app refactor

app.py

@@ -2,30 +2,48 @@ import pandas as pd
 import streamlit as st
 import math
 
-models_df = pd.read_csv("models_with_readmes.csv").dropna(subset=["readme"])
 
-st.title("Huggingface model explorer")
-st.text(f"search {len(models_df)} models by name or readme")
-st.text("note that there are many more models but here we only show those with readme")
+def setup_page():
+    st.title("Huggingface model explorer")
+    st.text(f"search {len(models_df)} models by name or readme")
+    st.text(
+        "note that there are many more models but here we only show those with readme"
+    )
+
+
+def setup_inputs():
+    col1, col2, col3 = st.columns(3)
+    query = col1.text_input("model name query", value="")
+    readme_query = col2.text_input("readme query", value="")
+    return query, readme_query, col3 
+
+
+def get_selected_models_df(query, readme_query):
+    return models_df[
+        models_df["readme"].str.lower().str.contains(readme_query)
+        & models_df["modelId"].str.lower().str.contains(query)
+    ]
 
-col1, col2, col3 = st.columns(3)
-query = col1.text_input("model name query", value="")
-readme_query = col2.text_input("readme query", value="")
 
-selected_models_df = models_df[
-    models_df["readme"].str.contains(readme_query)
-    & models_df["modelId"].str.contains(query)
-]
+def show_paged_selected_model_info(models_df, page_col):
+    page = page_col.number_input("page", 0, math.ceil(len(selected_models_df) / 10))
+    selected_models_df_subset = models_df.iloc[
+        page * n_per_page : (page+ 1) * n_per_page
+    ]
+    st.write(f"found {len(selected_models_df)} models")
+    for (model_name, tag, readme) in selected_models_df_subset[
+        ["modelId", "pipeline_tag", "readme"]
+    ].itertuples(index=False):
+        with st.expander(f"{model_name} ({tag})"):
+            st.write(readme)
+
+
+models_df = pd.read_csv("models_with_readmes.csv").dropna(subset=["readme"])
+
+setup_page()
+query, readme_query, page_col = setup_inputs()
+selected_models_df = get_selected_models_df(query, readme_query)
 
 n_per_page = 10
-slider = col3.number_input("page", 0, math.ceil(len(selected_models_df) / 10))
-selected_models_df_subset = selected_models_df.iloc[
-    slider * n_per_page : (slider + 1) * n_per_page
-]
-st.write(f"matched {len(selected_models_df)} models")
-
-for i, (model_name, tag, readme) in enumerate(
-    selected_models_df_subset[["modelId", "pipeline_tag", "readme"]].itertuples(index=False)
-):
-    with st.expander(f"{model_name} ({tag})"):
-        st.write(readme)
+
+show_paged_selected_model_info(selected_models_df, page_col)