diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000..011f535 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,33 @@ +{ + "name": "Python 3", + // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile + "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye", + "customizations": { + "codespaces": { + "openFiles": [ + "README.md", + "submissions/Raushan/main.py" + ] + }, + "vscode": { + "settings": {}, + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance" + ] + } + }, + "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y =6.1.2", + "scikit-learn>=1.7.0", + "seaborn>=0.13.2", + "streamlit>=1.46.0", +] diff --git a/submissions/Raushan/requirements.txt b/submissions/Raushan/requirements.txt new file mode 100644 index 0000000..e440893 --- /dev/null +++ b/submissions/Raushan/requirements.txt @@ -0,0 +1,49 @@ +altair==5.5.0 +attrs==25.3.0 +blinker==1.9.0 +cachetools==6.1.0 +certifi==2025.6.15 +charset-normalizer==3.4.2 +click==8.2.1 +contourpy==1.3.2 +cycler==0.12.1 +fonttools==4.58.4 +gitdb==4.0.12 +gitpython==3.1.44 +idna==3.10 +jinja2==3.1.6 +joblib==1.5.1 +jsonschema==4.24.0 +jsonschema-specifications==2025.4.1 +kiwisolver==1.4.8 +markupsafe==3.0.2 +matplotlib==3.10.3 +narwhals==1.43.1 +numpy==2.3.0 +packaging==25.0 +pandas==2.3.0 +pillow==11.2.1 +plotly==6.1.2 +protobuf==6.31.1 +pyarrow==18.1.0 +pydeck==0.9.1 +pyparsing==3.2.3 +python-dateutil==2.9.0.post0 +pytz==2025.2 +referencing==0.36.2 +requests==2.32.4 +rpds-py==0.25.1 +scikit-learn==1.7.0 +scipy==1.15.3 +seaborn==0.13.2 +six==1.17.0 +smmap==5.0.2 +streamlit==1.46.0 +tenacity==9.1.2 +threadpoolctl==3.6.0 +toml==0.10.2 +tornado==6.5.1 +typing-extensions==4.14.0 +tzdata==2025.2 +urllib3==2.5.0 +watchdog==6.0.0 diff --git a/submissions/Raushan/utils/analytics.py b/submissions/Raushan/utils/analytics.py new file mode 100644 index 0000000..50a4d9e --- /dev/null +++ b/submissions/Raushan/utils/analytics.py @@ -0,0 +1,135 @@ +# utils/analytics.py + +import streamlit as st +import plotly.express as px +import pandas as pd +import numpy as np +from sklearn.linear_model import LinearRegression +from scipy.stats import zscore + + +# Main function to display all AI-powered analytics sections +def show_analytics(df: pd.DataFrame): + st.header("๐Ÿค– AI-Powered Analytics") + show_trend_prediction(df) # Display trend prediction section + st.markdown("---") # Separator line + show_anomaly_detection(df) # Display anomaly detection section + st.markdown("---") # Separator line + show_correlation_analysis(df) # Display correlation analysis section + + +# Function to perform and display trend prediction for a selected city and metric +def show_trend_prediction(df): + st.subheader("๐Ÿ”ฎ Trend Prediction") + + # User selects city for trend prediction + trend_city = st.selectbox("Select City for Trend Prediction", df["City"].unique()) + + # User selects metric for trend prediction (excluding 'City' and 'Year' columns) + trend_metric = st.selectbox( + "Select Metric", + [col for col in df.columns if col not in ["City", "Year"]], + key="trend", + ) + + # Filter data for the selected city and metric, dropping missing values + df_trend = df[df["City"] == trend_city][["Year", trend_metric]].dropna() + + # Only proceed if at least 2 data points available to build a regression model + if len(df_trend) >= 2: + X = df_trend["Year"].values.reshape( + -1, 1 + ) # Predictor: Years reshaped for sklearn + y = df_trend[trend_metric].values # Target: metric values + + # Fit a linear regression model to historical data + model = LinearRegression().fit(X, y) + + # Prepare future years for prediction (next 5 years after max year in dataset) + future_years = np.arange(df["Year"].max() + 1, df["Year"].max() + 6).reshape( + -1, 1 + ) + + # Predict metric values for future years using the fitted model + predictions = model.predict(future_years) + + # Create DataFrame for future predictions + df_future = pd.DataFrame( + {"Year": future_years.flatten(), trend_metric: predictions} + ) + + # Combine historical and future data for plotting + df_combined = pd.concat([df_trend, df_future]) + + # Plot line chart with actual and predicted data + fig = px.line( + df_combined, + x="Year", + y=trend_metric, + title=f"Trend Prediction for {trend_metric} in {trend_city}", + ) + st.plotly_chart(fig, use_container_width=True) + else: + # Show warning if not enough data points to build a prediction model + st.warning("Not enough data for prediction.") + + +# Function to detect and display anomalies in selected metric using Z-score method +def show_anomaly_detection(df): + st.subheader("๐Ÿšจ Anomaly Detection") + + # User selects metric to analyze for anomalies (excluding 'City' and 'Year') + anomaly_metric = st.selectbox( + "Select Metric for Anomaly Detection", + [col for col in df.columns if col not in ["City", "Year"]], + key="anomaly", + ) + + # Make a copy of the DataFrame to add z-score column + anomaly_df = df.copy() + + # Calculate Z-score for the selected metric grouped by city + anomaly_df["z_score"] = anomaly_df.groupby("City")[anomaly_metric].transform(zscore) + + # Identify anomalies where absolute z-score > 2 (statistically significant) + anomalies = anomaly_df[anomaly_df["z_score"].abs() > 2] + + if not anomalies.empty: + # Display table of detected anomalies with relevant columns + st.dataframe( + anomalies[["City", "Year", anomaly_metric, "z_score"]], + use_container_width=True, + ) + else: + # Inform user if no significant anomalies were found + st.info("No significant anomalies found.") + + +# Function to display correlation analysis for selected numeric metric against others +def show_correlation_analysis(df): + st.subheader("๐Ÿ“Š Correlation Analysis") + + # Get list of numeric columns, excluding 'Year' + numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() + if "Year" in numeric_cols: + numeric_cols.remove("Year") + + # User selects metric to correlate against other numeric metrics + selected_corr_metric = st.selectbox( + "Select Metric to Correlate", + numeric_cols, + key="corr_analysis", + ) + + # Select numeric columns only for correlation matrix calculation + corr_df = df.select_dtypes(include=[np.number]) + + # Calculate correlation values of all numeric metrics with the selected metric + corr_values = corr_df.corr()[selected_corr_metric].sort_values(ascending=False) + + # Display the correlation results as a dataframe with proper column names + st.dataframe( + corr_values.reset_index().rename( + columns={"index": "Metric", selected_corr_metric: "Correlation"} + ) + ) diff --git a/submissions/Raushan/utils/constants.py b/submissions/Raushan/utils/constants.py new file mode 100644 index 0000000..7800900 --- /dev/null +++ b/submissions/Raushan/utils/constants.py @@ -0,0 +1,18 @@ +CITY_COORDS = { + "Mumbai": (19.0760, 72.8777), + "Delhi": (28.6139, 77.2090), + "Bangalore": (12.9716, 77.5946), + "Hyderabad": (17.3850, 78.4867), + "Chennai": (13.0827, 80.2707), + "Kolkata": (22.5726, 88.3639), + "Pune": (18.5204, 73.8567), + "Ahmedabad": (23.0225, 72.5714), + "Jaipur": (26.9124, 75.7873), + "Lucknow": (26.8467, 80.9462), +} + +POLICY_SUGGESTIONS = { + "HDI": "Invest in education and healthcare to improve quality of life.", + "Gini Coefficient": "Implement income redistribution policies to reduce inequality.", + "Renewable Energy %": "Subsidize solar and wind energy projects for sustainability.", +} diff --git a/submissions/Raushan/utils/data_loader.py b/submissions/Raushan/utils/data_loader.py new file mode 100644 index 0000000..146f26a --- /dev/null +++ b/submissions/Raushan/utils/data_loader.py @@ -0,0 +1,9 @@ +import os +import pandas as pd +import streamlit as st + +@st.cache_data +def load_data(filename: str): + base_path = os.path.dirname(__file__) + full_path = os.path.join(base_path, "..", filename) + return