lte drop trafic app

app.py

@@ -158,6 +158,10 @@ if check_password():
                 "apps/kpi_analysis/lte_capacity.py",
                 title=" 📊 LTE Capacity Analysis",
             ),
+            st.Page(
+                "apps/kpi_analysis/lte_drop_trafic.py",
+                title=" 📊 LTE Drop Traffic Analysis",
+            ),
         ],
         "Paging Analysis": [
             st.Page(

apps/kpi_analysis/lte_drop_trafic.py

@@ -0,0 +1,161 @@
+from io import BytesIO
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from utils.utils_vars import get_physical_db
+
+st.title("LTE Cell Traffic Drop Detection")
+doc_col, image_col = st.columns(2)
+
+with doc_col:
+    st.write(
+        """
+        This App allow you to detect cells with significant traffic drop in LTE Network.
+        - Upload traffic CSV file
+    - Select number of last days for drop analysis
+    - Select loss percentage threshold
+    """
+    )
+
+with image_col:
+    st.image("./assets/traffic_drop.png", width=250)
+
+uploaded_file = st.file_uploader("Upload traffic CSV file", type=["csv"])
+
+if uploaded_file:
+    df = pd.read_csv(uploaded_file, sep=";")
+
+    df["PERIOD_START_TIME"] = pd.to_datetime(df["PERIOD_START_TIME"], format="%m.%d.%Y")
+    df.sort_values("PERIOD_START_TIME", inplace=True)
+
+    df["Total_Traffic"] = (
+        df["4G/LTE DL Traffic Volume (GBytes)"]
+        + df["4G/LTE UL Traffic Volume (GBytes)"]
+    )
+
+    unique_dates = sorted(df["PERIOD_START_TIME"].unique())
+    last_n_days = st.slider(
+        "Select number of last days for drop analysis",
+        1,
+        min(10, len(unique_dates) - 1),
+        2,
+    )
+    treshold_percent = st.slider("Loss percentage threshold", 10, 100, 50)
+
+    last_days = unique_dates[-last_n_days:]
+    long_term_days = unique_dates[:-last_n_days]
+
+    last_df = df[df["PERIOD_START_TIME"].isin(last_days)]
+    long_term_df = df[df["PERIOD_START_TIME"].isin(long_term_days)]
+
+    avg_last = last_df.groupby("LNCEL name")["Total_Traffic"].mean()
+    avg_long = long_term_df.groupby("LNCEL name")["Total_Traffic"].mean()
+
+    result = pd.DataFrame(
+        {"avg_long_term": avg_long, "avg_last_days": avg_last}
+    ).dropna()
+
+    result["drop_%"] = (
+        (result["avg_long_term"] - result["avg_last_days"])
+        / result["avg_long_term"]
+        * 100
+    )
+    result = result[result["drop_%"] >= treshold_percent]
+    result = result.reset_index()
+
+    st.subheader("Cells with Significant Traffic Drop")
+    st.dataframe(result)
+
+    def convert_df(df: pd.DataFrame) -> bytes:
+        output = BytesIO()
+        df.to_excel(output, index=False)
+        processed_data = output.getvalue()
+        return processed_data
+
+    if not result.empty:
+        st.download_button(
+            label="📥 Download affected cells",
+            data=convert_df(result),
+            file_name="traffic_drop_cells.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+            type="primary",
+        )
+
+        @st.fragment
+        def trend_plot():
+            st.subheader("Traffic Trend Plot")
+            default_cell = result["LNCEL name"].iloc[0]
+            selected_cell = st.selectbox(
+                "Select cell to plot",
+                result["LNCEL name"].unique(),
+                index=result["LNCEL name"].unique().tolist().index(default_cell),
+            )
+
+            if selected_cell:
+                trend_df = df[df["LNCEL name"].eq(selected_cell)]
+                fig = px.line(
+                    trend_df,
+                    x="PERIOD_START_TIME",
+                    y="Total_Traffic",
+                    title="Traffic Trends",
+                    markers=True,
+                    height=700,
+                )
+
+                if selected_cell in avg_long:
+                    fig.add_shape(
+                        type="line",
+                        x0=trend_df["PERIOD_START_TIME"].min(),
+                        x1=trend_df["PERIOD_START_TIME"].max(),
+                        y0=avg_long[selected_cell],
+                        y1=avg_long[selected_cell],
+                        line=dict(color="blue", dash="dot"),
+                        name=f"{selected_cell} Long Term Avg",
+                    )
+
+                if last_days:
+                    start_date = pd.to_datetime(str(last_days[0]))
+                    fig.add_shape(
+                        type="line",
+                        x0=start_date,
+                        x1=start_date,
+                        y0=0,
+                        y1=trend_df["Total_Traffic"].max(),
+                        line=dict(color="red", dash="dash"),
+                        name="Start of Last Days",
+                    )
+
+                st.plotly_chart(fig, use_container_width=True)
+
+        trend_plot()
+
+        st.subheader("Map of Affected Cells (Bubble Size = Drop %)")
+        result_map = result.copy()
+        physical_db = get_physical_db()
+
+        # Add code column to physical_db element before _
+        physical_db["code"] = physical_db["Code_Sector"].str.split("_").str[0]
+        # add code column to result_map
+        result_map["code"] = result_map["LNCEL name"].str.split("_").str[0]
+
+        result_map = pd.merge(result_map, physical_db, on="code", how="left")
+
+        result_map["Latitude"] = result_map["Latitude"]
+        result_map["Longitude"] = result_map["Longitude"]
+        fig_map = px.scatter_map(
+            result_map,
+            lat="Latitude",
+            lon="Longitude",
+            size="drop_%",
+            color=result_map["drop_%"],
+            color_continuous_scale="reds",
+            hover_name="LNCEL name",
+            zoom=6,
+            height=600,
+            title="Dropped Cells Map",
+            map_style="satellite-streets",
+        )
+
+        st.plotly_chart(fig_map, use_container_width=True)