Update app.py

app.py

@@ -75,6 +75,126 @@ header.brand .subtitle {
 }
 """
 
+# Pre-made examples for each task (5 per tab)
+ner_examples = [
+    [
+        "Barack Obama visited Berlin in July 2013.",
+        "person,location,date",
+        "person: Full name\nlocation: City\ndate: Month and year"
+    ],
+    [
+        "Apple released the iPhone 13 on September 14, 2021.",
+        "organization,product,date",
+        "organization: Company name\nproduct: Device name\ndate: Full date"
+    ],
+    [
+        "Elon Musk announced Tesla's new Roadster at the LA Auto Show.",
+        "person,organization,event,location",
+        "person: Full name\norganization: Company name\nevent: Conference or show\nlocation: Venue"
+    ],
+    [
+        "The UEFA Champions League Final takes place in Istanbul this year.",
+        "event,location,date",
+        "event: Sports event\nlocation: City\ndate: Year"
+    ],
+    [
+        "Microsoft acquired GitHub in 2018 for $7.5 billion.",
+        "organization,organization,date,price",
+        "organization: Company name\ndate: Year\nprice: Acquisition value"
+    ]
+]
+
+class_examples = [
+    [
+        "The movie was a thrilling experience with stunning visuals.",
+        "sentiment",
+        "positive,negative,neutral",
+        "positive: Positive sentiment\nnegative: Negative sentiment\nneutral: Mixed or neutral",
+        False
+    ],
+    [
+        "Our Q1 results were disappointing, with sales down 10%.",
+        "financial_sentiment",
+        "positive,negative,neutral",
+        "positive: Gains\nnegative: Losses\nneutral: Flat",
+        False
+    ],
+    [
+        "I love the new interface but dislike the slow loading time.",
+        "feedback",
+        "praise,complaint,suggestion",
+        "praise: Positive feedback\ncomplaint: Negative feedback\nsuggestion: Improvement ideas",
+        True
+    ],
+    [
+        "The product meets expectations but could use more features.",
+        "review",
+        "positive,negative",
+        "positive: Meets expectations\nnegative: Lacking",
+        False
+    ],
+    [
+        "Customer support was helpful, though response times were slow.",
+        "support_sentiment",
+        "positive,negative,neutral",
+        "positive: Helpful support\nnegative: Unhelpful support\nneutral: Mixed experiences",
+        True
+    ]
+]
+
+struct_examples = [
+    [
+        "The iPad Pro comes with an M1 chip, 8GB RAM, 256GB storage, and a 12.9-inch display.",
+        json.dumps({
+            "device": [
+                "name::str::Model name",
+                "specs::list::Hardware specifications",
+                "price::str::Device cost"
+            ]
+        }, indent=2)
+    ],
+    [
+        "Plan: Write report (Due: May 10), Review code (Due: May 15), Deploy (Due: May 20)",
+        json.dumps({
+            "tasks": [
+                "title::str::Task title",
+                "due_date::str::Due date"
+            ]
+        }, indent=2)
+    ],
+    [
+        "Product: Coffee Mug; Price: $12; Features: ceramic, dishwasher-safe, 12oz capacity.",
+        json.dumps({
+            "product": [
+                "name::str::Product name",
+                "price::str::Product price",
+                "features::list::Product features"
+            ]
+        }, indent=2)
+    ],
+    [
+        "Event: AI Conference; Date: August 22, 2025; Location: Paris; Topics: ML, Ethics, Robotics.",
+        json.dumps({
+            "event": [
+                "name::str::Event name",
+                "date::str::Event date",
+                "location::str::Event location",
+                "topics::list::Covered topics"
+            ]
+        }, indent=2)
+    ],
+    [
+        "Recipe: Pancakes; Ingredients: flour, eggs, milk; Steps: mix, cook, serve.",
+        json.dumps({
+            "recipe": [
+                "title::str::Recipe title",
+                "ingredients::list::List of ingredients",
+                "steps::list::Preparation steps"
+            ]
+        }, indent=2)
+    ]
+]
+
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"), css=custom_css) as demo:
     # Header
     gr.HTML(
@@ -91,11 +211,12 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
         with gr.TabItem("🔍 Named Entity Recognition"):
             with gr.Row(elem_classes="card"):
                 with gr.Column(scale=2):
-                    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")
+                    txt1 = gr.Textbox(label="Input Text", lines=5)
+                    types1 = gr.Textbox(label="Entity Types (CSV)")
                     with gr.Accordion("Optional Descriptions", open=False):
-                        desc1 = gr.Textbox(lines=3, placeholder="person: Full name\norganization: Companies\n...")
+                        desc1 = gr.Textbox(lines=3)
                     btn1 = gr.Button("Extract Entities", variant="primary")
+                    gr.Examples(examples=ner_examples, inputs=[txt1, types1, desc1], outputs=None, fn=lambda *args: None, cache_examples=False)
                 with gr.Column(scale=1):
                     out1 = gr.Code(language="json", label="Results", lines=8)
                 btn1.click(run_ner, inputs=[txt1, types1, desc1], outputs=out1)
@@ -104,13 +225,14 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
         with gr.TabItem("📝 Text Classification"):
             with gr.Row(elem_classes="card"):
                 with gr.Column(scale=2):
-                    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")
+                    txt2 = gr.Textbox(label="Input Text", lines=5)
+                    task2 = gr.Textbox(label="Task Name")
+                    labs2 = gr.Textbox(label="Labels (CSV)")
                     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)
+                        desc2 = gr.Textbox(lines=3)
+                    multi2 = gr.Checkbox(label="Multi-label?")
                     btn2 = gr.Button("Classify Text", variant="primary")
+                    gr.Examples(examples=class_examples, inputs=[txt2, task2, labs2, desc2, multi2], outputs=None, fn=lambda *args: None, cache_examples=False)
                 with gr.Column(scale=1):
                     out2 = gr.Code(language="json", label="Results", lines=8)
                 btn2.click(run_class, inputs=[txt2, task2, labs2, desc2, multi2], outputs=out2)
@@ -119,16 +241,10 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
         with gr.TabItem("📐 Structure Extraction"):
             with gr.Row(elem_classes="card"):
                 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))
+                    txt3 = gr.Textbox(label="Input Text", lines=5)
+                    struct3 = gr.Code(language="json", label="Schema (JSON)", lines=8)
                     btn3 = gr.Button("Extract Structure", variant="primary")
+                    gr.Examples(examples=struct_examples, inputs=[txt3, struct3], outputs=None, fn=lambda *args: None, cache_examples=False)
                 with gr.Column(scale=1):
                     out3 = gr.Code(language="json", label="Results", lines=8)
                 btn3.click(run_struct, inputs=[txt3, struct3], outputs=out3)