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app.py

@@ -26,27 +26,109 @@ def on_btn_click():
 
 
 def main():
-    st.title(" Image Prediction (Computer Vision)")
-    option = st.selectbox(" ImageNet / CoCo", [" ImageNet ", " CoCo"])
-    value = st.slider(" Threshold", min_value=0, max_value=100, value=50, key=65)
+    st.title(" Corona Dashboard")
     (
         col1,
         col2,
     ) = st.columns(2)
     with col1:
-        if st.checkbox(" Remove Noise"):
-            st.write("Checkbox checked!")
+        option = st.selectbox(" San Francisco", [" San Francisco"])
     with col2:
-        if st.checkbox(" Increase Resolution"):
-            st.write("Checkbox checked!")
-    uploaded_file = st.file_uploader("Choose a file", type=["jpg", "png", "mp3"])
-    if st.button(" Predict"):
+        option = st.selectbox(" Monthly / Weekly", [" Monthly ", " Weekly"])
+    if st.checkbox(" Show raw data"):
+        st.write("Checkbox checked!")
+    if st.button(" Visualize"):
         st.write("Button clicked!")
-    st.subheader(" Original vs Predicted")
-    image_comparison(
-        img1="https://www.imgonline.com.ua/examples/red-yellow-flower.jpg",
-        img2="https://lettatai.sirv.com/imgonline-com-ua-Negative-lYz1br1SWE.jpg",
+    st.subheader(" Global Data")
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/plotly/datasets/master/volcano_db.csv",
+        encoding="iso-8859-1",
     )
+    freq = df
+    freq = freq.Country.value_counts().reset_index().rename(columns={"count": "x"})
+    df_v = pd.read_csv(
+        "https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv"
+    )
+    fig = make_subplots(
+        rows=2,
+        cols=2,
+        column_widths=[0.6, 0.4],
+        row_heights=[0.4, 0.6],
+        specs=[
+            [{"type": "scattergeo", "rowspan": 2}, {"type": "bar"}],
+            [None, {"type": "surface"}],
+        ],
+    )
+    fig.add_trace(
+        go.Scattergeo(
+            lat=df["Latitude"],
+            lon=df["Longitude"],
+            mode="markers",
+            hoverinfo="text",
+            showlegend=False,
+            marker=dict(color="crimson", size=4, opacity=0.8),
+        ),
+        row=1,
+        col=1,
+    )
+    fig.add_trace(
+        go.Bar(
+            x=freq["x"][0:10],
+            y=freq["Country"][0:10],
+            marker=dict(color="crimson"),
+            showlegend=False,
+        ),
+        row=1,
+        col=2,
+    )
+    fig.add_trace(go.Surface(z=df_v.values.tolist(), showscale=False), row=2, col=2)
+    fig.update_geos(
+        projection_type="orthographic",
+        landcolor="white",
+        oceancolor="MidnightBlue",
+        showocean=True,
+        lakecolor="LightBlue",
+    )
+    fig.update_xaxes(tickangle=45)
+    fig.update_layout(
+        template="plotly_dark",
+        margin=dict(r=10, t=25, b=40, l=60),
+        annotations=[
+            dict(
+                text="Source: NOAA",
+                showarrow=False,
+                xref="paper",
+                yref="paper",
+                x=0,
+                y=0,
+            )
+        ],
+    )
+    st.plotly_chart(fig)
+    (
+        col1,
+        col2,
+    ) = st.columns(2)
+    with col1:
+        st.table(
+            {
+                "Country": ["USA", "Canada", "UK", "Australia"],
+                "Population (millions)": [331, 38, 66, 25],
+                "GDP (trillion USD)": [22.675, 1.843, 2.855, 1.488],
+            }
+        )
+    with col2:
+        df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
+        fig = px.pie(
+            df,
+            values="pop",
+            names="country",
+            title="Population of American continent",
+            hover_data=["lifeExp"],
+            labels={"lifeExp": "life expectancy"},
+        )
+        fig.update_traces(textposition="inside", textinfo="percent+label")
+        st.plotly_chart(fig)
 
 
 if __name__ == "__main__":