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| import pickle | |
| import gradio as gr | |
| import geopandas as gpd | |
| from shapely.geometry import Point | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| with open("earthquake_model.pkl", 'rb') as file: | |
| model = pickle.load(file) | |
| def time2num(x): | |
| try: | |
| (h, m, s) = str(x).split(':') | |
| result = int(h) * 3600 + int(m) * 60 + int(s) | |
| return result | |
| except: | |
| return 0 | |
| def date2num(x): | |
| try: | |
| (m, d, y) = str(x).split("/") | |
| result = int(y) * 365 + int(m) * 30 + int(d) | |
| return result | |
| except: | |
| return 0 | |
| def datetime2num(date_str, time_str): | |
| date_value = date2num(date_str) | |
| time_value = time2num(time_str) | |
| return date_value ,time_value | |
| def test(model, date_str, time_str,all_points): | |
| data_list = [] | |
| for lat, lon in all_points: | |
| date, time = datetime2num(date_str, time_str) | |
| data_list.append([date, time, lat, lon]) | |
| np_array = np.array(data_list) | |
| res = model.predict_proba(np_array) | |
| return res | |
| def create_geodf(all_points, model, date_str, time_str): | |
| res = test(model, date_str, time_str,all_points) | |
| data_list = [] | |
| for lat, lon in all_points: | |
| date, time = datetime2num(date_str, time_str) | |
| data_list.append([date, time, lat, lon]) | |
| np_array = np.array(data_list) | |
| df = pd.DataFrame(np_array, columns=['Date', 'Time', 'Latitude', 'Longitude']) | |
| df['Probability_2'] = [i[1] for i in res] | |
| df['geometry'] = [Point(lon, lat) for lat, lon in zip(df['Latitude'], df['Longitude'])] | |
| crs = "EPSG:4326" | |
| gdf = gpd.GeoDataFrame(df, crs=crs, geometry='geometry') | |
| return gdf | |
| def plot_func(date_str, time_str): | |
| min_latitude = -90 | |
| max_latitude = 90 | |
| latitude_step = 1 | |
| min_longitude = -180 | |
| max_longitude = 180 | |
| longitude_step = 1 | |
| latitudes = np.arange(min_latitude, max_latitude + latitude_step, latitude_step) | |
| longitudes = np.arange(min_longitude, max_longitude + longitude_step, longitude_step) | |
| all_points = np.array(np.meshgrid(latitudes, longitudes)).T.reshape(-1, 2) | |
| gdf = create_geodf(all_points, model, date_str, time_str) | |
| top = gdf.nlargest(100, 'Probability_2') | |
| world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')) | |
| fig, ax = plt.subplots(figsize=(12, 12)) | |
| ax.imshow(np.ones((180, 360)), cmap='gray', extent=[-180, 180, -90, 90]) | |
| world.plot(ax=ax, color='lightgray', edgecolor='black') | |
| top.plot(ax=ax, markersize=50, color='red', legend=True, alpha=0.5) | |
| plt.xlabel('Longitude') | |
| plt.ylabel('Latitude') | |
| plt.title('Possible Earthquake Map') | |
| plt.grid(True) | |
| return plt.gcf() | |
| inputs = [gr.inputs.Textbox(label="Date: (MM/DD/YYYY)"), gr.inputs.Textbox(label="Time: (HH:MM:SS) GMT-4")] | |
| gr.Interface(fn=plot_func, inputs=inputs, outputs="plot",debugging=True).launch() |