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feat: add osm data

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bernard-ng
2025-09-21 16:23:44 +02:00
parent 63e23d6600
commit c1b502c878
39 changed files with 955 additions and 338 deletions
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web/interfaces/ner_testing.py
+20 -28
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@@ -33,7 +33,9 @@ class NERTesting:
# Load model
if not self.load_ner_model():
st.warning("NER model could not be loaded. Please ensure the model is trained and available.")
st.warning(
"NER model could not be loaded. Please ensure the model is trained and available."
)
return
# Display model information
@@ -53,9 +55,11 @@ class NERTesting:
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric("Training Examples", f"{self.training_stats.get('training_examples', 0):,}")
st.metric(
"Training Examples", f"{self.training_stats.get('training_examples', 0):,}"
)
with col2:
st.metric("Epochs", self.training_stats.get('epochs', 0))
st.metric("Epochs", self.training_stats.get("epochs", 0))
with col3:
st.metric("Final Loss", f"{self.training_stats.get('final_loss', 0):.2f}")
with col4:
@@ -64,7 +68,7 @@ class NERTesting:
def show_model_evaluation_info(self):
if self.evaluation_stats:
col1, col2, col3 = st.columns(4)
overall = self.evaluation_stats.get('overall', {})
overall = self.evaluation_stats.get("overall", {})
with col1:
st.metric("Overall Precision", f"{overall['precision']:.2f}")
@@ -79,7 +83,7 @@ class NERTesting:
name_input = st.text_input(
"Name:",
placeholder="e.g., Jean Baptiste Mukendi, Marie Kabamba Tshiala, Joseph Kasongo",
help="Enter a full name or multiple names separated by spaces"
help="Enter a full name or multiple names separated by spaces",
)
if name_input.strip():
if st.button("Analyze Name", type="primary"):
@@ -90,12 +94,12 @@ class NERTesting:
"Names:",
placeholder="Jean Baptiste Mukendi\nMarie Kabamba Tshiala\nJoseph Kasongo\nGrace Mbuyi Kalala",
height=150,
help="Enter each name on a new line"
help="Enter each name on a new line",
)
if names_input.strip():
if st.button("Analyze All Names", type="primary"):
names = [name.strip() for name in names_input.split('\n') if name.strip()]
names = [name.strip() for name in names_input.split("\n") if name.strip()]
for i, name in enumerate(names):
st.markdown(f"**Name {i+1}: {name}**")
self.analyze_and_display(name)
@@ -106,12 +110,12 @@ class NERTesting:
try:
result = self.ner_model.predict(text)
st.subheader("Analysis Results")
entities = result.get('entities', [])
entities = result.get("entities", [])
if entities:
self.show_visual_entities(text, entities)
native_count = sum(1 for e in entities if e['label'] == 'NATIVE')
surname_count = sum(1 for e in entities if e['label'] == 'SURNAME')
native_count = sum(1 for e in entities if e["label"] == "NATIVE")
surname_count = sum(1 for e in entities if e["label"] == "SURNAME")
col1, col2, col3 = st.columns(3)
with col1:
@@ -134,29 +138,17 @@ class NERTesting:
# Convert our entities format to spaCy format for displacy
ents = []
for entity in entities:
ents.append({
"start": entity['start'],
"end": entity['end'],
"label": entity['label']
})
ents.append(
{"start": entity["start"], "end": entity["end"], "label": entity["label"]}
)
# Create doc-like structure for displacy
doc_data = {
"text": text,
"ents": ents,
"title": None
}
doc_data = {"text": text, "ents": ents, "title": None}
# Custom colors for our labels
colors = {
"NATIVE": "#74C0FC", # Light blue
"SURNAME": "#69DB7C" # Light green
}
colors = {"NATIVE": "#74C0FC", "SURNAME": "#69DB7C"} # Light blue # Light green
options = {
"colors": colors,
"distance": 90
}
options = {"colors": colors, "distance": 90}
# Generate HTML visualization
html = displacy.render(doc_data, style="ent", manual=True, options=options)