pd.read_csv(full_path) diff --git a/submissions/Raushan/utils/filters.py b/submissions/Raushan/utils/filters.py new file mode 100644 index 0000000..19366af --- /dev/null +++ b/submissions/Raushan/utils/filters.py @@ -0,0 +1,50 @@ +import streamlit as st +import pandas as pd + + +# Sidebar filters for user inputs to filter the dataset +def sidebar_filters(df: pd.DataFrame): + st.sidebar.header("๐Ÿ” Filter Options") # Sidebar section header + + # Multi-select box for selecting cities from unique cities in the data + cities = st.sidebar.multiselect( + "Select Cities", options=sorted(df["City"].unique()) + ) + + # Multi-select box for selecting one or more metrics (exclude 'City' and 'Year') + metrics = st.sidebar.multiselect( + "Select Metrics", + options=[col for col in df.columns if col not in ["City", "Year"]], + ) + + # Slider to select a range of years within the dataset's min and max year + year_range = st.sidebar.slider( + "Select Year Range", + int(df["Year"].min()), + int(df["Year"].max()), + (int(df["Year"].min()), int(df["Year"].max())), + ) + + # Apply the filtering based on user selections and return filtered data and selections + df_filtered = filter_data(df, cities, metrics, year_range[0], year_range[1]) + return df_filtered, metrics, cities, year_range + + +# Function to filter data based on selected cities, metrics, and year range +def filter_data(df: pd.DataFrame, cities, metrics, year_start, year_end): + df_filtered = df.copy() + + # Filter rows to keep only selected cities if any selected + if cities: + df_filtered = df_filtered[df_filtered["City"].isin(cities)] + + # Filter rows for years within selected range + df_filtered = df_filtered[ + (df_filtered["Year"] >= year_start) & (df_filtered["Year"] <= year_end) + ] + + # If metrics selected, keep only 'City', 'Year', and those metrics columns + if metrics: + df_filtered = df_filtered[["City", "Year"] + metrics] + + return df_filtered diff --git a/submissions/Raushan/utils/insights.py b/submissions/Raushan/utils/insights.py new file mode 100644 index 0000000..93bd390 --- /dev/null +++ b/submissions/Raushan/utils/insights.py @@ -0,0 +1,163 @@ +import streamlit as st +import pandas as pd +from .constants import POLICY_SUGGESTIONS + + +# Show top N cities for a selected metric in a given year +def show_top_cities_by_metric(df: pd.DataFrame, metric: str, year: int, top_n=10): + st.markdown(f"### ๐Ÿ† Top {top_n} Cities by {metric} in {year}") + top_cities = ( + df[df["Year"] == year] + .sort_values(by=metric, ascending=False) + .loc[:, ["City", metric]] + .head(top_n) + .reset_index(drop=True) + ) + st.dataframe(top_cities, use_container_width=True) + + +# Show bottom percentile cities and their policy suggestions based on a metric +def show_bottom_cities_with_policy( + df: pd.DataFrame, metric: str, year: int, bottom_pct=10 +): + st.markdown( + f"### ๐Ÿ“‰ Policy Suggestions for Bottom {bottom_pct}% Cities by {metric} in {year}" + ) + n_cities = len(df["City"].unique()) + n_bottom = max( + 1, int(n_cities * bottom_pct / 100) + ) # Calculate how many cities fall in bottom_pct + bottom_cities = ( + df[df["Year"] == year] + .sort_values(by=metric) + .loc[:, ["City", metric]] + .head(n_bottom) + .reset_index(drop=True) + ) + st.dataframe(bottom_cities, use_container_width=True) + + st.markdown("#### Suggested Policies:") + # For each bottom city, show relevant policy suggestion from constants + for city in bottom_cities["City"]: + suggestion = POLICY_SUGGESTIONS.get( + metric, "General development assistance recommended." + ) + st.markdown(f"- **{city}**: {suggestion}") + + +# Highlight cities with best healthcare systems, based on combined healthcare indicators +def show_healthcare_leaders(df: pd.DataFrame, year: int, top_n=10): + st.markdown(f"### ๐Ÿฅ Cities with Best Healthcare Systems in {year}") + df_year = df[df["Year"] == year].copy() + + # Calculate a health score using