new exm

app.py

@@ -1,51 +1,66 @@
 import re
 import gradio as gr
 
-# Simulated GLiNER‐style output
+# Your actual GLiNER predictions
 SIM_ENTITIES = {
-    "Household Survey Data": "named dataset",
-    "Ethiopia":             "data geography",
-    "2020":                 "reference year",
-    "World Bank":           "publisher"
+    "Home Visits Survey":                       "named dataset",
+    "Jordan":                                    "data geography",
+    "Round II":                                  "version",
+    "HV":                                        "acronym",
+    "UNHCR":                                     "author",
+    "World Food Programme":                      "author",
+    "2013":                                      "reference year",
+    "2014":                                      "publication year",
+    "detailed socio-economic, health, and protection data": "data description",
 }
 
-def annotate_text(text):
+SAMPLE_TEXT = (
+    "The Jordan Home Visits Survey, Round II (HV), was carried out by UNHCR and the World Food "
+    "Programme between November 2013 and September 2014. Through in-home visits to Syrian refugee "
+    "households in Jordan, it gathered detailed socio-economic, health, and protection data—each "
+    "household tagged with a unique ID to allow longitudinal tracking."
+)
+
+def highlight_text(text):
     entities = []
     for phrase, label in SIM_ENTITIES.items():
         for m in re.finditer(re.escape(phrase), text):
             entities.append({
-                "entity": label,    # note: 'entity' key
+                "entity": label,
                 "start":  m.start(),
                 "end":    m.end(),
             })
     return {"text": text, "entities": entities}
 
 with gr.Blocks() as demo:
-    gr.Markdown("## Simulated GLiNER Highlighter\n"
-                "Type or paste a sentence and click **Highlight**.")
-
-    txt = gr.Textbox(
-        lines=3,
-        value="The Household Survey Data for Ethiopia in 2020 was published by the World Bank.",
+    gr.Markdown("## GLiNER NER + RE Highlighting\n"
+                "This demo uses your model’s actual predictions to annotate the sample sentence.\n\n"
+                "**Labels & spans** matching your `ner` and `relations` outputs will be colored below.")
+    
+    txt_in = gr.Textbox(
         label="Input Text",
-        placeholder="Enter any sentence here…"
+        lines=4,
+        value=SAMPLE_TEXT
     )
-    btn = gr.Button("Highlight")
-    out = gr.HighlightedText(label="Annotated Entities")
-
-    # fire on button click or Enter
-    btn.click(fn=annotate_text, inputs=txt, outputs=out)
-    txt.submit(fn=annotate_text, inputs=txt, outputs=out)
-    # initial render
-    demo.load(fn=annotate_text, inputs=txt, outputs=out)
-
+    btn = gr.Button("Highlight Entities")
+    txt_out = gr.HighlightedText(label="Annotated Entities")
+    
+    # wire up
+    btn.click(fn=highlight_text, inputs=txt_in, outputs=txt_out)
+    txt_in.submit(fn=highlight_text, inputs=txt_in, outputs=txt_out)
+    demo.load(fn=highlight_text, inputs=txt_in, outputs=txt_out)
+    
     gr.Markdown("""
 **Legend**  
-- **named dataset** → Household Survey Data  
-- **data geography** → Ethiopia  
-- **reference year** → 2020  
-- **publisher** → World Bank
+- **named dataset** → Home Visits Survey  
+- **data geography** → Jordan  
+- **version** → Round II  
+- **acronym** → HV  
+- **author** → UNHCR, World Food Programme  
+- **reference year** → 2013  
+- **publication year** → 2014  
+- **data description** → detailed socio-economic, health, and protection data
 """)
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    demo.launch()