Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
Update app.py
Browse files
app.py
CHANGED
@@ -8,6 +8,7 @@ import os
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hf_token = os.getenv("HF_TOKEN")
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login(hf_token)
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model = GLiNER2.from_pretrained("fastino/gliner2-base-0207")
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def run_ner(text, types_csv, descs):
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@@ -82,6 +83,8 @@ header.brand .subtitle {
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border-radius: 0.75rem;
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box-shadow: 0 4px 10px rgba(0,0,0,0.05);
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margin-bottom: 1.5rem;
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}
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.gr-button.primary {
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background: var(--primary) !important;
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@@ -105,7 +108,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
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with gr.Tabs():
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# NER Tab
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with gr.TabItem("π Named Entity Recognition"):
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with gr.Row(elem_classes="card"
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with gr.Column(scale=2):
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txt1 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to extract entities...")
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types1 = gr.Textbox(label="Entity Types (CSV)", value="person, organization, location, date, title, topic")
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@@ -118,7 +121,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
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# Classification Tab
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with gr.TabItem("π Text Classification"):
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with gr.Row(elem_classes="card"
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with gr.Column(scale=2):
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txt2 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to classify...")
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task2 = gr.Textbox(label="Task Name", value="sentiment_analysis")
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@@ -133,7 +136,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="blue"),
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# Structure Extraction Tab
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with gr.TabItem("π Structure Extraction"):
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with gr.Row(elem_classes="card"
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with gr.Column(scale=2):
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txt3 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text for structure extraction...")
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struct3 = gr.Code(language="json", label="Schema (JSON)", lines=8, value=json.dumps({
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hf_token = os.getenv("HF_TOKEN")
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login(hf_token)
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# Load model once
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model = GLiNER2.from_pretrained("fastino/gliner2-base-0207")
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def run_ner(text, types_csv, descs):
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border-radius: 0.75rem;
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box-shadow: 0 4px 10px rgba(0,0,0,0.05);
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margin-bottom: 1.5rem;
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display: grid;
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grid-gap: 1rem;
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}
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.gr-button.primary {
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background: var(--primary) !important;
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with gr.Tabs():
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# NER Tab
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with gr.TabItem("π Named Entity Recognition"):
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with gr.Row(elem_classes="card"):
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with gr.Column(scale=2):
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txt1 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to extract entities...")
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types1 = gr.Textbox(label="Entity Types (CSV)", value="person, organization, location, date, title, topic")
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# Classification Tab
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with gr.TabItem("π Text Classification"):
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with gr.Row(elem_classes="card"):
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with gr.Column(scale=2):
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txt2 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text to classify...")
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task2 = gr.Textbox(label="Task Name", value="sentiment_analysis")
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# Structure Extraction Tab
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with gr.TabItem("π Structure Extraction"):
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with gr.Row(elem_classes="card"):
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with gr.Column(scale=2):
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txt3 = gr.Textbox(label="Input Text", lines=5, placeholder="Enter text for structure extraction...")
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struct3 = gr.Code(language="json", label="Schema (JSON)", lines=8, value=json.dumps({
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