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| import streamlit as st | |
| import pandas as pd | |
| import icc # Importing ICC computation functions | |
| # Set up Streamlit page layout (Full Width) | |
| st.set_page_config(layout="wide", page_title="ICC Computation App", page_icon="📊") | |
| # Title and instructions | |
| st.title("📊 ICC Computation App") | |
| st.markdown("Upload a **CSV file** containing LLM evaluation scores and compute **Intraclass Correlation Coefficients (ICC)**.") | |
| # **Two Side-by-Side Containers** | |
| container_left, container_right = st.columns([1, 2]) # Left (Filters) | Right (ICC Results + Heatmaps) | |
| # **LEFT: File Upload & Selection Filters** | |
| with container_left: | |
| st.header("📂 Upload & Selection") | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload Your CSV", type=["csv"]) | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file, delimiter=",", dtype=str) # Read as string first | |
| required_columns = ["assessor", "respondent"] | |
| criterion_columns = [col for col in df.columns if col.startswith("criterion")] | |
| if not all(col in df.columns for col in required_columns) or len(criterion_columns) < 1: | |
| st.error("❌ Invalid CSV format.") | |
| else: | |
| st.success("✅ CSV format is valid!") | |
| # Sidebar filters | |
| st.subheader("🔍 Select Filters") | |
| # Extract Unique Options | |
| all_assessors = sorted(df["assessor"].unique()) | |
| all_respondents = sorted(df["respondent"].unique()) | |
| all_criteria = criterion_columns | |
| # **Assessors Selection with 'Select All'** | |
| select_all_assessors = st.checkbox("Select All Assessors", value=True) | |
| selected_assessors = st.multiselect( | |
| "Select Assessors", all_assessors, default=all_assessors if select_all_assessors else [] | |
| ) | |
| # **Respondents Selection with 'Select All'** | |
| select_all_respondents = st.checkbox("Select All Respondents", value=True) | |
| selected_respondents = st.multiselect( | |
| "Select Respondents", all_respondents, default=all_respondents if select_all_respondents else [] | |
| ) | |
| # **Criteria Selection with 'Select All'** | |
| select_all_criteria = st.checkbox("Select All Criteria", value=True) | |
| selected_criteria = st.multiselect( | |
| "Select Criteria", all_criteria, default=all_criteria if select_all_criteria else [] | |
| ) | |
| def reset_filters(): | |
| """Resets selections to all available options.""" | |
| st.session_state["selected_assessors"] = all_assessors | |
| st.session_state["selected_respondents"] = all_respondents | |
| st.session_state["selected_criteria"] = all_criteria | |
| # Reset All Button | |
| if st.button("🔄 Reset All Selections"): | |
| reset_filters() | |
| # Filter data based on user selection | |
| df = icc.preprocess_data(df, selected_assessors, selected_respondents, selected_criteria) | |
| if df.empty: | |
| st.error("⚠️ No data available with selected filters.") | |
| # **RIGHT: Display ICC Results + Heatmaps** | |
| with container_right: | |
| st.header("📊 ICC Results & Heatmaps") | |
| if uploaded_file is not None and not df.empty: | |
| with st.spinner("⏳ Computing ICC... Please wait."): | |
| icc_results = icc.compute_icc(df) | |
| if icc_results is not None: | |
| st.subheader("📈 Overall ICC Results") | |
| st.dataframe(icc_results, use_container_width=True) # Display ICC table | |
| else: | |
| st.warning("⚠️ Not enough respondents to compute ICC.") | |
| # **HEATMAPS: Display Below in 3 Columns** | |
| st.subheader("🔥 ICC Heatmaps (Assessor Agreement)") | |
| heatmap_cols = st.columns(3) # 3-column layout for heatmaps | |
| # Compute assessor ICC | |
| icc_matrix_types = icc.compute_assessor_icc(df) | |
| # Generate heatmaps and display | |
| heatmap_files = icc.generate_heatmaps(icc_matrix_types) | |
| for i, (icc_type, heatmap_file) in enumerate(heatmap_files.items()): | |
| heatmap_cols[i].image(heatmap_file, caption=f"ICC Heatmap ({icc_type})", use_container_width=True) | |