Update app.py

app.py

@@ -519,241 +519,176 @@ elif app_mode == "Advanced EDA":
         Uncover hidden patterns and relationships in your data.
     """)
 
-   if 'cleaned_data' not in st.session_state or st.session_state.cleaned_data is None:
-    st.warning("Please clean your data in the Smart Cleaning section first.")
-    st.stop()
-
-df = st.session_state.cleaned_data.copy()
-
-# Initialize session state for EDA configuration
-if 'eda_config' not in st.session_state:
-    st.session_state.eda_config = {
-        'plot_type': "Histogram",
-        'x_col': df.columns[0] if len(df.columns) > 0 else None,
-        'y_col': df.columns[1] if len(df.columns) > 1 else None,
-        'z_col': df.columns[2] if len(df.columns) > 2 else None,
-        'color_col': None,
-        'size_col': None,
-        'time_col': None,
-        'value_col': None,
-        'scatter_matrix_cols': df.select_dtypes(include=np.number).columns.tolist()[:5],
-        'color_palette': "Viridis",
-        'hover_data_cols': [],
-        'filter_col': None,
-        'filter_options': []
-    }
+    if 'cleaned_data' not in st.session_state or st.session_state.cleaned_data is None:
+        st.warning("Please clean your data in the Smart Cleaning section first.")
+        st.stop()
 
-# Data Filtering Section
-with st.expander("🔎 Data Filtering", expanded=True):
-    st.session_state.eda_config['filter_col'] = st.selectbox(
-        "Filter Column",
-        [None] + list(df.columns),
-        help="Choose a column to filter the data."
-    )
+    df = st.session_state.cleaned_data.copy()
+
+    # Initialize session state for EDA configuration
+    if 'eda_config' not in st.session_state:
+        st.session_state.eda_config = {
+            'plot_type': "Histogram",
+            'x_col': df.columns[0] if len(df.columns) > 0 else None,
+            'y_col': df.columns[1] if len(df.columns) > 1 else None,
+            'z_col': df.columns[2] if len(df.columns) > 2 else None,
+            'color_col': None,
+            'size_col': None,
+            'time_col': None,
+            'value_col': None,
+            'scatter_matrix_cols': df.select_dtypes(include=np.number).columns.tolist()[:5],
+            'color_palette': "Viridis",
+            'hover_data_cols': [],
+            'filter_col': None,
+            'filter_options': []
+        }
 
-    if st.session_state.eda_config['filter_col']:
-        unique_values = df[st.session_state.eda_config['filter_col']].unique()
-        st.session_state.eda_config['filter_options'] = st.multiselect(
-            "Filter Values",
-            unique_values,
-            default=unique_values,
-            help=f"Select values from '{st.session_state.eda_config['filter_col']}'"
+    # Data Filtering Section
+    with st.expander("🔎 Data Filtering", expanded=True):
+        st.session_state.eda_config['filter_col'] = st.selectbox(
+            "Filter Column",
+            [None] + list(df.columns),
+            help="Choose a column to filter the data."
         )
-        df = df[df[st.session_state.eda_config['filter_col']].isin(
-            st.session_state.eda_config['filter_options']
-        )]
-
-# Visualization Type Selection
-st.sidebar.header("📊 Visualization Configuration")
-plot_types = [
-    "Histogram", "Scatter Plot", "Box Plot", "Violin Plot",
-    "Correlation Heatmap", "Parallel Coordinates", "Andrews Curves",
-    "Pair Plot", "Density Contour", "3D Scatter", "Time Series",
-    "Sunburst Chart", "Funnel Chart", "Clustering Analysis"
-]
-st.session_state.eda_config['plot_type'] = st.sidebar.selectbox(
-    "Choose Visualization",
-    plot_types,
-    index=0
-)
 
-# Dynamic Controls Based on Plot Type
-if st.session_state.eda_config['plot_type'] != "Correlation Heatmap":
-    st.session_state.eda_config['x_col'] = st.sidebar.selectbox(
-        "X Axis",
-        df.columns,
-        index=df.columns.get_loc(st.session_state.eda_config['x_col'])
-        if st.session_state.eda_config['x_col'] in df.columns else 0
-    )
+        if st.session_state.eda_config['filter_col']:
+            unique_values = df[st.session_state.eda_config['filter_col']].unique()
 
