Update app.py

app.py

@@ -4,13 +4,10 @@ from gliner2 import GLiNER2
 from huggingface_hub import login
 import os
 
-# Get API key from environment variable
-hf_token = os.getenv("HF_TOKEN")
-
 # Authenticate with Hugging Face
+hf_token = os.getenv("HF_TOKEN")
 login(hf_token)
 
-# ——— Load model once ———
 model = GLiNER2.from_pretrained("fastino/gliner2-base-0207")
 
 def run_ner(text, types_csv, descs):
@@ -20,6 +17,7 @@ def run_ner(text, types_csv, descs):
     res = model.extract_entities(text=text, entity_types=inp, include_confidence=True)
     return model.pretty_print_results(res, include_confidence=True)
 
+
 def run_class(text, task, labels_csv, descs, multi):
     labels = [l.strip() for l in labels_csv.split(",") if l.strip()]
     desc_map = {k: v for line in descs.split("\n") if ":" in line for k,v in [line.split(":",1)]}
@@ -34,6 +32,7 @@ def run_class(text, task, labels_csv, descs, multi):
     res = model.classify_text(text=text, tasks=tasks, include_confidence=True)
     return model.pretty_print_results(res, include_confidence=True)
 
+
 def run_struct(text, struct_json):
     try:
         cfg = json.loads(struct_json)
@@ -42,143 +41,112 @@ def run_struct(text, struct_json):
     res = model.extract_json(text=text, structures=cfg, include_confidence=True)
     return model.pretty_print_results(res, include_confidence=True)
 
-# ——— Clean White Theme & Layout ———
+# Custom CSS for modern look
 custom_css = """
-body {
-  background: #ffffff !important;
-  font-family: 'Helvetica Neue', sans-serif;
-  color: #333333;
-}
-.gradio-container {
-  max-width: 600px;
-  padding: 0;
-  background: #ffffff;
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
+:root {
+  --primary: #4f46e5;
+  --secondary: #6366f1;
+  --background: #f9fafb;
+  --card-bg: #ffffff;
+  --text: #1f2937;
+  --muted: #6b7280;
 }
-header, .logo, .subtitle {
-  border: none !important;
-  box-shadow: none !important;
+body {
+  background: var(--background) !important;
+  font-family: 'Inter', sans-serif;
+  color: var(--text) !important;
 }
-.gradio-container * {
-  box-shadow: none !important;
+header.brand {
+  padding: 2rem 0;
+  text-align: center;
 }
-.card {
-  background: #ffffff;
-  padding: 15px;
+header.brand .logo {
+  font-size: 2rem;
+  font-weight: 700;
+  color: var(--primary);
 }
-label {
-  color: #444444;
-  font-weight: 600;
+header.brand .subtitle {
+  margin-top: 0.2rem;
+  font-size: 0.9rem;
+  color: var(--muted);
 }
-.gr-textbox textarea,
-.gr-code,
-.gr-dropdown,
-.gr-checkbox,
-.gr-button {
-  background: #ffffff !important;
-  box-shadow: none !important;
+.gradio-container {
+  max-width: 800px;
+  margin: auto;
+  padding: 1rem;
 }
-.accordion-button {
-  border: none !important;
-  box-shadow: none !important;
-  font-weight: 500;
+.card {
+  background: var(--card-bg);
+  padding: 1.5rem;
+  border-radius: 0.75rem;
+  box-shadow: 0 4px 10px rgba(0,0,0,0.05);
+  margin-bottom: 1.5rem;
 }
 .gr-button.primary {
-  background: #5b8def;
-  color: #ffffff;
+  background: var(--primary) !important;
+  color: #fff !important;
+  border-radius: 0.5rem;
+  padding: 0.6rem 1.2rem;
 }
 """
 
-with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as demo:
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"), css=custom_css) as demo:
     # Header
-    gr.HTML("""
-    <header style="text-align:center; padding:10px 0;">
-      <div class="logo" style="font-size:1.8rem; font-weight:700; color:#333333;">🎯 GLiNER2</div>
-      <div class="subtitle" style="font-size:0.85rem; color:#777777;">Compact • White Theme • Screenshot-Ready</div>
-    </header>
-    """)
+    gr.HTML(
+        """
+        <header class=\"brand\">  
+          <div class=\"logo\">✨ GLiNER2</div>  
+          <div class=\"subtitle\">Compact • Modern • Screenshot-Ready</div>  
+        </header>
+        """
+    )
 
