Create app.py

app.py

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+import gradio as gr
+# import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+from scipy.stats import multivariate_normal as mvn
+import random
+
+
+AIRTABLE_TOKEN = os.environ.get("AIRTABLE_TOKEN")
+SUMMARY_TABLE_URL = os.environ.get("SUMMARY_TABLE_URL")
+
+def send_to_airtable(data, url):
+    headers = {
+        "Authorization": f"Bearer {AIRTABLE_TOKEN}",
+        "Content-Type": "application/json"
+    }
+    data = json.dumps(data)
+    response = requests.post(url, headers=headers, data=data)
+    print(response.text)
+    return response.text
+
+def log_final_decision(group_name, total_yield, injection, production, url):
+    data = {"fields": {"group_name": group_name, "total_yield": total_yield, "injection": injection, "production": production}}
+    return send_to_airtable(data, url)
+
+
+df = pd.read_csv('volcs.csv')
+cities = ["Serang", "Bogor", "Jogja", "Bandung", "Tasikmalaya", "Purwokerto", "Semarang", "Malang", "Banyuwangi", "Tarogong", "Temanggung"]
+
+gammas = [0.9, 0.9, 0.93, 0.89, 0.81, 0.90, 0.98, 0.99, 0.89, 0.85, 0.80]
+cities_lats = [-6.12028, -6.59444, -7.7956, -6.9175, -7.3268, -7.4214, -6.96667, -7.9839, -8.2186, -7.3178564, -6.3929]
+cities_lons = [106.15028, 106.78917, 110.3644, 107.6191, 108.2142, 109.2341, 110.4208, 112.6315, 114.3695, 110.1779144, 105.8391]
+
+
+sigma = 0.05
+sigma_diag1 = 0.01
+sigma_diag2 = 0.005
+offset = 1
+num_grid = 800
+Sigma = [[sigma, sigma_diag1], [sigma_diag2, sigma]]
+
+# min_lat, max_lat = df['Latitude'].min() - offset, df['Latitude'].max() + offset
+# min_lon, max_lon = df['Longitude'].min() - offset, df['Longitude'].max() + offset
+
+# lats = np.linspace(min_lat, max_lat, num_grid)
+# lons = np.linspace(min_lon, max_lon, num_grid)
+# LON, LAT = np.meshgrid(lons, lats)
+
+# density = np.zeros(LAT.shape)
+# for idx, row in df.iterrows():
+#     lat, lon = row['Latitude'], row['Longitude']
+#     gaussian = mvn(mean=[lat, lon], cov=Sigma)
+#     density += gaussian.pdf(np.dstack((LAT, LON)))
+    
+# lon_points = LON.ravel().tolist()
+# lat_points = LAT.ravel().tolist()
+# density_points = density.ravel().tolist()
+
+def get_density(city):
+    idx = cities.index(city)
+    lat, lon = cities_lats[idx], cities_lons[idx]
+    density = 0
+    for idx, row in df.iterrows():
+        lat2, lon2 = row['Latitude'], row['Longitude']
+        gaussian = mvn(mean=[lat2, lon2], cov=Sigma)
+        density += gaussian.pdf([lat, lon])
+    return density
+
+
+def get_energy_yield(production, injection, group, length=30):
+    density = get_density(production)
+    gamma = gammas[cities.index(production)]
+    dist = random.uniform(0.98, 1.02)
+    
+    yield_dict = {"Year": [], "yield": []}
+    for i in range(1, length):
+        yield_dict["Year"].append(i)
+        yield_dict["yield"].append((10*density/dist) * gamma**i)
+    df = pd.DataFrame(yield_dict)    
+    
+    plt = gr.ScatterPlot(
+        value = df,
+        x = "Year",
+        y = "yield",
+        title = "Energy Yield Over Time",
+        x_title = "Year",
+        y_title = "Energy Yield (MWh)"
+    )
+    total_yield = sum(yield_dict['yield'])
+    total_text = f"Total Energy Yield for 30 years: {total_yield:.2f} MWh"
+    
+    #if group is empty, return warning as text label
+    if group == "":
+        total_text = "Please enter your group number"
+        plt = gr.ScatterPlot()
+        return total_text, plt
+    else:
+        log_final_decision(group, total_yield, injection, production, SUMMARY_TABLE_URL)
+        return total_text, plt
+    
+    
+
+
+# def draw_map():
+#     fig = go.Figure()
+
+#     fig.add_trace(go.Densitymapbox(
+#         lat=lat_points,
+#         lon=lon_points,
+#         z=density_points,
+#         radius=2,
+#         colorscale='Jet',
+#         opacity=0.5, 
+#         hoverinfo="skip",
+#     ))
+
+#     fig.add_trace(go.Scattermapbox(
+#         lat=cities_lats,
+#         lon=cities_lons,
+#         mode='markers',
+#         marker=go.scattermapbox.Marker(color="black"),
+#         text=cities,  
+#         hoverinfo="text"  
+#     ))
+
+
+#     fig.update_layout(
+#         title='Geothermal Potentials in Java, Indonesia (Old Data)',
+#         mapbox_style="open-street-map",
+#         mapbox=dict(
+#             center=dict(lat=(min_lat + max_lat) / 2, lon=(min_lon + max_lon) / 2),
+#             zoom=6
+#         ),
+#         margin=dict(l=0, r=0, t=0, b=0)
+        
+#     )
+
+
+#     return fig
+
+with gr.Blocks() as demo:   
+    
+    
+    gr.Label("Geothermal Potential in Java, Indonesia")
+    gr.HTML("<center><img src='https://analytics-project-simt-its.github.io/assets/img/geothermal_map.png'></center>")
+    gr.HTML("Interactive map: <a href='https://analytics-project-simt-its.github.io/geothermal_map.html'>here</a>")
+#     map = gr.Plot()     
+    
+    with gr.Row():        
+        injection = gr.Radio(cities, label="Location", info="Choose injection well location"),
+        production = gr.Radio(cities, label="Location", info="Choose production well location")
+            
+            
+    with gr.Row():
+        group = gr.Textbox(label="Group number", info="Enter your group number")
+        btn = gr.Button("Build Geothermal Wells")
+        
+    with gr.Row():
+        energy = gr.Label()
+        energy_30 = gr.ScatterPlot()
+            
+        
+    # demo.load(draw_map, [], map)
+    demo.load()
+    btn.click(
+        get_energy_yield, 
+        inputs = [production, injection, group],
+        outputs = [energy, energy_30]
+        )    
+    
+    
+    
+
+demo.launch()