Update src/app.py

src/app.py

@@ -1,222 +1,221 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
-from AutoVisualizer.processing import check_dataset_cleanliness, task_type,is_probably_categorical, is_discrete, is_continuous, parse_datetime_columns
-from AutoVisualizer.categorical_viz import combine_figures_as_subplots, generate_count_plots, generate_bar_plots, generate_grouped_bar_plots, generate_pie_plots, generate_categorical_correlation_heatmap
-from AutoVisualizer.numerical_viz import generate_box_plots, generate_numeric_correlation_heatmap, generate_scatter_plots, generate_histograms, generate_line_plots
-
-st.set_page_config(page_title= "Auto-Visualizer",page_icon= "📊",layout="wide")
-# col1, col2 = st.columns([0.3, 0.7])
-
-with st.sidebar:
-    # Upload the dataset file (can upload only CSV, XLSX, JSON, XML)
-    uploaded_file = st.file_uploader("Upload your dataset file:", ["csv", "xlsx", "json", "xml"])
-
-if uploaded_file is not None:
-    file_type = uploaded_file.name
-
-    try:
-        # Read the dataset through pandas
-        if file_type.endswith(".csv"):
-            df = pd.read_csv(uploaded_file)
-        elif file_type.endswith(".xlsx"):
-            df = pd.read_excel(uploaded_file)
-        elif file_type.endswith(".json"):
-            df = pd.read_json(uploaded_file)
-        else:
-            df = pd.read_xml(uploaded_file)
-    except Exception as e:
-        st.write("Error:", e)
-    
-    with st.sidebar:
-        # Show a a Disclaimer to user to upload as clean data as possible
-        st.info("""
-            ⚠️ **Heads up!** For the best experience, please upload a clean dataset.
-            
-            This app is designed for *visualizing data*, not cleaning it.
-            
-            📌 *Tip:* Use the quick checker below to spot potential issues.
-            """)
-
-        if st.button("Run Cleanliness Check"):
-            # Use session state to track button click
-            st.session_state.run_clean_check = True
-        st.divider()
-
-    # Display the cleanliness checker result on the main page (not sidebar) if button was clicked
-    if st.session_state.get("run_clean_check", False):
-        with st.expander("➡️ See Cleanliness Checker Result"):
-            check_dataset_cleanliness(df)
-
-    # Display the user DataFrame
-    st.markdown("Your Dataset:")
-    st.dataframe(df, height=210)
-    st.divider()
-
-    # Extract column names from the dataset + add "No Target" element if there's no target in the user dataset
-    feature_list = list(df.columns)
-    target_selector = ["No Target"] + feature_list
-
-    with st.sidebar:
-        # Ask the user to select target column from their dataset
-        target_col = st.selectbox("Specify the target column in your dataset:", target_selector)
-        # Identify the task/type of dataset (i.e. classification/regression/clustering(if no target feature at all))
-        task = task_type(df, target_col)
-        # Display the task to user
-        st.write(f"🔍 Task identified: **{task}**")
-        
-    # Identify the date-time columns (if any) and extract new time-based components from it
-    # date_time_ls       --> List that will store date-time feature names
-    # extracted_datetime --> List that will store extracted date-time feature names
-    df, date_time_ls, extracted_datetime = parse_datetime_columns(df)
-
-    # Remove date-time feature names as we already extracted time based components from it
-    feature_list = [x for x in feature_list if x not in date_time_ls]
-    
-    categorical_ls = []              # List that will store categorical feature names
-    discrete_ls = []                 # List that will store discrete feature names
-    continuous_ls = []               # List that will store continuous feature names
-    for feature in feature_list:
-        if is_probably_categorical(df[feature]):
-            categorical_ls.append(feature)         # Calling Categorical Feature Identifier Function
-        elif is_discrete(df[feature]):
-            discrete_ls.append(feature)            # Calling Discrete Feature Identifier Function
-        elif is_continuous(df[feature]):