-if st.session_state.eda_config['plot_type'] in ["Scatter Plot", "Box Plot", "Violin Plot", "Time Series", "3D Scatter", "Histogram"]:
-    st.session_state.eda_config['y_col'] = st.sidebar.selectbox(
-        "Y Axis",
-        df.columns,
-        index=df.columns.get_loc(st.session_state.eda_config['y_col'])
-        if st.session_state.eda_config['y_col'] in df.columns else 0
-    )
+            st.session_state.eda_config['filter_options'] = st.multiselect(
+                "Filter Values",
+                unique_values,
+                default=unique_values,
+                help=f"Select values from '{st.session_state.eda_config['filter_col']}'"
+            )
+            df = df[df[st.session_state.eda_config['filter_col']].isin(
+                st.session_state.eda_config['filter_options']
+            )]
 
-if st.session_state.eda_config['plot_type'] == "3D Scatter":
-    st.session_state.eda_config['z_col'] = st.sidebar.selectbox(
-        "Z Axis",
-        df.columns,
-        index=df.columns.get_loc(st.session_state.eda_config['z_col'])
-        if st.session_state.eda_config['z_col'] in df.columns else 0
-    )
-    st.session_state.eda_config['color_col'] = st.sidebar.selectbox(
-        "Color by",
-        [None] + list(df.columns)
+    # Visualization Type Selection
+    st.sidebar.header("📊 Visualization Configuration")
+    plot_types = [
+        "Histogram", "Scatter Plot", "Box Plot", "Violin Plot",
+        "Correlation Heatmap", "Parallel Coordinates", "Andrews Curves",
+        "Pair Plot", "Density Contour", "3D Scatter", "Time Series",
+        "Sunburst Chart", "Funnel Chart", "Clustering Analysis"
+    ]
+    st.session_state.eda_config['plot_type'] = st.sidebar.selectbox(
+        "Choose Visualization",
+        plot_types,
+        index=0
     )
 
-# Advanced Plot Customization
-with st.expander("🎨 Advanced Customization", expanded=False):
-    st.session_state.eda_config['color_palette'] = st.selectbox(
-        "Color Palette",
-        ["Viridis", "Plasma", "Magma", "Cividis", "RdBu", "Rainbow"]
-    )
-    st.session_state.eda_config['hover_data_cols'] = st.multiselect(
-        "Hover Data",
-        df.columns
-    )
+    # Dynamic Controls Based on Plot Type
+    if st.session_state.eda_config['plot_type'] != "Correlation Heatmap":
+        st.session_state.eda_config['x_col'] = st.sidebar.selectbox(
+            "X Axis",
+            df.columns,
+            index=df.columns.get_loc(st.session_state.eda_config['x_col'])
+            if st.session_state.eda_config['x_col'] in df.columns else 0
+        )
 
-# Plot Generation
-try:
-    fig = None
-    config = st.session_state.eda_config
-
-    if config['plot_type'] == "Histogram":
-        color_palette = config['color_palette']
-        colors = getattr(pc.sequential, color_palette)
-        fig = px.histogram(
-            df, x=config['x_col'], y=config['y_col'],
-            nbins=30, template="plotly_dark",
-            color=config['x_col'],
-            color_discrete_sequence = [colors[0]]
+    if st.session_state.eda_config['plot_type'] in ["Scatter Plot", "Box Plot", "Violin Plot", "Time Series", "3D Scatter", "Histogram"]:
+        st.session_state.eda_config['y_col'] = st.sidebar.selectbox(
+            "Y Axis",
+            df.columns,
+            index=df.columns.get_loc(st.session_state.eda_config['y_col'])
+            if st.session_state.eda_config['y_col'] in df.columns else 0
         )
 
