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()