-            continuous_ls.append(feature)          # Calling Continuous Feature Identifier Function
-    # Add time based components that appear to be categorical
-    for feature in extracted_datetime:
-        if is_probably_categorical(df[feature]):
-            categorical_ls.append(feature)
-    
-    # Creating Dialog Box to show identified features
-    @st.dialog("Identified/Extracted Features from your Dataset:-")
-    def open_dialog():
-        if categorical_ls:
-            with st.popover("Categorical Features", use_container_width= True):
-                st.code("\n".join([f"• {item}" for item in categorical_ls]))
-        if discrete_ls:
-            with st.popover("Discrete Features", use_container_width= True):
-                st.code("\n".join([f"• {item}" for item in discrete_ls]))
-        if continuous_ls:
-            with st.popover("Continuous Features", use_container_width= True):
-                st.code("\n".join([f"• {item}" for item in continuous_ls]))
-        if date_time_ls:
-            with st.popover("Date-Time Features", use_container_width= True):
-                st.code("\n".join([f"• {item}" for item in date_time_ls]))
-            with st.popover("Extracted features from your Date-Time like features", use_container_width= True):
-                st.code("\n".join([f"• {item}" for item in extracted_datetime]))
-    with st.sidebar:
-        # Calling the dialog box through a button
-        if st.button("See Your Feature Details"):
-            open_dialog()
-
-    # Generate the Plots
-    with st.spinner("Generating Plots.....", show_time= True):
-        if categorical_ls:
-            st.header("📊 Categorical Plots")
-            # 1. Count Plots
-            count_plots = []
-            for x_col in categorical_ls:
-                if df[x_col].nunique() <= 20:
-                    count_plots.extend(generate_count_plots(df, x_col))
-            
-            if count_plots:
-                st.subheader("Count Plots :-")
-                st.plotly_chart(combine_figures_as_subplots(count_plots), use_container_width=True)
-            
-            # 2. Bar Plots
-            bar_plots = []
-            # (Categorical vs Discrete + Continuous)
-            for x_col in categorical_ls:
-                if df[x_col].nunique() <= 20:
-                    bar_plots.extend(generate_bar_plots(df, x_col, discrete_ls + continuous_ls))
-
-            if bar_plots:
-                st.subheader("Bar Plots :-")
-                st.plotly_chart(combine_figures_as_subplots(bar_plots), use_container_width=True)
-            
-            # 3. Grouped Bar Plots
-            grp_bar_plots = []
-            # (Categorical vs Discrete + Continuous)
-            grp_bar_plots.extend(generate_grouped_bar_plots(df, categorical_ls, discrete_ls + continuous_ls))
-
-            if grp_bar_plots:
-                st.subheader("Grouped Bar Plots :-")
-                st.plotly_chart(combine_figures_as_subplots(grp_bar_plots), use_container_width=True)
-            
-            # 4. Pie Charts
-            pie_plots = []
-            for x_col in categorical_ls:
-                if df[x_col].nunique() <= 20:
-                    pie_plots.extend(generate_pie_plots(df, x_col))
-
-            if pie_plots:
-                st.subheader("Pie Charts :-")
-                st.plotly_chart(combine_figures_as_subplots(pie_plots), use_container_width=True)
-
-        if continuous_ls:
-            st.header("📊 Numerical Plots")
-            # 5. Box Plots
-            box_plots = []
-            for x_col in categorical_ls:
-                if df[x_col].nunique() <= 10:
-                    box_plots.extend(generate_box_plots(df, x_col, continuous_ls))
-
-            if box_plots:
-                st.subheader("Box Plots :-")
-                st.plotly_chart(combine_figures_as_subplots(box_plots), use_container_width=True)
-            
-            # 6. Heat Maps
-            heat_maps = []
-            if task == 'Regression':
-                if categorical_ls:
-                    heat_maps.extend(generate_categorical_correlation_heatmap(df, target_col, categorical_ls))
-            heat_maps.extend(generate_numeric_correlation_heatmap(df[continuous_ls]))
