Interface changes by codex

app.py

@@ -4,12 +4,33 @@
 
 import streamlit as st
 import pandas as pd
+from io import BytesIO
 
 # -- FUNCTIONS --
 
 from functions.extract_usage import extract_usage
 from functions.necessity_index import compute_necessity 
 from functions.column_detection import detect_freeform_answer_col
+import typing
+
+# -- CACHEABLE PROCESSING --
+@st.cache_data
+def load_and_process(raw_csv: bytes) -> typing.Tuple[pd.DataFrame, str]:
+    """
+    Load CSV from raw bytes, detect freeform column, compute necessity scores,
+    and extract usage items. Returns processed DataFrame and freeform column name.
+    """
+    # Read original data
+    df_orig = pd.read_csv(BytesIO(raw_csv))
+    # Detect narrative column
+    freeform_col = detect_freeform_answer_col(df_orig)
+    # Compute necessity scores
+    scored = df_orig.join(df_orig[freeform_col].apply(compute_necessity))
+    # Extract usage via AI
+    docs = df_orig[freeform_col].to_list()
+    usage = extract_usage(docs)
+    scored['Usage'] = usage
+    return scored, freeform_col
 
 ################################
 # APP SCRIPT
@@ -20,34 +41,72 @@ st.title("Grant Applications Helper")
 uploaded_file = st.file_uploader("Upload grant applications file for analysis", type='csv')
 
 if uploaded_file is not None:
-    df = pd.read_csv(uploaded_file)
+    # Read raw bytes for caching and repeated use
+    raw = uploaded_file.read()
 
+    # --- Original Data Preview ---
     st.markdown("""
     ### Data Preview
     Here's the data you uploaded!
     """
-                )
-
-    st.dataframe(df)
-
-    ## ------- 0. PREPROCESSING ----------
-
-    freeform_col = detect_freeform_answer_col(df) # <- detects the long-form column used for processing
-
-    docs = df[freeform_col].to_list()
-
-    
-    ## -------- 1. ASSIGN NECESSITY INDEX ------------
-
-    df = df.join(df[freeform_col].apply(compute_necessity))
-
-
-    ## --------- 2. EXTRACT USAGE -------------
-
-    with st.spinner("Extracting usage with AI...", show_time=True):
-        extracted_usage = extract_usage(docs)
-
-    df['Usage'] = extracted_usage
-
-    st.dataframe(df)
+    )
+    df_orig = pd.read_csv(BytesIO(raw))
+    st.dataframe(df_orig)
+
+    # --- Processed Data (cached): add scores & extracted usage ---
+    df, freeform_col = load_and_process(raw)
+
+    # -- Interactive Filtering & Review Interface --
+    st.sidebar.header("Filters")
+    # Filter by necessity index
+    min_idx = float(df['necessity_index'].min())
+    max_idx = float(df['necessity_index'].max())
+    filter_range = st.sidebar.slider(
+        "Necessity Index Range", min_value=min_idx, max_value=max_idx, value=(min_idx, max_idx)
+    )
+    filtered_df = df[df['necessity_index'].between(filter_range[0], filter_range[1])]
+
+    # Sidebar summary
+    st.sidebar.markdown(f"**Total Applications:** {len(df)}")
+    st.sidebar.markdown(f"**Filtered Applications:** {len(filtered_df)}")
+
+    # Distribution chart
+    st.subheader("Necessity Index Distribution")
+    st.bar_chart(df['necessity_index'])
+
+    # Review applications
+    st.subheader("Applications")
+    for idx, row in filtered_df.iterrows():
+        with st.expander(f"Application {idx} | Necessity: {row['necessity_index']:.1f}"):
+            col1, col2 = st.columns((1, 3))
+            col1.metric("Necessity", f"{row['necessity_index']:.1f}")
+            col1.metric("Urgency", f"{row['urgency_score']}")
+            col1.metric("Severity", f"{row['severity_score']}")
+            col1.metric("Vulnerability", f"{row['vulnerability_score']}")
+            # Clean usage items
+            usage_items = [item for item in row['Usage'] if item and item.lower() != 'none']
+            if usage_items:
+                col2.markdown("**Extracted Usage Items:**")
+                col2.write(", ".join(usage_items))
+            else:
+                col2.markdown("*No specific usage items extracted.*")
+            col2.markdown("**Excerpt:**")
+            col2.write(row[freeform_col])
+            # Shortlist checkbox
+            st.checkbox(
+                "Shortlist this application",
+                key=f"shortlist_{idx}"
+            )
+
+    # Shortlist summary and download
+    shortlisted = [
+        i for i in filtered_df.index
+        if st.session_state.get(f"shortlist_{i}", False)
+    ]
+    st.sidebar.markdown(f"**Shortlisted:** {len(shortlisted)}")
+    if shortlisted:
+        csv = df.loc[shortlisted].to_csv(index=False).encode('utf-8')
+        st.sidebar.download_button(
+            "Download Shortlist", csv, "shortlist.csv", "text/csv"
+        )