Update app.py

app.py

@@ -1,17 +1,261 @@
 import streamlit as st
+import os
+from streamlit_option_menu import option_menu
+import pandas as pd
+import plotly.express as px
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
 from streamlit_ace import st_ace
+from streamlit_pandas_profiling import st_profile_report
 
-code= "def f1(x): return str(x * 3)"
-exec(code)
-st.write(f1(3))
 
-content = st_ace(language="python")
 
-st.write(len(content.splitlines()))
-exec(content)
-code= "def f1(x): return str(x * 3)"
- 
-exec(code)
-st.text(content)
-st.write(f1(3))
-st.write(test_fun(3))
+def set_data_files_session_object(file_name, file_path):
+    if 'data_files' not in st.session_state:
+        files_dictionary = {}
+        files_dictionary[file_name] = file_path
+        st.session_state['data_files'] = files_dictionary
+    else:
+        files_dictionary = st.session_state['data_files']
+        files_dictionary[file_name] = file_path
+        st.session_state['data_files'] = files_dictionary
+
+def set_filtered_data_session_object(df, file_name):
+    if 'filtered_data' not in st.session_state:
+        filtered_data_dictionary = {}
+        filtered_data_dictionary[file_name] = df
+        st.session_state['filtered_data'] = filtered_data_dictionary
+    else:
+        filtered_data_dictionary = st.session_state['filtered_data']
+        filtered_data_dictionary[file_name] = df
+        st.session_state['filtered_data'] = filtered_data_dictionary
+
+def set_dataframe_session_object(file_name, file_path):
+    if 'data_frames' not in st.session_state:
+        data_frame_dictionary = {}
+        data_frame_dictionary[file_name] = pd.read_csv(file_path)
+        st.session_state['data_frames'] = data_frame_dictionary
+    else:
+        data_frame_dictionary = st.session_state['data_frames']
+        data_frame_dictionary[file_name] = pd.read_csv(file_path)
+        st.session_state['data_frames'] = data_frame_dictionary
+        
+def save_file(file_object):
+    file_path = os.path.join(os.getcwd(), "uploaded_files", file_object.name)
+    with open(file_path, "wb") as f:
+        f.write(file_object.getbuffer())
+    
+    set_data_files_session_object(file_object.name, file_path)
+    set_dataframe_session_object(file_object.name, file_path)
+    
+
+
+
+def create_upload_file_component():
+    uploaded_files = st.file_uploader("Upload one file at a time.", type=['csv', 'xls', 'xlsx', 'pkl', 'pdf'],
+                                      accept_multiple_files=True)
+
+    if uploaded_files:
+
+        os.makedirs(os.path.join(os.getcwd(), "uploaded_files"), mode=0o777, exist_ok=True)
+        for uploaded_file in uploaded_files:
+            save_file(uploaded_file)
+
+def create_component_to_add_target_func(selected_files, dfs, i):
+    target_var_name = st.text_input("Name of the target variable",key="target_var" + str(i))
+#     content = st_ace(language="python")
+#     if content:
+    code= "def f1(x): return str(x * 3)"
+    exec(code)
+    st.write(f1(3))
+
+#         st.write(len(content.splitlines()))
+#         exec(content)
+#         code= "def f1(x): return str(x * 3)"
+   
+#         exec(code)
+#         st.text(content)
+#         st.write(f1(3))
+
+
+def create_component_for_analysis_for_single_df(selected_files, dfs, i):
+    st.subheader(selected_files[i])
+    df = dfs[selected_files[i]]
+
+    filter_data = st.checkbox("Analyse on Filtered Data",key="filter_data_check"+str(i))
+    
+    if filter_data:
+        action = "data_filter"
+        col_to_filter = st.selectbox("Select the field to Filter on ", df.columns.values,
+                                     key= action + "_col_filter_" + str(i))
+        filter_operation = st.selectbox("Operation ",
+                                        ['Greater Than', 'Equals', 'Less Than', "In", "In Between"],
+                                        key=action + "_col_filter_op_" + str(i))
+        selected_filter_vals = None
+        
+        if filter_operation:
+            if filter_operation == 'In':
+                selected_filter_vals = st.multiselect("Select Values to Filter on ", df[col_to_filter].unique(),
+                                                      key=action + "_col_filter_val_" + str(i))
+                if selected_filter_vals:
+                    filtered_df = df[df[col_to_filter].isin(selected_filter_vals)]