-            if heat_maps: 
-                st.subheader("Heat Maps :-")
-                st.plotly_chart(combine_figures_as_subplots(heat_maps), use_container_width=True)
-            
-            # 7. Scatter Plots
-            if len(continuous_ls) >= 2:
-                st.subheader("Scatter Plots")
-            scatter_plots = []
-            # Creation of unique feature pairs (no repetition like (B, A) if (A, B) is already used)
-            feature_pairs = []
-            for i in range(len(continuous_ls)):
-                for j in range(i + 1, len(continuous_ls)):
-                    feature_pairs.append((continuous_ls[i], continuous_ls[j]))
-            selection = st.pills("Highlight using a categorical feature :- ", categorical_ls)
-            scatter_plots.extend(generate_scatter_plots(df, feature_pairs, selection))
-            if scatter_plots:
-                st.plotly_chart(combine_figures_as_subplots(scatter_plots), use_container_width=True)
-            
-            # 8. Histograms
-            histograms = []
-            histograms.extend(generate_histograms(df, continuous_ls))
-
-            if histograms:
-                st.subheader("Histograms")
-                st.plotly_chart(combine_figures_as_subplots(histograms), use_container_width=True)
-            
-            # 9. Line Plots
-            line_plots = []
-            if date_time_ls:
-                # Extract only date-related components
-                date_related_keywords = ['_year', '_month', '_day', '_weekday']
-                date_component_cols = [col for col in extracted_datetime if any(key in col for key in date_related_keywords)]
-                if date_component_cols:
-                    st.subheader("Line Plots :-")
-                    # Mapping of labels to values
-                    time_grouping_options = {
-                        "Daily": "D",
-                        "Weekly": "W",
-                        "Monthly": "ME",
-                        "Yearly": "YE"
-                    }
-                    time_choice  = st.pills("Choose time interval for grouping :- ", list(time_grouping_options.keys()))
-                    # Extract the actual value for resampling
-                    selected_freq = time_grouping_options[time_choice] if time_choice else "ME"  # Use default "ME" if no selection
-                    line_plots.extend(generate_line_plots(df, date_component_cols, continuous_ls, selected_freq))
-            
-            if line_plots:
-                st.plotly_chart(combine_figures_as_subplots(line_plots), use_container_width= True)
+import streamlit as st
+import numpy as np
+import pandas as pd
+from AutoVisualizer.processing import check_dataset_cleanliness, task_type,is_probably_categorical, is_discrete, is_continuous, parse_datetime_columns
+from AutoVisualizer.categorical_viz import combine_figures_as_subplots, generate_count_plots, generate_bar_plots, generate_grouped_bar_plots, generate_pie_plots, generate_categorical_correlation_heatmap
+from AutoVisualizer.numerical_viz import generate_box_plots, generate_numeric_correlation_heatmap, generate_scatter_plots, generate_histograms, generate_line_plots
+
+# st.set_page_config(page_title= "Auto-Visualizer",page_icon= "📊",layout="wide")
+
+with st.sidebar:
+    # Upload the dataset file (can upload only CSV, XLSX, JSON, XML)
+    uploaded_file = st.file_uploader("Upload your dataset file:", ["csv", "xlsx", "json", "xml"])
+
+if uploaded_file is not None:
+    file_type = uploaded_file.name
+
+    try:
+        # Read the dataset through pandas
+        if file_type.endswith(".csv"):
+            df = pd.read_csv(uploaded_file)
+        elif file_type.endswith(".xlsx"):
+            df = pd.read_excel(uploaded_file)
+        elif file_type.endswith(".json"):
+            df = pd.read_json(uploaded_file)
+        else:
+            df = pd.read_xml(uploaded_file)
+    except Exception as e:
+        st.write("Error:", e)
+    
+    with st.sidebar:
+        # Show a a Disclaimer to user to upload as clean data as possible
+        st.info("""
+            ⚠️ **Heads up!** For the best experience, please upload a clean dataset.
