Update CrossTalk AI with clean standard UI

app.py

@@ -258,140 +258,203 @@ def safe_search(query):
     )
 
 
+
 custom_css = """
 .gradio-container {
-    background:
-        radial-gradient(circle at 15% 5%, rgba(139,92,246,0.32), transparent 28%),
-        radial-gradient(circle at 90% 10%, rgba(168,85,247,0.22), transparent 26%),
-        linear-gradient(180deg, #07020F 0%, #090716 50%, #07020F 100%) !important;
-    color: #F8F5FF !important;
-    font-family: Inter, ui-sans-serif, system-ui, sans-serif !important;
+    background: #0B0F19 !important;
+    color: #F8FAFC !important;
+    font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif !important;
 }
 
 #shell {
-    max-width: 1280px;
-    margin: auto;
+    max-width: 1180px;
+    margin: 0 auto;
+    padding: 26px 18px 36px 18px;
 }
 
 .hero {
-    background: linear-gradient(135deg, rgba(18,16,38,0.96), rgba(37,22,78,0.82));
-    border: 1px solid rgba(255,255,255,0.10);
-    border-radius: 30px;
-    padding: 34px;
+    background: #111827;
+    border: 1px solid #243044;
+    border-radius: 18px;
+    padding: 26px 30px;
     margin-bottom: 22px;
-    box-shadow: 0 26px 80px rgba(0,0,0,0.42);
+    box-shadow: 0 18px 45px rgba(0,0,0,0.24);
 }
 
 .badge {
     display: inline-block;
-    padding: 8px 13px;
+    padding: 6px 11px;
     border-radius: 999px;
-    background: rgba(139,92,246,0.16);
-    border: 1px solid rgba(139,92,246,0.36);
-    color: #EDE7FF;
-    font-weight: 800;
-    font-size: 13px;
+    background: #172033;
+    border: 1px solid #2B3A55;
+    color: #CBD5E1;
+    font-weight: 700;
+    font-size: 12px;
     margin-bottom: 14px;
 }
 
 .title {
-    font-size: 48px;
-    line-height: 1.02;
-    font-weight: 950;
-    letter-spacing: -0.05em;
-    margin: 0;
-    background: linear-gradient(135deg, #FFFFFF, #D9CCFF, #A78BFA);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
+    font-size: 34px;
+    line-height: 1.12;
+    font-weight: 850;
+    letter-spacing: -0.035em;
+    margin: 0 0 10px 0;
+    color: #FFFFFF;
 }
 
 .subtitle {
+    color: #CBD5E1;
+    font-size: 15px;
+    line-height: 1.65;
     max-width: 980px;
-    margin-top: 15px;
-    color: #BDB2DE;
-    font-size: 16px;
-    line-height: 1.7;
 }
 
-.metrics {
-    display: grid;
-    grid-template-columns: repeat(4, 1fr);
-    gap: 14px;
-    margin-top: 24px;
+.info-strip {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 10px;
+    margin-top: 18px;
 }
 
-.metric {
-    padding: 17px;
-    border-radius: 20px;
-    background: rgba(255,255,255,0.055);
-    border: 1px solid rgba(255,255,255,0.09);
+.info-pill {
+    padding: 9px 12px;
+    border-radius: 10px;
+    background: #0F172A;
+    border: 1px solid #26344D;
+    color: #E2E8F0;
+    font-size: 13px;
+    font-weight: 650;
 }
 
-.metric strong {
-    display: block;
-    color: #FFFFFF;
-    font-size: 25px;
-    font-weight: 950;
+.main-card {
+    background: #111827 !important;
+    border: 1px solid #243044 !important;
+    border-radius: 18px !important;
+    padding: 22px !important;
+    box-shadow: 0 16px 38px rgba(0,0,0,0.22);
 }
 
-.metric span {
-    color: #B8ADD8;
-    font-size: 12px;
+.search-card {
+    border-top: 4px solid #3B82F6 !important;
 }
 
-.panel {
-    background: rgba(18,16,38,0.78) !important;
-    border: 1px solid rgba(255,255,255,0.10) !important;
-    border-radius: 24px !important;
-    padding: 18px !important;
-    box-shadow: 0 20px 55px rgba(0,0,0,0.30);
+.output-card {
+    border-top: 4px solid #22C55E !important;
 }
 