-    elif config['plot_type'] == "Scatter Plot":
-        fig = px.scatter(
-            df, x=config['x_col'], y=config['y_col'],
-            color=config['color_col'],
-            size=config['size_col'],
-            hover_data=config['hover_data_cols']
+    if st.session_state.eda_config['plot_type'] == "3D Scatter":
+        st.session_state.eda_config['z_col'] = st.sidebar.selectbox(
+            "Z Axis",
+            df.columns,
+            index=df.columns.get_loc(st.session_state.eda_config['z_col'])
+            if st.session_state.eda_config['z_col'] in df.columns else 0
+        )
+        st.session_state.eda_config['color_col'] = st.sidebar.selectbox(
+            "Color by",
+            [None] + list(df.columns)
         )
 
-    elif config['plot_type'] == "3D Scatter":
-        fig = px.scatter_3d(
-            df, x=config['x_col'], y=config['y_col'], z=config['z_col'],
-            color=config['color_col'],
-            color_discrete_sequence=[config['color_palette']]
+    # Advanced Plot Customization
+    with st.expander("🎨 Advanced Customization", expanded=False):
+        st.session_state.eda_config['color_palette'] = st.selectbox(
+            "Color Palette",
+            ["Viridis", "Plasma", "Magma", "Cividis", "RdBu", "Rainbow"]
         )
+        st.session_state.eda_config['hover_data_cols'] = st.multiselect(
+            "Hover Data",
+            df.columns
+        )
+
+    # Plot Generation
+    try:
+        fig = None
+        config = st.session_state.eda_config
 
-    elif config['plot_type'] == "Correlation Heatmap":
-        numeric_df = df.select_dtypes(include=np.number)
-        if not numeric_df.empty:
-            corr = numeric_df.corr()
-            fig = px.imshow(
-                corr, text_auto=True,
-                color_continuous_scale=config['color_palette']
+        if config['plot_type'] == "Histogram":
+            color_palette = config['color_palette']
+            colors = getattr(pc.sequential, color_palette)
+            fig = px.histogram(
+                df, x=config['x_col'], y=config['y_col'],
+                nbins=30, template="plotly_dark",
+                color=config['x_col'],
+                color_discrete_sequence = [colors[0]]
             )
-        else:
-            st.warning("No numerical columns found for correlation heatmap.")
 
-    elif config['plot_type'] == "Box Plot":
-        fig = px.box(
-            df, x=config['x_col'], y=config['y_col'],
-            color=config['color_col']
-        )
+        elif config['plot_type'] == "Scatter Plot":
+            fig = px.scatter(
+                df, x=config['x_col'], y=config['y_col'],
+                color=config['color_col'],
+                size=config['size_col'],
+                hover_data=config['hover_data_cols']
+            )
 
-    elif config['plot_type'] == "Violin Plot":
-        fig = px.violin(
-            df, x=config['x_col'], y=config['y_col'],
-            box=True, points="all",
-            color=config['color_col']
-        )
+        elif config['plot_type'] == "3D Scatter":
+            fig = px.scatter_3d(
+                df, x=config['x_col'], y=config['y_col'], z=config['z_col'],
+                color=config['color_col'],
+                color_discrete_sequence=[config['color_palette']]
+            )
 
-    elif config['plot_type'] == "Time Series":
-        df = df.sort_values(by=config['time_col'])
-        fig = px.line(
-            df, x=config['time_col'], y=config['value_col'],
-            color=config['color_col']
-        )
+        elif config['plot_type'] == "Correlation Heatmap":
+            numeric_df = df.select_dtypes(include=np.number)
+            if not numeric_df.empty:
+                corr = numeric_df.corr()
+                fig = px.imshow(
+                    corr, text_auto=True,
+                    color_continuous_scale=config['color_palette']
+                )
+            else:
+                st.warning("No numerical columns found for correlation heatmap.")
 