+            
+            This app is designed for *visualizing data*, not cleaning it.
+            
+            📌 *Tip:* Use the quick checker below to spot potential issues.
+            """)
+
+        if st.button("Run Cleanliness Check"):
+            # Use session state to track button click
+            st.session_state.run_clean_check = True
+        st.divider()
+
+    # Display the cleanliness checker result on the main page (not sidebar) if button was clicked
+    if st.session_state.get("run_clean_check", False):
+        with st.expander("➡️ See Cleanliness Checker Result"):
+            check_dataset_cleanliness(df)
+
+    # Display the user DataFrame
+    st.markdown("Your Dataset:")
+    st.dataframe(df, height=210)
+    st.divider()
+
+    # Extract column names from the dataset + add "No Target" element if there's no target in the user dataset
+    feature_list = list(df.columns)
+    target_selector = ["No Target"] + feature_list
+
+    with st.sidebar:
+        # Ask the user to select target column from their dataset
+        target_col = st.selectbox("Specify the target column in your dataset:", target_selector)
+        # Identify the task/type of dataset (i.e. classification/regression/clustering(if no target feature at all))
+        task = task_type(df, target_col)
+        # Display the task to user
+        st.write(f"🔍 Task identified: **{task}**")
+        
+    # Identify the date-time columns (if any) and extract new time-based components from it
+    # date_time_ls       --> List that will store date-time feature names
+    # extracted_datetime --> List that will store extracted date-time feature names
+    df, date_time_ls, extracted_datetime = parse_datetime_columns(df)
+
+    # Remove date-time feature names as we already extracted time based components from it
+    feature_list = [x for x in feature_list if x not in date_time_ls]
+    
+    categorical_ls = []              # List that will store categorical feature names
+    discrete_ls = []                 # List that will store discrete feature names
+    continuous_ls = []               # List that will store continuous feature names
+    for feature in feature_list:
+        if is_probably_categorical(df[feature]):
+            categorical_ls.append(feature)         # Calling Categorical Feature Identifier Function
+        elif is_discrete(df[feature]):
+            discrete_ls.append(feature)            # Calling Discrete Feature Identifier Function
+        elif is_continuous(df[feature]):
+            continuous_ls.append(feature)          # Calling Continuous Feature Identifier Function
+    # Add time based components that appear to be categorical
+    for feature in extracted_datetime:
+        if is_probably_categorical(df[feature]):
+            categorical_ls.append(feature)
+    
+    # Creating Dialog Box to show identified features
+    @st.dialog("Identified/Extracted Features from your Dataset:-")
+    def open_dialog():
+        if categorical_ls:
+            with st.popover("Categorical Features", use_container_width= True):
+                st.code("\n".join([f"• {item}" for item in categorical_ls]))
+        if discrete_ls:
+            with st.popover("Discrete Features", use_container_width= True):
+                st.code("\n".join([f"• {item}" for item in discrete_ls]))
+        if continuous_ls:
+            with st.popover("Continuous Features", use_container_width= True):
+                st.code("\n".join([f"• {item}" for item in continuous_ls]))
+        if date_time_ls:
+            with st.popover("Date-Time Features", use_container_width= True):
+                st.code("\n".join([f"• {item}" for item in date_time_ls]))
+            with st.popover("Extracted features from your Date-Time like features", use_container_width= True):
+                st.code("\n".join([f"• {item}" for item in extracted_datetime]))
+    with st.sidebar:
+        # Calling the dialog box through a button
+        if st.button("See Your Feature Details"):
+            open_dialog()
+
+    # Generate the Plots
+    with st.spinner("Generating Plots.....", show_time= True):
+        if categorical_ls:
+            st.header("📊 Categorical Plots")
+            # 1. Count Plots
+            count_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    count_plots.extend(generate_count_plots(df, x_col))
+            
+            if count_plots:
+                st.subheader("Count Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(count_plots), use_container_width=True)