-button.primary-btn {
-    background: linear-gradient(135deg, #8B5CF6, #A855F7) !important;
-    border: none !important;
-    border-radius: 16px !important;
-    color: white !important;
-    font-weight: 900 !important;
-    min-height: 48px !important;
+.section-title {
+    color: #F8FAFC;
+    font-size: 19px;
+    font-weight: 800;
+    margin-bottom: 4px;
 }
 
-button.secondary-btn {
-    background: rgba(255,255,255,0.08) !important;
-    border: 1px solid rgba(255,255,255,0.12) !important;
-    border-radius: 16px !important;
-    color: white !important;
-    font-weight: 800 !important;
-    min-height: 48px !important;
+.section-subtitle {
+    color: #94A3B8;
+    font-size: 13px;
+    line-height: 1.5;
+    margin-bottom: 16px;
 }
 
 textarea, input {
-    background: rgba(5,4,14,0.78) !important;
-    color: white !important;
-    border: 1px solid rgba(255,255,255,0.14) !important;
-    border-radius: 16px !important;
+    background: #0B1220 !important;
+    color: #FFFFFF !important;
+    border: 1px solid #334155 !important;
+    border-radius: 12px !important;
+    font-size: 15px !important;
+}
+
+textarea:focus, input:focus {
+    border-color: #60A5FA !important;
+    box-shadow: 0 0 0 3px rgba(59,130,246,0.18) !important;
+}
+
+button.search-button {
+    background: #2563EB !important;
+    border: 1px solid #2563EB !important;
+    border-radius: 12px !important;
+    color: #FFFFFF !important;
+    font-weight: 800 !important;
+    min-height: 46px !important;
+}
+
+button.search-button:hover {
+    background: #1D4ED8 !important;
+}
+
+button.clear-button {
+    background: #1F2937 !important;
+    border: 1px solid #374151 !important;
+    border-radius: 12px !important;
+    color: #E5E7EB !important;
+    font-weight: 750 !important;
+    min-height: 46px !important;
+}
+
+.status-panel textarea {
+    background: #07111F !important;
+    border: 1px solid #334155 !important;
+    color: #FFFFFF !important;
+    font-weight: 750 !important;
+}
+
+.result-note {
+    background: #0F172A;
+    border: 1px solid #243044;
+    border-radius: 12px;
+    padding: 12px 14px;
+    color: #CBD5E1;
+    font-size: 13px;
+    line-height: 1.55;
+}
+
+.dataframe {
+    border-radius: 12px !important;
+    overflow: hidden !important;
+    border: 1px solid #243044 !important;
 }
 
 .footer {
     text-align: center;
-    color: #A99FD0;
+    color: #94A3B8;
     font-size: 13px;
     line-height: 1.6;
     margin-top: 18px;
 }
 
+#examples-block {
+    margin-top: 12px;
+}
+
 @media (max-width: 900px) {
-    .title { font-size: 34px; }
-    .metrics { grid-template-columns: repeat(2, 1fr); }
+    .title {
+        font-size: 28px;
+    }
 }
 """
 
 
+EMPTY_RESULTS = pd.DataFrame(
+    columns=[
+        "query",
+        "score",
+        "method",
+        "confidence",
+        "language",
+        "source_text",
+        "english_meaning",
+        "bangla_meaning",
+        "part_of_speech",
+        "note"
+    ]
+)
+
+
 with gr.Blocks(
     css=custom_css,
-    theme=gr.themes.Soft(
-        primary_hue="violet",
-        secondary_hue="purple",
+    theme=gr.themes.Base(
+        primary_hue="blue",
+        secondary_hue="slate",
         neutral_hue="slate"
     )
 ) as demo:
@@ -400,35 +463,43 @@ with gr.Blocks(
         gr.HTML(
             """
             <div class="hero">
-                <div class="badge">🌐 Full trained hybrid retrieval system</div>
+                <div class="badge">Full trained hybrid retrieval system</div>
                 <h1 class="title">CrossTalk AI</h1>
                 <div class="subtitle">
-                    A confidence-aware lexical retrieval and translation support system for low-resource ethnic languages of Bangladesh.
-                    It combines exact dictionary matching, base-form matching, fine-tuned multilingual E5 semantic fallback,
-                    FAISS vector search, and safe output handling.
+                    A retrieval-based language identification and translation support system for low-resource ethnic languages of Bangladesh.
+                    It combines verified dictionary lookup with fine-tuned E5 semantic retrieval and FAISS vector search.
                 </div>
-                <div class="metrics">
-                    <div class="metric"><strong>12</strong><span>Ethnic language groups</span></div>
-                    <div class="metric"><strong>70K+</strong><span>Indexed source entries</span></div>
-                    <div class="metric"><strong>E5</strong><span>Fine-tuned semantic retriever</span></div>
-                    <div class="metric"><strong>FAISS</strong><span>Vector search backend</span></div>
+                <div class="info-strip">
+                    <div class="info-pill">12 language groups</div>
+                    <div class="info-pill">70K+ indexed entries</div>
+                    <div class="info-pill">Fine-tuned E5 retriever</div>
+                    <div class="info-pill">FAISS vector search</div>
+                    <div class="info-pill">Confidence-aware output</div>
                 </div>
             </div>
             """
         )
 
