Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import gradio as gr | |
| import json | |
| from gliner2 import GLiNER2 | |
| from huggingface_hub import login | |
| import os | |
| # 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): | |
| types = [t.strip() for t in types_csv.split(",") if t.strip()] | |
| desc_map = {k: v for line in descs.split("\n") if ":" in line for k,v in [line.split(":",1)]} | |
| inp = desc_map if desc_map else types | |
| 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)]} | |
| inp = desc_map if desc_map else labels | |
| tasks = { | |
| task: { | |
| "labels": list(inp.keys()) if isinstance(inp,dict) else inp, | |
| "multi_label": multi, | |
| **({"label_descriptions": inp} if isinstance(inp,dict) else {}) | |
| } | |
| } | |
| 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) | |
| except json.JSONDecodeError as e: | |
| return f"❌ Invalid JSON: {e}" | |
| res = model.extract_json(text=text, structures=cfg, include_confidence=True) | |
| return model.pretty_print_results(res, include_confidence=True) | |
| # Simplified CSS - uses default backgrounds | |
| custom_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap'); | |
| body { | |
| font-family: 'Inter', sans-serif; | |
| } | |
| header.brand { | |
| padding: 2rem 0; | |
| text-align: center; | |
| } | |
| header.brand .logo { | |
| font-size: 2rem; | |
| font-weight: 700; | |
| color: #4f46e5; | |
| } | |
| header.brand .subtitle { | |
| margin-top: 0.2rem; | |
| font-size: 0.9rem; | |
| color: #6b7280; | |
| } | |
| .gr-button.primary { | |
| background: #4f46e5 !important; | |
| color: #fff !important; | |
| border-radius: 0.5rem; | |
| padding: 0.6rem 1.2rem; | |
| } | |
| """ | |
| # 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( | |
| """ | |
| <header class=\"brand\"> | |
| <div class=\"logo\">✨ GLiNER2</div> | |
| </header> | |
| """ | |
| ) | |
| with gr.Tabs(): | |
| # NER Tab | |
| 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) | |
| types1 = gr.Textbox(label="Entity Types (CSV)") | |
| with gr.Accordion("Optional Descriptions", open=False): | |
| 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) | |
| # Classification Tab | |
| with gr.TabItem("📝 Text Classification"): | |
| with gr.Row(elem_classes="card"): | |
| with gr.Column(scale=2): | |
| 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) | |
| 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) | |
| # Structure Extraction Tab | |
| with gr.TabItem("📐 Structure Extraction"): | |
| with gr.Row(elem_classes="card"): | |
| with gr.Column(scale=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) | |
| demo.launch(share=False, width=800) | |