Update biomap/app.py

biomap/app.py

@@ -1,110 +1,110 @@
-from plot_functions import *
-import hydra
-
-import torch
-from model import LitUnsupervisedSegmenter
-from helper import inference_on_location_and_month, inference_on_location
-from plot_functions import segment_region
-
-from functools import partial
-import gradio as gr
-import logging
-
-import geopandas as gpd
-mapbox_access_token = "pk.eyJ1IjoiamVyZW15LWVraW1ldHJpY3MiLCJhIjoiY2xrNjBwNGU2MDRhMjNqbWw0YTJrbnpvNCJ9.poVyIzhJuJmD6ffrL9lm2w"
-geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
-
-def get_geomap(long, lat ):
-    
-
-    fig = go.Figure(go.Scattermapbox(
-            lat=geo_df.geometry.y,
-            lon=geo_df.geometry.x,
-            mode='markers',
-            marker=go.scattermapbox.Marker(
-                size=14
-            ),
-            text=geo_df.name,
-        ))
-    
-    fig.add_trace(go.Scattermapbox(lat=[lat],
-        lon=[long],
-        mode='markers',
-        marker=go.scattermapbox.Marker(
-            size=14
-        ),
-        marker_color="green",
-        text=['Actual position']))
-
-    fig.update_layout(
-        showlegend=False,
-        hovermode='closest',
-        mapbox=dict(
-            accesstoken=mapbox_access_token,
-            center=go.layout.mapbox.Center(
-                lat=lat,
-                lon=long
-            ),
-            zoom=3
-        )
-    )
-    
-    return fig
-
-
-if __name__ == "__main__":
-    
-    
-    logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.INFO)
-    # Initialize hydra with configs
-    #hydra.initialize(config_path="configs", job_name="corine")
-    cfg = hydra.compose(config_name="my_train_config.yml")
-    logging.info(f"config : {cfg}")
-    # Load the model
-
-    nbclasses = cfg.dir_dataset_n_classes
-    model = LitUnsupervisedSegmenter(nbclasses, cfg)
-    logging.info(f"Model Initialiazed")
-    
-    model_path = "checkpoint/model/model.pt"
-    saved_state_dict = torch.load(model_path, map_location=torch.device("cpu"))
-    logging.info(f"Model weights Loaded")
-    model.load_state_dict(saved_state_dict)
-    logging.info(f"Model Loaded")
-# css=".VIDEO video{height: 100%;width:50%;margin:auto};.VIDEO{height: 50%;};.svelte-1vnmhm4{height:auto}"
-    with gr.Blocks() as demo:
-        gr.Markdown("Estimate Biodiversity in the world.")
-        with gr.Tab("Single Image"):
-            with gr.Row():
-                input_map = gr.Plot().style()  
-                with gr.Column():
-                    input_latitude = gr.Number(label="lattitude", value=2.98)
-                    input_longitude = gr.Number(label="longitude", value=48.81)
-                    input_date = gr.Textbox(label="start_date", value="2020-03-20")
-
-            single_button = gr.Button("Predict")
-            with gr.Row():
-                raw_image = gr.Image(label = "Localisation visualization")
-                output_image = gr.Image(label = "Labeled visualisation")
-                score_biodiv = gr.Number(label = "Biodiversity score")
-
-        with gr.Tab("TimeLapse"):
-            with gr.Row():
-                input_map_2 = gr.Plot().style() 
-            with gr.Row():
-                timelapse_input_latitude = gr.Number(value=2.98, label="Latitude")
-                timelapse_input_longitude = gr.Number(value=48.81, label="Longitude")
-                timelapse_start_date = gr.Textbox(value='2020-05-01', label="Start Date")
-                timelapse_end_date = gr.Textbox(value='2020-06-30', label="End Date")
-            segmentation = gr.CheckboxGroup(choices=['month', 'year', '2months'], value=['month'], label="Select Segmentation Level:")
-            timelapse_button = gr.Button(value="Predict")     
-            map = gr.Plot().style()
-        
-        demo.load(get_geomap, [input_latitude, input_longitude], input_map)
-        single_button.click(get_geomap, [input_latitude, input_longitude], input_map)
-        single_button.click(partial(inference_on_location_and_month, model), inputs=[input_latitude, input_longitude, input_date], outputs=[raw_image, output_image,score_biodiv])
-
-        demo.load(get_geomap, [timelapse_input_latitude, timelapse_input_longitude], input_map_2)
-        timelapse_button.click(get_geomap, [timelapse_input_latitude, timelapse_input_longitude], input_map_2)
-        timelapse_button.click(segment_region, inputs=[timelapse_input_latitude, timelapse_input_longitude, timelapse_start_date, timelapse_end_date,segmentation], outputs=[map])