+            elif filter_operation == 'Equals':
+                selected_filter_vals = st.text_input("Enter a numeric value",
+                                                     key=action + "_col_filter_val_" + str(i))
+                if selected_filter_vals:
+                    filtered_df = df[df[col_to_filter] == selected_filter_vals]
+            elif filter_operation == 'Greater Than':
+                selected_filter_vals = st.text_input("Enter a numeric value",
+                                                     key=action + "_col_filter_val_" + str(i))
+                if selected_filter_vals:
+                    filtered_df = df[df[col_to_filter] > selected_filter_vals]
+            elif filter_operation == 'Less Than':
+                selected_filter_vals = st.text_input("Enter a numeric value",
+                                                     key=action + "_col_filter_val_" + str(i))
+                if selected_filter_vals:
+                    filtered_df = df[df[col_to_filter] < selected_filter_vals]
+            elif filter_operation == 'In Between':
+                selected_filter_vals = st.select_slider("Select range",
+                                                        (df[col_to_filter].min(), df[col_to_filter].max()),
+                                                        key=action + "_col_filter_val_" + str(i))
+                if selected_filter_vals:
+                    filtered_df = df[df[col_to_filter] < selected_filter_vals]
+            
+            if selected_filter_vals:
+                set_filtered_data_session_object(filtered_df,selected_files[i])
+#                 st.write(df.shape)
+#                 st.write( st.session_state['filtered_data'][selected_files[i]].shape)
+
+    analysis_actions = st.multiselect("What analysis do you wish to do?",
+                                      ['Summary of Data', 'Sample Data','Get Profile' ,'Univariate Analysis',
+                                       'Bivariate Analysis','Add a Target Column'], key='analysis_action_' + str(i))
+    if analysis_actions:
+        
+        df_for_analysis = st.session_state['filtered_data'][selected_files[i]] if filter_data else df
+                                                                                          
+        for action in analysis_actions:
+
+            if action == 'Sample Data':
+                st.write(df_for_analysis.sample(10))
+            elif action == 'Get Profile':
+                
+                pr = df_for_analysis.profile_report()
+                st_profile_report(pr)
+                
+            elif action == 'Summary of Data':
+                st.write(df_for_analysis.describe())
+#                 col_to_filter = st.selectbox("Select the field to Filter on ", df.columns.values,
+#                                              key=action + "_col_filter_" + str(i))
+#                 selected_filter_vals = st.multiselect("Select Values to Filter on ", df[col_to_filter].unique(),
+#                                                       key=action + "_col_filter_val_" + str(i))
+            elif action == 'Univariate Analysis':
+                cols_for_analysis = st.multiselect("Select Columns for Univariate Analysis",options= df_for_analysis.columns.values)
+                for col in cols_for_analysis:
+                    if str(df_for_analysis[col].dtype) in ['int64','float64'] and df_for_analysis[col].nunique() > 10 :
+                        
+                        fig = px.scatter(x=df_for_analysis.index, y=df_for_analysis[col],labels=dict(x="Index", y=col))
+                        st.plotly_chart(fig, use_container_width=True)
+
+                    elif str(df_for_analysis[col].dtype) in ['object','category'] or df_for_analysis[col].nunique() <= 10:
+                        
+                        value_dist_df = df_for_analysis[col].value_counts(normalize=True)[:20].reset_index()
+                        value_dist_df.columns = [col,'% Distribution']
+                        
+                        value_dist_df_counts = df_for_analysis[col].value_counts()[:20].reset_index()
+                        value_dist_df_counts.columns = [col,'Count']
+                        value_dist_df = value_dist_df.merge(value_dist_df_counts,on=col)
+                        
