Py.Cafe

import streamlit as st
import pandas as pd
import time
#from st_aggrid import AgGrid

# Set page layout
st.set_page_config(layout="wide", page_title="Test Cases Prediction App")

# Initialize session state for active tab, progress, and file upload
if 'active_tab' not in st.session_state:
    st.session_state.active_tab = None#'📂 Load CSV'
if 'progress' not in st.session_state:
    st.session_state.progress = 0
if 'file_uploaded' not in st.session_state:
    st.session_state.file_uploaded = False
if 'predict_done' not in st.session_state:
    st.session_state.predict_done = False
if "prediction_results_shown" not in st.session_state:
    st.session_state.prediction_results_shown = False
if "deploy_done" not in st.session_state:
    st.session_state.deploy_done = False

# Define tab styles
tab_styles = {
    'Load Input File': 'black',
    'Predict': 'black',
    'Prediction Results': 'black',
    'Deploy': 'black'
}

# Update active tab style
if st.session_state.active_tab:
    tab_styles[st.session_state.active_tab] = 'blue'

# Create a sidebar for buttons
with st.sidebar:
    st.image(r"Logo.jpg", width=None)
    if st.button('📂 Load Deployment details', use_container_width=True):
        st.session_state.active_tab = 'Load Input File'
        #st.session_state.progress = 25
        st.rerun()
    if st.button('📈 Predict', use_container_width=True, disabled=not st.session_state.file_uploaded):
        st.session_state.active_tab = 'Predict'
        st.session_state.progress = 50
        st.rerun()
    if st.button('💡Prediction Results', use_container_width=True, disabled=not st.session_state.predict_done):
        st.session_state.active_tab = 'Prediction Results'
        st.session_state.progress = 75
        st.rerun()
    if st.button('🚀 Deploy', use_container_width=True, disabled=not st.session_state.prediction_results_shown):
        st.session_state.active_tab = 'Deploy'
        st.session_state.deploy_done = True
        st.session_state.progress = 100
        st.rerun()

# Create tabs aligned horizontally on the upper side
# Main content area (70% width)

# with open(r"C:\Users\SKumar28\Desktop\QA_AI_streamlit\ST_APP\assests\styles.css") as f:
#         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

with st.container():
    # st.markdown(f"### <span style='color:{tab_styles['Predict']};background-color: #2471a3'>", unsafe_allow_html=True)
    st.title("Application Test Plan AI Framework")
    # background_color = "lightblue"
    # Load the CSS file
    # with open(r"C:\Users\SKumar28\Desktop\QA_AI_streamlit\ST_APP\assests\styles.css") as f:
    #     st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
    tab1, tab2, tab3, tab4 = st.columns(4)
    with tab1: 
        st.markdown(f"### <span style='color:{tab_styles['Load Input File']}; font-size: 24px; height: 13vh; border-radius: 10px; text-align: center'>Load Input File </span>", unsafe_allow_html=True)
        #st.markdown('<div class="column">📂Load Deployment details</div>', unsafe_allow_html=True)
        #st.markdown(f"""<style> .st-column {background-color: {background_color}; } </style> """, unsafe_allow_html=True)
    with tab2: 
        st.markdown(f"### <span style='color:{tab_styles['Predict']};b font-size: 24px; height: 13vh; text-align: center'>Predict</span>", unsafe_allow_html=True)
        #st.markdown('<div class="column">🤖 Predict</div>', unsafe_allow_html=True)
    with tab3: 
        st.markdown(f"### <span style='color:{tab_styles['Prediction Results']}; font-size: 24px; height: 13vh; text-align: center'>Prediction Results</span>", unsafe_allow_html=True)
    with tab4: 
        st.markdown(f"### <span style='color:{tab_styles['Deploy']}; font-size: 24px; height: 13vh; text-align: center'>Deploy</span>", unsafe_allow_html=True)


# Display progress bar
progress_bar = st.progress(st.session_state.progress)

# Function to load CSV and show summary
# def load_csv():
#     uploaded_file = st.file_uploader("Browse input file for prediction", type=["csv"])
#     if uploaded_file is not None:
#         df = pd.read_csv(uploaded_file)
#         df_grouped= df.groupby('Feature').agg({'File Changed': ['sum','count']})
#         df_grouped.columns =  ['File Changed Count', 'File Count']
#         df_grouped= df_grouped.reset_index()
#         st.write("Code File changes summary:")
#         st.dataframe(df_grouped, hide_index=True)
#         # Mark file as uploaded and enable Predict button
#         st.session_state.file_uploaded = True
#         st.session_state.progress = 25

