Create app.py

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import pandas as pd
+
+from streamlit_utils import (
+    get_query,
+    render_query,
+    render_suggestions,
+    search_movie,
+    summarized_plot,
+)
+
+st.title("Search Wikipedia Movie Plots")
+
+
+st.sidebar.info(
+    "Search Wikipedia movie plots and summarize the results. Type a query to start or pick one of these suggestions:"
+)
+user_query = get_query()
+
+container = st.container()
+container.write("Some of the searches you can do: ")
+render_suggestions()
+render_query()
+MAX_ITEMS = st.number_input("Number of results", min_value=1, max_value=10, step=1)
+
+
+if not user_query:
+    st.stop()
+
+container = st.container()
+header = container.empty()
+header.write(f"Looking for results for:  _{user_query}_")
+
+
+if MAX_ITEMS:
+    with st.spinner("AI doing it's magic"):
+        sample_df = search_movie(user_query, limit=MAX_ITEMS)
+        peft_model_text_output_list = summarized_plot(sample_df, limit=MAX_ITEMS)
+        for i in range(MAX_ITEMS):
+            # placeholders[i].info(label=sample_df.iloc[i]["title"], expanded=False)
+            with st.sidebar.expander(
+                label=f'See the complete plot for: _{sample_df.iloc[i]["title"]}_',
+                expanded=False,
+            ):
+                sample_df.iloc[i]["plot"]
+            with st.expander(
+                label=f'See the summarized plot for: _{sample_df.iloc[i]["title"]}_'
+            ):
+                peft_model_text_output_list[i]
+
+header.write(f"That's what I found about: _{user_query}_ . ")
+header.write(f"**Summarizing results...**")
+
+
+header.success("Search finished. Try something else!")
+st.balloons()