Update my_pages/information_loss.py
Browse files- my_pages/information_loss.py +47 -22
my_pages/information_loss.py
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import streamlit as st
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from utils import go_to
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import random
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# Sample list of possible features
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ALL_FEATURES = [
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"Credit Score", "Annual Income", "Loan Amount Requested",
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"Number of Previous Loans", "Debt-to-Income Ratio",
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def render():
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st.title("Information Loss Demo")
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#
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if "available_features" not in st.session_state:
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st.session_state.available_features = ALL_FEATURES.copy()
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if "selected_features" not in st.session_state:
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st.session_state.selected_features = []
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# ---
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col1, col2 = st.columns([1,
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#
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with col1:
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st.image(
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"https://cdn-icons-png.flaticon.com/512/1048/1048949.png",
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caption="Loan Applicant",
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width=200
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)
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#
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with col2:
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st.markdown("### Available Features
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for feature in st.session_state.available_features:
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#
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if "show_message" in st.session_state:
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st.markdown(
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f"<div style='text-align:center; color:red; font-weight:bold;'>{st.session_state.show_message}</div>",
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unsafe_allow_html=True
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)
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del st.session_state.show_message
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st.markdown("---")
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# --- BOTTOM HALF: Dataset table ---
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st.markdown("### Current Dataset")
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if st.session_state.selected_features:
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# Create table with placeholder values
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import pandas as pd
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cols = st.session_state.selected_features + ["Target Label"]
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df = pd.DataFrame(columns=cols)
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st.dataframe(df, use_container_width=True)
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else:
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st.info("No features added yet. Click a feature above to add it to the dataset.")
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import streamlit as st
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from utils import go_to
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import random
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import pandas as pd
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ALL_FEATURES = [
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"Credit Score", "Annual Income", "Loan Amount Requested",
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"Number of Previous Loans", "Debt-to-Income Ratio",
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def render():
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st.title("Information Loss Demo")
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# --- SESSION STATE ---
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if "available_features" not in st.session_state:
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st.session_state.available_features = ALL_FEATURES.copy()
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if "selected_features" not in st.session_state:
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st.session_state.selected_features = []
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if "cloud_positions" not in st.session_state:
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# Pre-generate random positions for each feature
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st.session_state.cloud_positions = {
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feature: (random.randint(0, 80), random.randint(0, 80))
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for feature in ALL_FEATURES
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}
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# --- LAYOUT ---
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col1, col2 = st.columns([1, 2])
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# TOP LEFT: Loan applicant icon
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with col1:
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st.image(
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"https://cdn-icons-png.flaticon.com/512/1048/1048949.png",
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caption="Loan Applicant",
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width=200
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)
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# TOP RIGHT: Feature cloud
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with col2:
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st.markdown("### Available Features")
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# Create a fixed-height area for the cloud
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cloud_html = "<div style='position: relative; height: 300px; border: 1px dashed #ccc;'>"
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for feature in st.session_state.available_features:
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top, left = st.session_state.cloud_positions[feature]
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# Button styled as text link in random position
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button_html = f"""
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<form action="" method="get" style="position: absolute; top: {top}%; left: {left}%; transform: translate(-50%, -50%);">
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<button name="feature_click" value="{feature}" style="background:none; border:none; color:blue; text-decoration:underline; cursor:pointer;">
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{feature}
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</button>
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</form>
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"""
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cloud_html += button_html
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cloud_html += "</div>"
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st.markdown(cloud_html, unsafe_allow_html=True)
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# --- CLICK HANDLING ---
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clicked_feature = st.query_params.get("feature_click")
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if clicked_feature and clicked_feature in st.session_state.available_features:
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st.session_state.selected_features.append(clicked_feature + " (approx)")
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st.session_state.available_features.remove(clicked_feature)
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st.session_state.show_message = (
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f"Sorry, '{clicked_feature}' cannot be precisely collected. Adding approximation instead."
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)
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# Clear the query param to avoid re-adding on refresh
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st.query_params.clear()
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st.experimental_rerun()
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# --- MESSAGE ---
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if "show_message" in st.session_state:
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st.markdown(
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f"<div style='text-align:center; color:red; font-weight:bold; margin:20px 0;'>{st.session_state.show_message}</div>",
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unsafe_allow_html=True
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)
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del st.session_state.show_message
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st.markdown("---")
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# --- BOTTOM HALF: Dataset table ---
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st.markdown("### Current Dataset")
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if st.session_state.selected_features:
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cols = st.session_state.selected_features + ["Target Label"]
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df = pd.DataFrame(columns=cols)
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st.dataframe(df, use_container_width=True)
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else:
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st.info("No features added yet. Click a feature above to add it to the dataset.")
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