multiple healthcare metrics (weights are arbitrary) + df_year["Health Score"] = ( + df_year.get("Healthcare Expenditure per Capita", 0) + + df_year.get("Physicians per 1000", 0) * 1000 + + df_year.get("Hospital Beds per 1000", 0) * 100 + ) + + top_health = ( + df_year.sort_values(by="Health Score", ascending=False) + .loc[:, ["City", "Health Score"]] + .head(top_n) + .reset_index(drop=True) + ) + st.dataframe(top_health, use_container_width=True) + + +# Show environmental leaders and laggards based on combined environmental metrics +def show_environmental_leaders_and_laggards(df: pd.DataFrame, year: int, top_n=5): + st.markdown(f"### ๐ŸŒฑ Environmental Leaders and Laggards in {year}") + df_year = df[df["Year"] == year].copy() + + # Green Score is a composite metric rewarding positive and penalizing negative environmental factors + df_year["Green Score"] = ( + df_year.get("Renewable Energy %", 0) + + df_year.get("Forest Area %", 0) + - df_year.get("CO2 Emissions per Capita", 0) + - df_year.get("Air Quality Index", 0) + ) + + best = ( + df_year.sort_values(by="Green Score", ascending=False) + .loc[:, ["City", "Green Score"]] + .head(top_n) + .reset_index(drop=True) + ) + worst = ( + df_year.sort_values(by="Green Score", ascending=True) + .loc[:, ["City", "Green Score"]] + .head(top_n) + .reset_index(drop=True) + ) + + col1, col2 = st.columns(2) + with col1: + st.markdown("**Top Environmental Performers**") + st.dataframe(best, use_container_width=True) + with col2: + st.markdown("**Worst Environmental Performers**") + st.dataframe(worst, use_container_width=True) + + +# Display a custom insight summary based on selected filters (cities, metrics, year range) +def show_custom_insight( + filtered_df: pd.DataFrame, cities: list, metrics: list, year_range: tuple +): + st.markdown("### ๐Ÿ”Ž Custom Insight Summary") + + # Check for empty selections and inform user accordingly + if not cities: + st.info( + "โš ๏ธ No city selected. Please select one or more cities from the sidebar to see insights." + ) + return + if not metrics: + st.info( + "โš ๏ธ No metric selected. Please select one or more metrics from the sidebar to see insights." + ) + return + if filtered_df.empty: + st.info( + "No data available for the selected filters. Please adjust your selections." + ) + return + + # Summary of filtered data selection + st.write( + f"You have selected data for {len(filtered_df['City'].unique())} cities between {year_range[0]} and {year_range[1]}." + ) + avg_metrics = filtered_df[metrics].mean().round(2) + st.write("Average values for selected metrics:") + st.dataframe( + avg_metrics.reset_index().rename( + columns={"index": "Metric", 0: "Average Value"} + ), + use_container_width=True, + ) + + +# Main function to show key insights, with optional filters passed +def show_key_insights(df, year=None, cities=None, metrics=None, year_range=None): + # Select year for insights; defaults to latest year + year = st.selectbox( + "Select Year for Insights", + options=sorted(df["Year"].unique()), + index=len(df["Year"].unique()) - 1, # default latest year selected + ) + + # Show various key insights for the selected year + show_top_cities_by_metric(df, "HDI", year) + show_bottom_cities_with_policy(df, "Gini Coefficient", year) + show_healthcare_leaders(df, year) + show_environmental_leaders_and_laggards(df, year) + + # If filters for cities, metrics, and year_range are passed, show custom insight summary + if cities is not None and metrics is not None and year_range is not None: + filtered_df = df[ + (df["City"].isin(cities)) + & (df["Year"] >= year_range[0]) + & (df["Year"] <= year_range[1]) + ] + show_custom_insight(filtered_df, cities, metrics, year_range) diff --git a/submissions/Raushan/utils/policy_insights.py b/submissions/Raushan/utils/policy_insights.py new file mode 100644 index 0000000..15a2b08 --- /dev/null +++ b/submissions/Raushan/utils/policy_insights.py @@ -0,0 +1,38 @@ +import streamlit as st +import pandas as pd +from .constants import POLICY_SUGGESTIONS + + +def show_policy_insights(df: pd.DataFrame): + st.header("๐Ÿ“€ Policy Insights") + + # Select a metric to identify underperforming cities + st.subheader("Policy Suggestions for Underperforming Cities") + under_metric = st.selectbox( + "Select Metric for Underperformance", + [col for col in df.columns if col not in ["City", "Year"]], + key="under_metric", + ) + + # Slider to choose bottom percentage threshold for defining underperformance + threshold = st.slider("Bottom % Threshold", 1, 30, 10) + + # Calculate number of bottom cities based on selected threshold + n_bottom = int(len(df["City"].unique()) * threshold / 100) + latest_year = df["Year"].max() + + # Filter data for latest year and get bottom cities by selected metric + bottom_cities = ( + df[df["Year"] == latest_year].sort_values(by=under_metric).head(n_bottom) + ) + + # Display the bottom cities with their metric values + st.dataframe(bottom_cities[["City", under_metric]], use_container_width=True) + st.markdown("#### Suggested Policies") + + # Show policy suggestions mapped to the selected metric or a default message + for city in bottom_cities["City"]: + suggestion = POLICY_SUGGESTIONS.get( + under_metric, "General development assistance recommended" + ) + st.markdown(f"- **{city}**: {suggestion}") diff --git a/submissions/Raushan/utils/visualizations.py b/submissions/Raushan/utils/visualizations.py new file mode 100644 index 0000000..e65d90b --- /dev/null +++ b/submissions/Raushan/utils/visualizations.py @@ -0,0 +1,220 @@ +import streamlit as st +import plotly.express as px +import seaborn as sns +import matplotlib.pyplot as plt +import pydeck as pdk +import pandas as pd +import numpy as np +from .constants import CITY_COORDS # Predefined city coordinates for map visualization + + +def plot_bar_chart(df_vis, metrics, year): + """Plot bar charts for given metrics and year.""" + for metric in metrics: + fig = px.bar( + df_vis, x="City", y=metric, title=f"Bar Chart of {metric} in {year}" + ) + st.plotly_chart(fig, use_container_width=True) + + +def plot_line_chart(df_line, metrics): + """Plot line charts for given metrics across years, grouped by city.""" + for metric in metrics: + fig = px.line( + df_line, + x="Year", + y=metric, + color="City", + title=f"Line Graph of {metric} over Years", + markers=True, + ) + st.plotly_chart(fig, use_container_width=True) + + +def plot_scatter_plot(df_vis, metrics, year): + """ + Plot scatter plot between two selected metrics for a given year. + Requires exactly two metrics to compare. + """ + if len(metrics) < 2: + st.warning("Please select at least two metrics for scatter plot.") + elif len(metrics) > 2: + st.error("You can only select up to 2 values.") + else: + x_metric, y_metric = metrics + fig = px.scatter( + df_vis, + x=x_metric, + y=y_metric, + color="City", + hover_name="City", + title=f"Scatter: {y_metric} vs {x_metric} in {year}", + ) + st.plotly_chart(fig, use_container_width=True) + + +def plot_pie_chart(df): + """ + Display a pie chart for a selected metric and year, showing proportion by city. + Allows dynamic selection of metric and year. + """ + st.subheader("๐Ÿฅง Pie Chart") + pie_metric = st.selectbox( + "Select Metric for Pie Chart", + [col for col in df.columns if col not in ["City", "Year"]], + key="pie", + ) + pie_year = st.selectbox( + "Select Year for Pie Chart", + [year for year in range(int(df["Year"].min()), int(df["Year"].max()) + 1)], + ) + df_pie = df[df["Year"] == pie_year] + fig_pie = px.pie( + df_pie, + values=pie_metric, + names="City", + title=f"Pie Chart of {pie_metric} by City in {pie_year}", + ) + st.plotly_chart(fig_pie, use_container_width=True) + + +def plot_heatmap(df): + """ + Plot a correlation heatmap for numeric metrics (excluding City and Year). + Helps identify relationships between