statistics = crit
+            current_pvalue = p_value
+            feedback_col1, feedback_col2 = st.columns(2)
+            
+            with feedback_col1:
+                if st.button("✅ Expected", use_container_width=True, type="secondary", key=f"expected_btn_{hash(text_input[:50])}"):
+                    try:
+                        success, message = feedback_manager.save_feedback(
+                            current_text,
+                            current_domain,
+                            current_statistics,
+                            current_pvalue,
+                            'expected'
+                        )
+                        if success:
+                            st.success("✅ Thank you for your feedback!")
+                            st.caption(f"💾 {message}")
+                        else:
+                            st.error(f"Failed to save feedback: {message}")
+                    except Exception as e:
+                        st.error(f"Failed to save feedback: {str(e)}")
+                        import traceback
+                        st.code(traceback.format_exc())
+            
+            with feedback_col2:
+                if st.button("❌ Unexpected", use_container_width=True, type="secondary", key=f"unexpected_btn_{hash(text_input[:50])}"):
+                    try:
+                        success, message = feedback_manager.save_feedback(
+                            current_text,
+                            current_domain,
+                            current_statistics,
+                            current_pvalue,
+                            'unexpected'
+                        )
+                        if success:
+                            st.warning("❌ Feedback recorded! This will help us improve.")
+                            st.caption(f"💾 {message}")
+                        else:
+                            st.error(f"Failed to save feedback: {message}")
+                    except Exception as e:
+                        st.error(f"Failed to save feedback: {str(e)}")
+                        import traceback
+                        st.code(traceback.format_exc())
+            
+            if st.session_state.feedback_given:
+                st.success("✅ Feedback submitted successfully!")
+            # ============================================
 
             # Show detailed results
             with result_placeholder: