Added tabs to keep interface cleaner

app.py

@@ -5,6 +5,7 @@
 import streamlit as st
 import pandas as pd
 from io import BytesIO
+from streamlit_extras.metric_cards import style_metric_cards
 
 # ---- FUNCTIONS ----
 
@@ -46,105 +47,76 @@ if uploaded_file is not None:
     # Read raw bytes for caching and repeated use --> this ensure all the processing isn't repeated when a user changes the filters
     raw = uploaded_file.read()
 
-    with st.expander("Data Preview", icon='📊'):
-        st.markdown("Here's the data you uploaded")
-        df_orig = pd.read_csv(BytesIO(raw))
-        st.dataframe(df_orig)
-
     ## ---- PROCESSED DATA (CACHED) ----
 
     df, freeform_col = load_and_process(raw)
 
     ## ---- INTERACTIVE FILTERING & REVIEW INTERFACE ----
     
-    st.sidebar.title("Filters")
-    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)}")
-
-    ## ---- NECESSITY INDEX CHART ----
-
-    st.header("Processed Applications")
-    st.markdown("#### Necessity Index Distribution")
-
-    with st.expander("Learn about our indexing algorithm", icon='🌱'):
-        st.markdown(
-                """
-                This algorithm is designed to help us evaluate grant applications by assigning a "necessity score" to each application. Here's how it works:
-
-                #### Lexicon
-
-                **Keywords:** The algorithm looks for specific keywords in the application text. These keywords are grouped into five main categories:
-                - **Urgency:** Words like "urgent" and "immediate" that express pressing needs.
-                - **Severity:** Words like "severe" and "desperate" that indicate serious issues.
-                - **Vulnerability:** Words like "disability" "SEND", "underserved" that represent at-risk groups.
-                - **Emotional Appeal:** Words like "hope", "committed", "passionate", and "love" that communicate an emotional connection to the mission of each application.
-                - **Superlatives:** Words like "completely", "massive", and "significantly" that can serve as proxies for passionate writing.
-
-                #### Weights
-
-                Each category is **assigned a weight accroding to its importance** with **urgency and vulnerability markers** having the highest weights, followed by **severity markers**, and lastly, **superlatives and emotional appeal markers**.
-
-                #### Scoring Process
-
-                - **Need Identification:** The algorithm scans through the application text to count
-                how many times each keyword appears.
-                - **Weighted Scoring:** It multiplies these counts by the corresponding category weight to compute a total "necessity index", and then normalises the values to produce a range between 0 and 1.
-
-                #### What It Means
-
-                Together, all these scores help can help us rank the applications based on how critical the prize is to each school, with a **higher necessity index suggesting a more compelling case** and a lower score suggesting a "want" rather than a "need".
-                """
+    tab1, tab2 = st.tabs(["Shortlist Manager","Insights"])
+
+    with tab1:
+        st.sidebar.title("Filters")
+        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)}")
+
+        ## ---- NECESSITY INDEX CHART ----
+
+        # Review applications
+        st.subheader("Filtered Applications")
+        st.markdown("To filter applications, use the app's side panel on the left-hand side.")
+        for idx, row in filtered_df.iterrows():
+            with st.expander(f"Application \#{idx}"):
+                st.write("")
+                col1, col2, col3, col4 = st.columns(4)
+                col1.metric("Necessity", f"{row['necessity_index']:.1f}")
+                col2.metric("Urgency", f"{int(row['urgency_score'])}")
+                col3.metric("Severity", f"{int(row['severity_score'])}")
+                col4.metric("Vulnerability", f"{int(row['vulnerability_score'])}")
+                style_metric_cards(box_shadow=False, border_left_color='#E7F4FF',background_color='#E7F4FF', border_size_px=0, border_radius_px=6)
+                # Clean usage items
+                usage_items = [item for item in row['Usage'] if item and item.lower() != 'none']
+                st.markdown("##### Excerpt")
+                st.write(row[freeform_col])
+                if usage_items:
+                    st.markdown("##### Usage")
+                    # Display usage items as colored pills
+                    pills_html = "".join(
+                            f"<span style='display:inline-block;background-color:#E7F4FF;color:#125E9E;border-radius:20px;padding:4px 10px;margin:2px;font-size:0.95rem;'>{item}</span>"
+                        for item in usage_items
+                    )
+                    st.markdown(pills_html, unsafe_allow_html=True)
+                else:
+                    st.caption("*No usage found*")
+                st.write("")
+                # Shortlist checkbox
+                st.checkbox(
+                    "Add to shortlist",
+                    key=f"shortlist_{idx}"
                 )
 
-    st.bar_chart(df['necessity_index'])
-
-    # Review applications
-    st.subheader("Filtered Applications")
-    st.markdown("To filter applications, use the app's side panel on the left-hand side.")
-    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']
-            col2.markdown("**EXCERPT**")
-            col2.write(row[freeform_col])
-            if usage_items:
-                col2.markdown("**EXTRACTED USAGE ITEMS:**")
-                # Display usage items as colored pills
-                pills_html = "".join(
-                    f"<span style='display:inline-block;background-color:#D7E0FB;color:#3C63C9;border-radius:20px;padding:4px 10px;margin:2px;'>{item}</span>"
-                    for item in usage_items
-                )
-                col2.markdown(pills_html, unsafe_allow_html=True)
-            else:
-                col2.markdown("*No specific usage items extracted.*")
-            # 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"
             )
 
-    # 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"
-        )
-
+    with tab2:
+        st.subheader("Necessity Index Distribution")
+        st.write("")
+        st.bar_chart(df['necessity_index'], color='#81bc4d')
+        st.dataframe(df, hide_index=True)