Create app.py

app.py

@@ -0,0 +1,134 @@
+import streamlit as st
+import plotly.graph_objects as go
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+import requests
+
+def search_geonames(location):
+ api_endpoint = "http://api.geonames.org/searchJSON"
+ username = "zekun" 
+
+ params = {
+ 'q': location,
+ 'username': username,
+ 'maxRows': 5
+ }
+
+ response = requests.get(api_endpoint, params=params)
+ data = response.json()
+
+ if 'geonames' in data:
+ fig = go.Figure() 
+ for place_info in data['geonames']:
+ latitude = float(place_info.get('lat', 0.0))
+ longitude = float(place_info.get('lng', 0.0))
+
+ fig.add_trace(go.Scattermapbox(
+ lat=[latitude],
+ lon=[longitude],
+ mode='markers',
+ marker=go.scattermapbox.Marker(
+ size=10,
+ color='orange',
+ ),
+ text=[f'Location: {location}'],
+ hoverinfo="text",
+ hovertemplate='<b>Location</b>: %{text}',
+ ))
+
+ fig.update_layout(
+ mapbox_style="open-street-map",
+ hovermode='closest',
+ mapbox=dict(
+ bearing=0,
+ center=go.layout.mapbox.Center(
+ lat=latitude,
+ lon=longitude
+ ),
+ pitch=0,
+ zoom=2
+ ))
+
+ st.plotly_chart(fig)
+ 
+ # Return an empty figure
+ return go.Figure()
+
+
+def mapping(location):
+ st.write(f"Mapping location: {location}")
+
+ search_geonames(location)
+
+
+
+def generate_human_readable(tokens,labels):
+ ret = []
+ for t,lab in zip(tokens,labels):
+ if t == '[SEP]':
+ continue
+
+ if t.startswith("##") :
+ assert len(ret) > 0
+ ret[-1] = ret[-1] + t.strip('##')
+
+ elif lab==2:
+ assert len(ret) > 0
+ ret[-1] = ret[-1] + " "+ t.strip('##')
+ else:
+ ret.append(t)
+
+ return ret
+
+
+
+def showOnMap(input_sentence):
+ # get the location names: 
+
+ model_name = "zekun-li/geolm-base-toponym-recognition"
+
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
+ model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+ tokens = tokenizer.encode(input_sentence, return_tensors="pt")
+
+ outputs = model(tokens) 
+
+ predicted_labels = torch.argmax(outputs.logits, dim=2)
+
+ predicted_labels = predicted_labels.detach().cpu().numpy()
+
+ # "id2label": { "0": "O", "1": "B-Topo", "2": "I-Topo" }
+
+ predicted_labels = [model.config.id2label[label] for label in predicted_labels[0]]
+
+ predicted_labels = torch.argmax(outputs.logits, dim=2)
+
+ query_tokens = tokens[0][torch.where(predicted_labels[0] != 0)[0]]
+
+ query_labels = predicted_labels[0][torch.where(predicted_labels[0] != 0)[0]]
+
+ human_readable = generate_human_readable(tokenizer.convert_ids_to_tokens(query_tokens), query_labels)
+ #['Los Angeles', 'L . A .', 'California', 'U . S .', 'Southern California', 'Los Angeles', 'United States', 'New York City']
+
+ return human_readable
+
+
+
+
+
+def show_on_map():
+
+ input = st.text_area("Enter a sentence:", height=200)
+
+ st.button("Submit")
+
+ places = showOnMap(input)
+
+ selected_place = st.selectbox("Select a location:", places)
+ mapping(selected_place)
+
+
+
+if __name__ == "__main__":
+ show_on_map()