     with gr.Tabs():
-        # Structure Extraction Tab
-        with gr.TabItem("Hierarchical Structure Extraction"):
-            with gr.Row(elem_classes="card"):
-                with gr.Column(scale=2):
-                    txt3 = gr.Textbox(
-                        label="Input text", lines=3,
-                        value=(
-                            "The Acme Pro Laptop 15” features an Intel Core i7 processor, 16GB RAM, 512GB SSD, "
-                            "and a 15.6-inch 4K display. Priced at $1,499, it offers Wi-Fi 6, Bluetooth 5.2, and "
-                            "a backlit keyboard."
-                        )
-                    )
-                    struct3 = gr.Code(
-                        language="json", lines=7,
-                        label = "Schema",
-                        value=json.dumps({
-                            "product": [
-                                "name::str::Product name and model",
-                                "price::str::Product cost",
-                                "features::list::Key product features",
-                                "category::[electronics|software|hardware]::str"
-                            ]
-                        }, indent=2)
-                    )
-                    btn3 = gr.Button("Predict", variant="primary")
-                with gr.Column(scale=1):
-                    out3 = gr.Code(language="json", lines=8, label="Output")
-                btn3.click(run_struct, [txt3, struct3], out3)
-
         # NER Tab
-        with gr.TabItem("Named Entity Recognition"):
-            with gr.Row(elem_classes="card"):
+        with gr.TabItem("🔍 Named Entity Recognition"):
+            with gr.Row(elem_classes="card", gap="small"):
                 with gr.Column(scale=2):
-                    txt1 = gr.Textbox(
-                        label="Text", lines=4,
-                        value=(
-                            "Dr. Alice Smith, Chief Data Scientist at OpenAI, spoke at the AI Summit "
-                            "in San Francisco on June 12, 2025, about advancements in large-scale language "
-                            "models, ethical AI guidelines, and real-world GPT-4 Turbo applications."
-                        )
-                    )
-                    types1 = gr.Textbox(label="Types (csv)", value="person, title, organization, event, location, date, topic")
-                    with gr.Accordion("Descriptions (opt)", open=False):
-                        desc1 = gr.Textbox(lines=4, placeholder=(
-                            "person: Full names\n"
-                            "title: Roles\n"
-                            "organization: Companies\n"
-                            "event: Conferences\n"
-                            "location: Cities\n"
-                            "date: Temporal expressions"
-                        ))
-                    btn1 = gr.Button("Predict", variant="primary")
+                    txt1 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to extract entities...")
+                    types1 = gr.Textbox(label="Entity Types (CSV)", value="person, organization, location, date, title, topic")
+                    with gr.Accordion("Optional Descriptions", open=False):
+                        desc1 = gr.Textbox(lines=3, placeholder="person: Full name\norganization: Companies\n...")
+                    btn1 = gr.Button("Extract Entities", variant="primary")
                 with gr.Column(scale=1):
-                    out1 = gr.Code(language="json", lines=8)
-                btn1.click(run_ner, [txt1, types1, desc1], out1)
+                    out1 = gr.Code(language="json", label="Results", lines=8)
+                btn1.click(run_ner, inputs=[txt1, types1, desc1], outputs=out1)
 
         # Classification Tab
-        with gr.TabItem("Text Classification"):
-            with gr.Row(elem_classes="card"):
+        with gr.TabItem("📝 Text Classification"):
+            with gr.Row(elem_classes="card", gap="small"):
                 with gr.Column(scale=2):
-                    txt2 = gr.Textbox(
-                        label="Text", lines=4,
-                        value=(
-                            "The Q2 2025 financial report shows a 15% revenue increase driven by cloud "
-                            "services, offset by a 12% rise in R&D costs. Overall sentiment is cautiously "
-                            "optimistic among stakeholders."
-                        )
-                    )
-                    task2 = gr.Textbox(label="Task", value="financial_sentiment")
-                    labs2 = gr.Textbox(label="Labels (csv)", value="positive, negative, neutral, mixed, uncertain")
-                    with gr.Accordion("Label Descriptions (opt)", open=False):
-                        desc2 = gr.Textbox(lines=3, placeholder=(
-                            "positive: Favorable outcomes\n"
-                            "negative: Concerns raised\n"
-                            "neutral: Balanced reporting"
-                        ))
-                    multi2 = gr.Checkbox(label="Multi-label?", value=True)
-                    btn2 = gr.Button("Predict", variant="primary")
+                    txt2 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to classify...")
+                    task2 = gr.Textbox(label="Task Name", value="sentiment_analysis")
+                    labs2 = gr.Textbox(label="Labels (CSV)", value="positive, negative, neutral")
+                    with gr.Accordion("Optional Label Descriptions", open=False):
+                        desc2 = gr.Textbox(lines=3, placeholder="positive: Positive sentiment\n...")
+                    multi2 = gr.Checkbox(label="Multi-label?", value=False)
+                    btn2 = gr.Button("Classify Text", variant="primary")
                 with gr.Column(scale=1):
-                    out2 = gr.Code(language="json", lines=8)
-                btn2.click(run_class, [txt2, task2, labs2, desc2, multi2], out2)
+                    out2 = gr.Code(language="json", label="Results", lines=8)
+                btn2.click(run_class, inputs=[txt2, task2, labs2, desc2, multi2], outputs=out2)
 
+        # Structure Extraction Tab
+        with gr.TabItem("📐 Structure Extraction"):
+            with gr.Row(elem_classes="card", gap="small"):
+                with gr.Column(scale=2):
+                    txt3 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text for structure extraction...")
+                    struct3 = gr.Code(language="json", label="Schema (JSON)", lines=8, value=json.dumps({
+                        "product": [
+                            "name::str::Product name and model",
+                            "price::str::Product price",
+                            "features::list::Key features",
+                            "category::str::Product category"
+                        ]
+                    }, indent=2))
+                    btn3 = gr.Button("Extract Structure", variant="primary")
+                with gr.Column(scale=1):
+                    out3 = gr.Code(language="json", label="Results", lines=8)
+                btn3.click(run_struct, inputs=[txt3, struct3], outputs=out3)
 
     demo.launch(share=False)