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app1.py

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+import pandas as pd
+
+# in some cases ipynb needs the line bellow in order to correctly show the plots
+#matplotlib inline 
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Apply the default theme and activate color codes
+sns.set_theme()
+#sns.set(color_codes=True)
+
+# load a dataset
+penguins = sns.load_dataset("penguins")
+penguins.head()
+
+sns.displot(data=penguins,
+            x="flipper_length_mm")
+sns.displot(data=penguins,
+            x="flipper_length_mm",
+            kind="kde")
+
+sns.displot(data=penguins,
+            x="flipper_length_mm",
+            kind="kde",
+            hue="species")
+
+#%%writefile app1.py
+
+import streamlit as st
+import pandas as pd
+
+
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Apply the default theme and activate color codes
+sns.set_theme()
+#sns.set(color_codes=True)
+
+#%%writefile  -a app1.py
+
+################### import dataset
+penguins = sns.load_dataset("penguins")
+
+#%%writefile  -a app1.py
+
+########### set the title and subtitle
+st.title("Differences between penguins")
+
+st.subheader("My flipper is longer!!!")
+
+#%%writefile  -a app1.py
+
+########## we add image
+st.image("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/man/figures/lter_penguins.png")
+
+#%%writefile  -a app1.py
+
+############## we create filters for our interactive plot
+with st.sidebar:
+    st.subheader("Filters")
+
+    all_species = sorted(penguins["species"].dropna().unique().tolist())
+    selected_species = st.multiselect(
+        "Species to show",
+        options=all_species,
+        default=all_species,
+    )
+
+    feature_options = {
+        "Flipper length (mm)": "flipper_length_mm",
+        "Bill length (mm)": "bill_length_mm",
+        "Bill depth (mm)": "bill_depth_mm",
+        "Body mass (g)": "body_mass_g",
+    }
+    feature_label = st.selectbox("Feature (x-axis)", list(feature_options.keys()))
+    x_col = feature_options[feature_label]
+
+    # KDE options
+    fill = st.checkbox("Shade area", value=True)
+    bw_adjust = st.slider("Smoothing (bw_adjust)", 0.2, 2.0, 1.0, 0.1)
+    common_norm = st.checkbox("Normalize across species", value=False)
+
+    #%%writefile  -a app1.py
+
+if not selected_species:
+    st.info("Select at least one species to display the plot.")
+else:
+    data = penguins[penguins["species"].isin(selected_species)].dropna(subset=[x_col])
+
+    g = sns.displot(
+        data=data,
+        x=x_col,
+        kind="kde",
+        hue="species",
+        fill=fill,
+        bw_adjust=bw_adjust,
+        common_norm=common_norm,
+        height=4,
+        aspect=1.6,
+    )
+    fig = g.fig if hasattr(g, "fig") else g.figure
+    st.pyplot(fig)
+    plt.close(fig)
+
+    #%%writefile  -a app.py
+
+########  add button to save image
+from io import BytesIO
+
+buf = BytesIO()
+fig.savefig(buf, format="png", dpi=200, bbox_inches="tight") 
+buf.seek(0)
+st.download_button(
+    "Save image",
+    data=buf,
+    file_name=f"penguins_{x_col}.png",
+    mime="image/png",
+)
+
+#%%writefile  -a app.py
+
+########### Footer
+st.caption("Developed for SDS M1 course.")