Update app.py

app.py

@@ -31,9 +31,10 @@ if uploaded_file:
     df = pd.read_csv(uploaded_file)
     st.success("✅ File uploaded and loaded!")
 
-    # Basic Preview
+    # Custom column selection for preview
     st.subheader("📃 Data Preview")
-    st.dataframe(df.head())
+    selected_columns = st.multiselect("Select columns to preview", df.columns.tolist(), default=df.columns.tolist()[:5])
+    st.dataframe(df[selected_columns].head())
 
     # Descriptive Stats
     st.subheader("📊 Descriptive Statistics")
@@ -46,6 +47,46 @@ if uploaded_file:
     sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f", ax=ax)
     st.pyplot(fig)
 
+    # Technical Visualizations
+    st.subheader("📊 Technical Graphs")
+    numeric_columns = df.select_dtypes(include='number').columns.tolist()
+
+    # Time Series Plot
+    selected_graph_column = st.selectbox("Select a parameter for time series plot", numeric_columns)
+    time_column = st.selectbox("Select time/index column (optional)", ['Index'] + df.columns.tolist(), index=0)
+
+    fig2, ax2 = plt.subplots(figsize=(10, 4))
+    if time_column != 'Index':
+        try:
+            df[time_column] = pd.to_datetime(df[time_column])
+            df_sorted = df.sort_values(by=time_column)
+            ax2.plot(df_sorted[time_column], df_sorted[selected_graph_column])
+            ax2.set_xlabel(time_column)
+        except:
+            ax2.plot(df[selected_graph_column])
+            ax2.set_xlabel("Index")
+    else:
+        ax2.plot(df[selected_graph_column])
+        ax2.set_xlabel("Index")
+    ax2.set_title(f"Trend Over Time: {selected_graph_column}")
+    ax2.set_ylabel(selected_graph_column)
+    st.pyplot(fig2)
+
+    # Pairplot
+    if len(numeric_columns) > 1:
+        st.subheader("🔄 Pairwise Parameter Relationships")
+        sampled_df = df[numeric_columns].sample(n=200, random_state=1) if len(df) > 200 else df[numeric_columns]
+        pair_fig = sns.pairplot(sampled_df)
+        st.pyplot(pair_fig)
+
+    # Boxplots
+    st.subheader("📈 Distribution & Outliers per Parameter")
+    selected_box_column = st.selectbox("Select parameter for boxplot", numeric_columns)
+    fig3, ax3 = plt.subplots()
+    sns.boxplot(y=df[selected_box_column], ax=ax3)
+    ax3.set_title(f"Boxplot: {selected_box_column}")
+    st.pyplot(fig3)
+
     # Anomaly Detection
     st.subheader("⚠️ Anomaly Detection using Isolation Forest")
     num_df = df.select_dtypes(include='number').dropna()