+            
+            # 2. Bar Plots
+            bar_plots = []
+            # (Categorical vs Discrete + Continuous)
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    bar_plots.extend(generate_bar_plots(df, x_col, discrete_ls + continuous_ls))
+
+            if bar_plots:
+                st.subheader("Bar Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(bar_plots), use_container_width=True)
+            
+            # 3. Grouped Bar Plots
+            grp_bar_plots = []
+            # (Categorical vs Discrete + Continuous)
+            grp_bar_plots.extend(generate_grouped_bar_plots(df, categorical_ls, discrete_ls + continuous_ls))
+
+            if grp_bar_plots:
+                st.subheader("Grouped Bar Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(grp_bar_plots), use_container_width=True)
+            
+            # 4. Pie Charts
+            pie_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    pie_plots.extend(generate_pie_plots(df, x_col))
+
+            if pie_plots:
+                st.subheader("Pie Charts :-")
+                st.plotly_chart(combine_figures_as_subplots(pie_plots), use_container_width=True)
+
+        if continuous_ls:
+            st.header("📊 Numerical Plots")
+            # 5. Box Plots
+            box_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 10:
+                    box_plots.extend(generate_box_plots(df, x_col, continuous_ls))
+
+            if box_plots:
+                st.subheader("Box Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(box_plots), use_container_width=True)
+            
+            # 6. Heat Maps
+            heat_maps = []
+            if task == 'Regression':
+                if categorical_ls:
+                    heat_maps.extend(generate_categorical_correlation_heatmap(df, target_col, categorical_ls))
+            heat_maps.extend(generate_numeric_correlation_heatmap(df[continuous_ls]))
+            if heat_maps: 
+                st.subheader("Heat Maps :-")
+                st.plotly_chart(combine_figures_as_subplots(heat_maps), use_container_width=True)
+            
+            # 7. Scatter Plots
+            if len(continuous_ls) >= 2:
+                st.subheader("Scatter Plots")
+            scatter_plots = []
+            # Creation of unique feature pairs (no repetition like (B, A) if (A, B) is already used)
+            feature_pairs = []
+            for i in range(len(continuous_ls)):
+                for j in range(i + 1, len(continuous_ls)):
+                    feature_pairs.append((continuous_ls[i], continuous_ls[j]))
+            selection = st.pills("Highlight using a categorical feature :- ", categorical_ls)
+            scatter_plots.extend(generate_scatter_plots(df, feature_pairs, selection))
+            if scatter_plots:
+                st.plotly_chart(combine_figures_as_subplots(scatter_plots), use_container_width=True)
+            
+            # 8. Histograms
+            histograms = []
+            histograms.extend(generate_histograms(df, continuous_ls))
+
+            if histograms:
+                st.subheader("Histograms")
+                st.plotly_chart(combine_figures_as_subplots(histograms), use_container_width=True)
+            
+            # 9. Line Plots
+            line_plots = []
+            if date_time_ls:
+                # Extract only date-related components
+                date_related_keywords = ['_year', '_month', '_day', '_weekday']
+                date_component_cols = [col for col in extracted_datetime if any(key in col for key in date_related_keywords)]
+                if date_component_cols:
+                    st.subheader("Line Plots :-")
+                    # Mapping of labels to values
+                    time_grouping_options = {
+                        "Daily": "D",
+                        "Weekly": "W",
+                        "Monthly": "ME",
+                        "Yearly": "YE"
+                    }
+                    time_choice  = st.pills("Choose time interval for grouping :- ", list(time_grouping_options.keys()))
+                    # Extract the actual value for resampling
+                    selected_freq = time_grouping_options[time_choice] if time_choice else "ME"  # Use default "ME" if no selection
+                    line_plots.extend(generate_line_plots(df, date_component_cols, continuous_ls, selected_freq))
+            
+            if line_plots:
+                st.plotly_chart(combine_figures_as_subplots(line_plots), use_container_width= True)