-    elif config['plot_type'] == "Scatter Matrix":
-        fig = px.scatter_matrix(
-            df, dimensions=config['scatter_matrix_cols'],
-            color=config['color_col']
-        )
+        elif config['plot_type'] == "Box Plot":
+            fig = px.box(
+                df, x=config['x_col'], y=config['y_col'],
+                color=config['color_col']
+            )
 
-    if fig:
-        st.plotly_chart(fig, use_container_width=True)
-except Exception as e:
-    st.error(f"An error occurred while generating the plot: {e}")
-
-# Statistical Analysis Section
-with st.expander("📊 Statistical Analysis", expanded=True):
-    analysis_type = st.selectbox("Select Analysis Type", [
-        "Descriptive Statistics", 
-        "Correlation Analysis", 
-        "Hypothesis Testing", 
-        "Distribution Fitting"
-    ])
-
-    if analysis_type == "Descriptive Statistics":
-        st.write(df.describe(include='all'))
-
-    elif analysis_type == "Correlation Analysis":
-        numeric_cols = df.select_dtypes(include=np.number).columns
-        if len(numeric_cols) >= 2:
-            corr_method = st.selectbox("Correlation Method", [
-                "Pearson", "Kendall", "Spearman"
-            ])
-            corr_matrix = df[numeric_cols].corr(method=corr_method.lower())
-            st.write(corr_matrix)
-            st.heatmap(corr_matrix, annot=True, cmap=config['color_palette'])
-        else:
-            st.warning("Need at least 2 numeric columns for correlation analysis")
+        elif config['plot_type'] == "Violin Plot":
+            fig = px.violin(
+                df, x=config['x_col'], y=config['y_col'],
+                box=True, points="all",
+                color=config['color_col']
+            )
 
-    elif analysis_type == "Hypothesis Testing":
-        test_type = st.selectbox("Select Test Type", [
-            "T-test", "Chi-Squared Test", "ANOVA", "Mann-Whitney U"
-        ])
-        if test_type == "T-test":
-            col1 = st.selectbox("Column 1 (Numeric)", df.select_dtypes(include=np.number).columns)
-            col2 = st.selectbox("Column 2 (Categorical)", df.select_dtypes(include='object').columns)
-            if st.button("Run T-test"):
-                groups = df.groupby(col2)[col1].apply(list)
-                if len(groups) == 2:
-                    t_stat, p_value = stats.ttest_ind(groups.iloc[0], groups.iloc[1])
-                    st.write(f"T-statistic: {t_stat:.4f}")
-                    st.write(f"P-value: {p_value:.4f}")
-                    if p_value < 0.05:
-                        st.write("Reject the null hypothesis.")
-                    else:
-                        st.write("Fail to reject the null hypothesis.")
-                else:
-                    st.write("Select a categorical column with exactly two categories.")
-
-    elif analysis_type == "Distribution Fitting":
-        numeric_col = st.selectbox("Select Numeric Column", df.select_dtypes(include=np.number).columns)
-        dist_types = ["Normal", "Log-Normal", "Exponential", "Gamma"]
-        selected_dist = st.selectbox("Select Distribution Type", dist_types)
-        if st.button("Fit Distribution"):
-            from scipy.stats import norm, lognorm, expon, gamma
-            dist_functions = {
-                "Normal": norm,
-                "Log-Normal": lognorm,
-                "Exponential": expon,
-                "Gamma": gamma
-            }
-            params = dist_functions[selected_dist].fit(df[numeric_col].dropna())
-            st.write(f"Fitted Parameters: {params}")
-
-# Data Profiling Section
-with st.expander("📝 Generate Full Data Profile", expanded=False):
-    if st.button("🚀 Generate Comprehensive Report"):
-        with st.spinner("Generating report..."):
-            pr = ProfileReport(df, explorative=True)
-            st_profile_report(pr)
+        elif config['plot_type'] == "Time Series":
+            df = df.sort_values(by=config['time_col'])
+            fig = px.line(
+                df, x=config['time_col'], y=config['value_col'],
+                color=config['color_col']
+            )
+
+        elif config['plot_type'] == "Scatter Matrix":
+            fig = px.scatter_matrix(
+                df, dimensions=config['scatter_matrix_cols'],
+                color=config['color_col']
+            )
+
+        if fig:
+            st.plotly_chart(fig, use_container_width=True)
+    except Exception as e:
+        st.error(f"An error occurred while generating the plot: {e}")
                 
 # Model Training Section
 elif app_mode == "Model Training":