-        with gr.Row():
-            with gr.Column(scale=5, elem_classes=["panel"]):
-                gr.Markdown("### Search source word")
+        with gr.Row(equal_height=False):
+            with gr.Column(scale=5, elem_classes=["main-card", "search-card"]):
+                gr.HTML(
+                    """
+                    <div class="section-title">Search input</div>
+                    <div class="section-subtitle">
+                        Enter an ethnic source word. Exact and base-form matches are treated as verified dictionary outputs.
+                    </div>
+                    """
+                )
+
                 query = gr.Textbox(
-                    label="Input word",
-                    placeholder="Try: kəkhyáŋ, Hula, Aina, aam, bajaoo",
+                    label="Source word",
+                    placeholder="Example: kəkhyáŋ, Hula, Aina, aam, bajaoo",
                     lines=1
                 )
 
                 with gr.Row():
-                    clear_btn = gr.Button("Clear", elem_classes=["secondary-btn"])
-                    submit_btn = gr.Button("Search", elem_classes=["primary-btn"])
+                    clear_btn = gr.Button("Clear", elem_classes=["clear-button"])
+                    submit_btn = gr.Button("Search", elem_classes=["search-button"])
 
                 gr.Examples(
                     examples=[
@@ -440,34 +511,70 @@ with gr.Blocks(
                         ["unknown tribal word"]
                     ],
                     inputs=query,
-                    label="Quick examples"
+                    label="Examples",
+                    elem_id="examples-block"
+                )
+
+            with gr.Column(scale=7, elem_classes=["main-card", "output-card"]):
+                gr.HTML(
+                    """
+                    <div class="section-title">Retrieval output</div>
+                    <div class="section-subtitle">
+                        The system returns verified dictionary matches first. Semantic fallback appears only when no verified match is found.
+                    </div>
+                    """
+                )
+
+                status = gr.Textbox(
+                    label="System status",
+                    lines=1,
+                    elem_classes=["status-panel"],
+                    value="Ready."
                 )
 
-            with gr.Column(scale=7, elem_classes=["panel"]):
-                gr.Markdown("### Retrieval output")
-                status = gr.Textbox(label="System status", lines=1)
                 summary = gr.Markdown(
-                    value="Enter a source word to retrieve verified dictionary matches or semantic candidates."
+                    value="""
+                    <div class="result-note">
+                    Enter a source word and click Search. Results will appear below.
+                    </div>
+                    """
                 )
+
                 results = gr.Dataframe(
-                    label="Results",
+                    label="Results table",
+                    value=EMPTY_RESULTS,
                     interactive=False,
-                    wrap=True
+                    wrap=True,
+                    max_height=420
                 )
 
         gr.HTML(
             """
             <div class="footer">
-                Exact and base-form matches are verified dictionary outputs.
-                Fine-tuned semantic fallback results are candidate suggestions only and should not be treated as confirmed translations.
+                Exact/base-form matches are verified dictionary outputs. Fine-tuned semantic fallback results are candidate suggestions only, not confirmed translations.
             </div>
             """
         )
 
-    submit_btn.click(fn=safe_search, inputs=query, outputs=[status, summary, results])
-    query.submit(fn=safe_search, inputs=query, outputs=[status, summary, results])
+    submit_btn.click(
+        fn=safe_search,
+        inputs=query,
+        outputs=[status, summary, results]
+    )
+
+    query.submit(
+        fn=safe_search,
+        inputs=query,
+        outputs=[status, summary, results]
+    )
+
     clear_btn.click(
-        fn=lambda: ("", "Ready.", "Enter a source word to retrieve verified dictionary matches or semantic candidates.", pd.DataFrame()),
+        fn=lambda: (
+            "",
+            "Ready.",
+            '<div class="result-note">Enter a source word and click Search. Results will appear below.</div>',
+            EMPTY_RESULTS
+        ),
         inputs=None,
         outputs=[query, status, summary, results]
     )