-    demo.launch(share=True)
+from plot_functions import *
+import hydra
+
+import torch
+from model import LitUnsupervisedSegmenter
+from helper import inference_on_location_and_month, inference_on_location
+from plot_functions import segment_region
+
+from functools import partial
+import gradio as gr
+import logging
+
+import geopandas as gpd
+mapbox_access_token = "pk.eyJ1IjoiamVyZW15LWVraW1ldHJpY3MiLCJhIjoiY2xrNjBwNGU2MDRhMjNqbWw0YTJrbnpvNCJ9.poVyIzhJuJmD6ffrL9lm2w"
+geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
+
+def get_geomap(long, lat ):
+    
+
+    fig = go.Figure(go.Scattermapbox(
+            lat=geo_df.geometry.y,
+            lon=geo_df.geometry.x,
+            mode='markers',
+            marker=go.scattermapbox.Marker(
+                size=14
+            ),
+            text=geo_df.name,
+        ))
+    
+    fig.add_trace(go.Scattermapbox(lat=[lat],
+        lon=[long],
+        mode='markers',
+        marker=go.scattermapbox.Marker(
+            size=14
+        ),
+        marker_color="green",
+        text=['Actual position']))
+
+    fig.update_layout(
+        showlegend=False,
+        hovermode='closest',
+        mapbox=dict(
+            accesstoken=mapbox_access_token,
+            center=go.layout.mapbox.Center(
+                lat=lat,
+                lon=long
+            ),
+            zoom=3
+        )
+    )
+    
+    return fig
+
+
+if __name__ == "__main__":
+    
+    
+    logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.INFO)
+    # Initialize hydra with configs
+    #hydra.initialize(config_path="configs", job_name="corine")
+    cfg = hydra.compose(config_name="my_train_config.yml")
+    logging.info(f"config : {cfg}")
+    # Load the model
+
+    nbclasses = cfg.dir_dataset_n_classes
+    model = LitUnsupervisedSegmenter(nbclasses, cfg)
+    logging.info(f"Model Initialiazed")
+    
+    model_path = "biomap/checkpoint/model/model.pt"
+    saved_state_dict = torch.load(model_path, map_location=torch.device("cpu"))
+    logging.info(f"Model weights Loaded")
+    model.load_state_dict(saved_state_dict)
+    logging.info(f"Model Loaded")
+# css=".VIDEO video{height: 100%;width:50%;margin:auto};.VIDEO{height: 50%;};.svelte-1vnmhm4{height:auto}"
+    with gr.Blocks() as demo:
+        gr.Markdown("Estimate Biodiversity in the world.")
+        with gr.Tab("Single Image"):
+            with gr.Row():
+                input_map = gr.Plot().style()  
+                with gr.Column():
+                    input_latitude = gr.Number(label="lattitude", value=2.98)
+                    input_longitude = gr.Number(label="longitude", value=48.81)
+                    input_date = gr.Textbox(label="start_date", value="2020-03-20")
+
+            single_button = gr.Button("Predict")
+            with gr.Row():
+                raw_image = gr.Image(label = "Localisation visualization")
+                output_image = gr.Image(label = "Labeled visualisation")
+                score_biodiv = gr.Number(label = "Biodiversity score")
+
+        with gr.Tab("TimeLapse"):
+            with gr.Row():
+                input_map_2 = gr.Plot().style() 
+            with gr.Row():
+                timelapse_input_latitude = gr.Number(value=2.98, label="Latitude")
+                timelapse_input_longitude = gr.Number(value=48.81, label="Longitude")
+                timelapse_start_date = gr.Textbox(value='2020-05-01', label="Start Date")
+                timelapse_end_date = gr.Textbox(value='2020-06-30', label="End Date")
+            segmentation = gr.CheckboxGroup(choices=['month', 'year', '2months'], value=['month'], label="Select Segmentation Level:")
+            timelapse_button = gr.Button(value="Predict")     
+            map = gr.Plot().style()
+        
+        demo.load(get_geomap, [input_latitude, input_longitude], input_map)
+        single_button.click(get_geomap, [input_latitude, input_longitude], input_map)
+        single_button.click(partial(inference_on_location_and_month, model), inputs=[input_latitude, input_longitude, input_date], outputs=[raw_image, output_image,score_biodiv])
+
+        demo.load(get_geomap, [timelapse_input_latitude, timelapse_input_longitude], input_map_2)
+        timelapse_button.click(get_geomap, [timelapse_input_latitude, timelapse_input_longitude], input_map_2)
+        timelapse_button.click(segment_region, inputs=[timelapse_input_latitude, timelapse_input_longitude, timelapse_start_date, timelapse_end_date,segmentation], outputs=[map])
+    demo.launch(share=True)