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Added dynamic UI #42

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Added dynamic UI #42

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93 changes: 62 additions & 31 deletions visualize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,63 +3,94 @@
import matplotlib.pyplot as plt
import seaborn as sns

st.set_page_config(page_title="Mining Site Visualization", page_icon="🔍")
# Set page configuration with wider layout
st.set_page_config(page_title="Mining Site Visualization", page_icon="🔍", layout="wide")

@st.cache_data
def load_data():
# Load the dataset
# Load and cache the dataset for faster performance
data = pd.read_csv("space_mining_dataset.csv")
return data

def show_visualize_page():
st.title("Mining Site Visualization")
st.title("🚀 Mining Site Visualization")
st.write("Explore different visualizations to understand the dataset and the impact of user preferences.")

data = load_data()
# Loading data with a progress bar
with st.spinner("Loading dataset..."):
data = load_data()

# Visualization 1: Distribution of Features
# Add expander for dataset preview
with st.expander("Preview Dataset"):
st.write(data.head())

# Add a sidebar for feature selection and additional options
st.sidebar.header("Feature Settings")

# Visualization 1: Distribution of Features with dynamic bins option
st.subheader("Distribution of Features")
feature = st.selectbox("Select Feature to Visualize", data.columns[1:]) # Exclude non-numeric columns if necessary
st.write("Visualize the distribution of different features with a histogram and density plot.")

feature = st.sidebar.selectbox("Select Feature to Visualize", data.columns[1:], help="Choose a feature to visualize.")
bins = st.sidebar.slider("Number of Bins", 10, 50, 20, help="Set the number of bins for the histogram.")

# Displaying the histogram and density plot
fig, ax = plt.subplots()
sns.histplot(data[feature], bins=20, kde=True, ax=ax)
sns.histplot(data[feature], bins=bins, kde=True, ax=ax, color="skyblue")
ax.set_xlabel(feature)
st.pyplot(fig)

# Visualization 2: Pairplot of Selected Features
# Visualization 2: Pairplot of Selected Features with hue options
st.subheader("Pairplot of Selected Features")
features = st.multiselect("Select Features for Pairplot", data.columns[1:]) # Exclude non-numeric columns if necessary
features = st.multiselect("Select Features for Pairplot", data.columns[1:], help="Select two or more features to visualize relationships.")

if len(features) > 1:
fig, ax = plt.subplots()
sns.pairplot(data[features + ['final_score']], diag_kind='kde', hue='final_score')
hue_feature = st.selectbox("Select Feature for Color Encoding", ['final_score'] + features, help="Select a feature to color the pairplot.")
fig = sns.pairplot(data[features + ['final_score']], diag_kind='kde', hue=hue_feature, palette='coolwarm')
st.pyplot(fig)
else:
st.write("Please select more than one feature.")
st.warning("Please select more than one feature for pairplot.")

# Visualization 3: Impact of Weights on Recommendations
st.subheader("Impact of Weights on Recommendations")
st.write("Adjust the weights to see how the recommendations change.")
st.write("Adjust the weights below to see how the recommendations for mining sites change based on different criteria.")

st.sidebar.header("Weights Settings")

iron_weight = st.slider("Iron Weight", 0.0, 1.0, 0.3)
nickel_weight = st.slider("Nickel Weight", 0.0, 1.0, 0.2)
water_ice_weight = st.slider("Water Ice Weight", 0.0, 1.0, 0.2)
other_minerals_weight = st.slider("Other Minerals Weight", 0.0, 1.0, 0.1)
sustainability_weight = st.slider("Sustainability Weight", 0.0, 1.0, 0.1)
distance_weight = st.slider("Distance Weight", -1.0, 0.0, -0.1)
# Dynamically adjusting sliders for weights
iron_weight = st.sidebar.slider("Iron Weight", 0.0, 1.0, 0.3, help="Set the weight for iron.")
nickel_weight = st.sidebar.slider("Nickel Weight", 0.0, 1.0, 0.2, help="Set the weight for nickel.")
water_ice_weight = st.sidebar.slider("Water Ice Weight", 0.0, 1.0, 0.2, help="Set the weight for water ice.")
other_minerals_weight = st.sidebar.slider("Other Minerals Weight", 0.0, 1.0, 0.1, help="Set the weight for other minerals.")
sustainability_weight = st.sidebar.slider("Sustainability Weight", 0.0, 1.0, 0.1, help="Set the weight for sustainability.")
distance_weight = st.sidebar.slider("Distance Weight", -1.0, 0.0, -0.1, help="Set the weight for distance from Earth (negative weight for minimizing distance).")

# Calculate and display adjusted scores
adjusted_scores = data.copy()
adjusted_scores['adjusted_score'] = (
iron_weight * adjusted_scores['iron'] +
nickel_weight * adjusted_scores['nickel'] +
water_ice_weight * adjusted_scores['water_ice'] +
other_minerals_weight * adjusted_scores['other_minerals'] +
sustainability_weight * adjusted_scores['sustainability_index'] +
distance_weight * adjusted_scores['distance_from_earth']
)
# Display a progress bar for score calculation
with st.spinner("Calculating adjusted scores..."):
adjusted_scores = data.copy()
adjusted_scores['adjusted_score'] = (
iron_weight * adjusted_scores['iron'] +
nickel_weight * adjusted_scores['nickel'] +
water_ice_weight * adjusted_scores['water_ice'] +
other_minerals_weight * adjusted_scores['other_minerals'] +
sustainability_weight * adjusted_scores['sustainability_index'] +
distance_weight * adjusted_scores['distance_from_earth']
)

# Display top N sites based on adjusted scores
top_n = st.slider("Number of Top Sites to Display", 1, 10, 5)
top_n = st.sidebar.slider("Number of Top Sites to Display", 1, 10, 5)
top_sites = adjusted_scores.sort_values(by='adjusted_score', ascending=False).head(top_n)

st.subheader(f"Top {top_n} Sites Based on Adjusted Scores")
st.write(top_sites[['Celestial Body', 'iron', 'nickel', 'water_ice', 'distance_from_earth', 'adjusted_score']])
st.write("The table below shows the top mining sites based on the weighted scores.")
st.dataframe(top_sites[['Celestial Body', 'iron', 'nickel', 'water_ice', 'distance_from_earth', 'adjusted_score']])

# Add bar chart to visualize top sites by adjusted scores
st.subheader("Bar Chart of Adjusted Scores for Top Sites")
st.bar_chart(top_sites[['Celestial Body', 'adjusted_score']].set_index('Celestial Body'))

# Add a download button for users to download top sites as CSV
csv = top_sites.to_csv(index=False)
st.download_button(label="Download Top Sites as CSV", data=csv, file_name='top_mining_sites.csv', mime='text/csv')

show_visualize_page()
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