+                        trace1 = go.Bar(x=value_dist_df[col],y=value_dist_df['Count'],name='Count',marker=dict(color='rgb(34,163,192)'))
+                        trace2 = go.Scatter(x=value_dist_df[col],y=value_dist_df['% Distribution'],name='% Distribution',yaxis='y2')
+
+                        fig = make_subplots(specs=[[{"secondary_y": True}]])
+                        fig.add_trace(trace1)
+                        fig.add_trace(trace2,secondary_y=True)
+                        
+                        fig['layout'].update(height = 600, width = 800, title = f"{col} data distribution",xaxis=dict(tickangle=-90))
+                        
+#                         fig.update_layout(height=200, width=400, title_text=f"{col} data distribution")
+                        
+                        st.plotly_chart(fig, use_container_width=True)
+        
+            elif action == "Add a Target Column":
+#                 create_component_to_add_target_func(selected_files, dfs, i)
+                code= "def f1(x): return str(x * 3)"
+                exec(code)
+                st.write(f1(3))
+                        
+            
+def create_component_for_data_analysis():
+    if 'data_files' in st.session_state:
+
+        selected_files = st.multiselect("Select the File(S) to analyze", st.session_state['data_files'].keys())
+
+        if selected_files:
+            cols = st.columns(len(selected_files))
+
+            dfs = {}
+
+            for selected_file in selected_files:
+                if selected_file in  st.session_state['data_frames']:
+                    dfs[selected_file] =  st.session_state['data_frames'][selected_file]
+                else:
+                    st.session_state['data_frames'][selected_file] = pd.read_csv(st.session_state['data_files'][selected_file])
+                    dfs[selected_file] = st.session_state['data_frames'][selected_file]
+
+            for i, col in enumerate(cols):
+                with col:
+                    create_component_for_analysis_for_single_df(selected_files, dfs, i)
+
+    else:
+        st.write("Upload a file to start analysis")
+
+
+# def build_interface_for_model_analysis():
+st.title("Model Results Analyzer")
+with st.sidebar:
+    
+
+    selected_menu = option_menu(None, ["Home", "Upload Data", "Add Features","Analyze Data"],
+                                icons=['house', 'cloud-upload', "list-task", 'gear'],
+                                menu_icon="cast", default_index=0, orientation="vertical",
+                                styles={
+                                    "container": {"padding": "0!important", "background-color": "#fafafa"},
+                                    "icon": {"color": "orange", "font-size": "15px"},
+                                    "nav-link": {"font-size": "15px", "text-align": "left", "margin": "0px",
+                                                 "--hover-color": "#eee"},
+                                    "nav-link-selected": {"background-color": "green"},
+                                })
+
+if selected_menu == "Home":
+    st.markdown('**This is to analyse models performance.**')
+
+elif selected_menu == "Upload Data":
+
+    create_upload_file_component()
+
+    if 'data_files' in st.session_state:
+        st.write(pd.DataFrame(
+            data={"File Name": pd.DataFrame.from_dict(st.session_state['data_files'], orient='index').index}))
+
+elif selected_menu == "Analyze Data":
+    create_component_for_data_analysis()
+
+elif selected_menu == "Add Features":
+    if 'data_files' in st.session_state:
+        selected_file = st.selectbox("Select the File(S) to analyze", st.session_state['data_files'].keys())
+        
+        if selected_file:
+            df =  st.session_state['data_frames'][selected_file]
+            st.header("Enter the function definiton to create a new feature")
+            feature_name = st.text_input("Enter the New Feature Name")
+            st.warning("please retain the function signature as 'add_feature(row)'")
+            
+            content = st_ace(language="python",value="def add_feature(row):")
+            
+            if content != 'def add_feature(row):':
+                exec(content)
+                df[feature_name] = df.apply(lambda x:add_feature(x),axis=1)
+               
+                st.session_state['data_frames'][selected_file] = df
+                st.write(df.columns.values)
+