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Update app.py
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app.py
CHANGED
@@ -7,10 +7,10 @@ from dash import dcc, html, Input, Output, State
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import dash_bootstrap_components as dbc
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# Load CSV into DataFrame
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df = pd.read_csv('dashboard18.csv')
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# List of unique farm names for the dropdown
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farm_names = df['مزرعه'].unique()
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# Initialize the Dash app
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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@@ -44,11 +44,11 @@ def update_graph(selected_farm):
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return go.Figure(), "Please select a farm."
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# Filter data based on selected farm
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filtered_df = df[df['
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# Prepare 3D surface data
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weeks = np.arange(1, 19)
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heights = filtered_df.iloc[:,
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fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'surface'}]])
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@@ -56,7 +56,7 @@ def update_graph(selected_farm):
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go.Surface(
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z=heights,
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x=weeks,
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y=filtered_df['
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colorscale='Viridis'
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)
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)
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import dash_bootstrap_components as dbc
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# Load CSV into DataFrame
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df = pd.read_csv('/mnt/data/dashboard18.csv')
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# List of unique farm names for the dropdown
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farm_names = df[' مزرعه '].unique()
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# Initialize the Dash app
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app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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return go.Figure(), "Please select a farm."
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# Filter data based on selected farm
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filtered_df = df[df[' مزرعه '] == selected_farm]
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# Prepare 3D surface data
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weeks = np.arange(1, 19)
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heights = filtered_df.iloc[:, 6:24].values # Adjusted to match height columns
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fig = make_subplots(rows=1, cols=1, specs=[[{'type': 'surface'}]])
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go.Surface(
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z=heights,
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x=weeks,
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y=filtered_df[' مزرعه '].values,
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colorscale='Viridis'
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)
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)
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