metrics. + """ + st.subheader("๐ŸŒก๏ธ Correlation Heatmap") + df_corr = df[[col for col in df.columns if col not in ["City", "Year"]]].corr() + fig, ax = plt.subplots(figsize=(12, 8)) + sns.heatmap(df_corr, annot=False, cmap="coolwarm", ax=ax) + st.pyplot(fig) + + +def plot_interactive_map(df): + """ + Display an interactive map of cities with circle sizes proportional + to the selected metricโ€™s value for a chosen year. + """ + st.subheader("๐Ÿ—บ๏ธ Interactive City Map") + map_metric = st.selectbox( + "Select Metric to Visualize on Map", + [col for col in df.columns if col not in ["City", "Year"]], + key="map_metric", + ) + map_year = st.selectbox("Select Year for Map", sorted(df["Year"].unique())) + + # Filter data for the selected year and metric + df_map_metric = df[df["Year"] == map_year][["City", map_metric]] + + # Prepare dataframe for map with city coordinates + map_data = pd.DataFrame( + [ + {"City": city, "lat": lat, "lon": lon} + for city, (lat, lon) in CITY_COORDS.items() + ] + ) + + # Merge metric data with coordinates + map_data = pd.merge(map_data, df_map_metric, on="City", how="left") + + # Normalize radius for circle sizes on map + min_val = map_data[map_metric].min() + max_val = map_data[map_metric].max() + map_data["radius"] = 20000 + 80000 * (map_data[map_metric] - min_val) / ( + max_val - min_val + ) + + # Define map layer for city scatter points + layer = pdk.Layer( + "ScatterplotLayer", + data=map_data, + get_position="[lon, lat]", + get_color="[100, 150, 255, 160]", + get_radius="radius", + pickable=True, + auto_highlight=True, + ) + + # Set initial view state for the map centered on cities + view_state = pdk.ViewState( + latitude=map_data["lat"].mean(), + longitude=map_data["lon"].mean(), + zoom=4.5, + pitch=0, + ) + + # Tooltip on hover showing city and metric value + tooltip = { + "html": f"City: {{City}}
{map_metric}: {{{map_metric}}}", + "style": {"backgroundColor": "black", "color": "white"}, + } + + # Render the interactive map chart in Streamlit + st.pydeck_chart( + pdk.Deck( + map_style=None, + initial_view_state=view_state, + layers=[layer], + tooltip=tooltip, + ) + ) + + +def show_visualizations(df: pd.DataFrame): + """ + Main visualization controller function to show different types of charts + based on user input selections from Streamlit widgets. + """ + st.subheader("๐Ÿ“Š Visualization") + + # User selects graph type + graph_type = st.selectbox( + "Select Graph Type", options=["Bar Chart", "Line Graph", "Scatter Plot"] + ) + + # For scatter plot, allow multiple metric selection; for others single metric + if graph_type == "Scatter Plot": + vis_metrics = st.multiselect( + "Select Metric(s)", + [col for col in df.columns if col not in ["City", "Year"]], + default=[df.columns[2]], + ) + else: + vis_metrics = [ + st.selectbox( + "Select a Metric", + [col for col in df.columns if col not in ["City", "Year"]], + ) + ] + + # Select cities (common for all graphs) + vis_cities = st.multiselect( + "Select City/Cities", + sorted(df["City"].unique()), + default=[df["City"].unique()[0]], + ) + + if graph_type != "Line Graph": + vis_year = st.selectbox("Select Year", sorted(df["Year"].unique())) + df_vis = df[(df["Year"] == vis_year) & (df["City"].isin(vis_cities))] + + # Plot based on graph type + if graph_type == "Bar Chart": + plot_bar_chart(df_vis, vis_metrics, vis_year) + elif graph_type == "Line Graph": + df_line = df[df["City"].isin(vis_cities)] + plot_line_chart(df_line, vis_metrics) + elif graph_type == "Scatter Plot": + plot_scatter_plot(df_vis, vis_metrics, vis_year) + + # Optional pie chart toggle in sidebar + if st.sidebar.checkbox("Show Pie Chart"): + st.markdown("---") + plot_pie_chart(df) + + # Always show heatmap and interactive map below + st.markdown("---") + plot_heatmap(df) + + st.markdown("---") + plot_interactive_map(df) diff --git 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