Create app.py

app.py

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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)
+