# Blank screen until an action is performed
if not st.session_state.active_tab:
    st.write("🔹 **Click an option from the left navigation to begin.**")
# Display content based on active tab

elif st.session_state.active_tab == 'Load Input File':
#    load_csv()
    uploaded_file = st.file_uploader("Browse input file for prediction", type=["csv"])
    if uploaded_file is not None:
        df = pd.read_csv(uploaded_file)
        st.session_state.file_uploaded = True
        df_grouped= df.groupby('Feature').agg({'File Changed': ['sum','count']})
        df_grouped.columns =  ['File Changed Count', 'File Count']
        df_grouped= df_grouped.reset_index()
        st.write("Code File changes summary:")
        st.dataframe(df_grouped, hide_index=True, use_container_width=True)#, width=600, height=300)
        #AgGrid(df_grouped)
        # Mark file as uploaded and enable Predict button
        st.session_state.file_uploaded = True
        st.session_state.progress = 25
#    st.session_state.progress = 25
elif st.session_state.active_tab == 'Predict':
    # prediction_summary = predict()
    # st.write(prediction_summary)
    if st.session_state.file_uploaded:# is not None:
#        st.session_state.csv_uploaded = None
        model_list= ['MLP', 'Reinforcement MLP']
        #st.header("Select the model")
        model_select= st.selectbox("Select the model for prediction", options= model_list)
        with st.spinner("Running predictions..."):
            time.sleep(2)  # Simulating processing time
            df = pd.read_csv("https://py.cafe/files/surajbhu/streamlit_TC/TestCasePrediction.csv")
            st.session_state.df_predictions = df
            st.session_state.predict_done = True
            st.success("✅ Prediction complete!")
            threshold_value = st.slider("Decision Threshold", min_value=0.00, max_value=1.00, value=0.52)
            col1, col2 = st.columns(2)
            # Display image in the first column
            with col1:
                st.image("https://py.cafe/files/surajbhu/streamlit_TC/Threshold.png", width=400)
                # Display dataframe in the second column
            with col2:       
                df['Risky/Non Risky'] = df.apply(lambda row: 0 if row['Prob'] < threshold_value else 1, axis=1)
                prediction= df.groupby('Feature').agg({'Risky/Non Risky': ['sum','count']})
                prediction.columns =  ['Risky Test Cases Count', 'Total Test Cases Count']
                prediction= prediction.reset_index()
                st.write(threshold_value)
                st.dataframe(prediction, hide_index=True, use_container_width=True)
                      


elif st.session_state.active_tab == 'Prediction Results':
    if st.session_state.predict_done:
        pred_count = st.session_state.df_predictions["Risky/Non Risky Prediction"].value_counts()
        pred_count.columns =  ['Count']
        pred_count= pred_count.reset_index()
        percentages = st.session_state.df_predictions["Risky/Non Risky Prediction"].value_counts(normalize=True) * 100
        percentages = percentages.reset_index()
        percentages.columns =  ['Risky/Non Risky Prediction','Percentage']
        pred_summary= pd.merge(pred_count, percentages, on= 'Risky/Non Risky Prediction')
        st.write("### Prediction Summary")
        st.dataframe(pred_summary, hide_index=True, use_container_width=True)

        st.write("### Risky Test Cases by feature")
        CF= pd.read_csv("https://py.cafe/files/surajbhu/streamlit_TC/CF.csv")
        CF_summary= CF.groupby('Feature').agg({'File Changed': ['sum','count']})
        CF_summary.columns =  [ 'Changed CF Count', 'Total CF Count']
        CF_summary= CF_summary.reset_index()
        pred_data= st.session_state.df_predictions.groupby('Feature').agg({'Risky/Non Risky Prediction': ['sum','count']})
        pred_data.columns =  ['Risky Test Cases Count', 'Total Test Cases Count']
        pred_data= pred_data.reset_index()
        summarydata= pd.merge(CF_summary,pred_data, on= 'Feature')
        st.dataframe(summarydata, hide_index=True, use_container_width=True)

        # Download predictions
        st.download_button(
            label="📥 Download Predictions",
            data=st.session_state.df_predictions.to_csv(index=False),
            file_name="predictions_output.csv",
            mime="text/csv"
        )
        st.session_state.prediction_results_shown=True
    # prediction_results_summary = show_prediction_results()
    # st.write(prediction_results_summary)
elif st.session_state.active_tab == 'Deploy':
    if st.session_state.prediction_results_shown:
        st.write("✅ **Test cases deployed for testing.**")
        df_prediction = pd.read_csv("https://py.cafe/files/surajbhu/streamlit_TC/TestCasePrediction.csv")
        Risky_Testcase= df_prediction[df_prediction['Risky/Non Risky Prediction']==1]
        st.dataframe(Risky_Testcase, hide_index=True, use_container_width=True, height=500)