Create app.py

app.py

@@ -0,0 +1,55 @@
+# MetaDiscovery Agent - Phase 1: LOC API Integration and Metadata Gap Analysis
+
+import requests
+import pandas as pd
+import streamlit as st
+import plotly.express as px
+
+# Streamlit app header
+st.title("MetaDiscovery Agent for Library of Congress Collections")
+st.markdown("""
+This tool connects to the LOC API, retrieves metadata from a selected collection, and performs
+an initial analysis of metadata completeness.
+""")
+
+# User selects a collection (predefined for prototype)
+collection_url = "https://www.loc.gov/collections/american-revolutionary-war-maps/?fo=json"
+st.sidebar.markdown("## Settings")
+st.sidebar.write("Collection: American Revolutionary War Maps")
+
+# Fetch data from LOC API
+response = requests.get(collection_url)
+data = response.json()
+
+# Parse metadata records
+records = data.get("results", [])
+
+# Extract selected metadata fields
+items = []
+for record in records:
+    items.append({
+        "title": record.get("title"),
+        "date": record.get("date"),
+        "subject": record.get("subject"),
+        "creator": record.get("creator"),
+        "description": record.get("description")
+    })
+
+# Create DataFrame
+metadata_df = pd.DataFrame(items)
+st.subheader("📦 Retrieved Metadata Sample")
+st.dataframe(metadata_df.head())
+
+# Metadata completeness analysis
+st.subheader("🧠 Metadata Completeness Analysis")
+completeness = metadata_df.notnull().mean() * 100
+completeness_df = pd.DataFrame({"Field": completeness.index, "Completeness (%)": completeness.values})
+
+# Plot completeness
+fig = px.bar(completeness_df, x="Field", y="Completeness (%)", title="Metadata Completeness by Field")
+st.plotly_chart(fig)
+
+# List records with missing values
+st.subheader("⚠️ Records with Incomplete Metadata")
+incomplete_records = metadata_df[metadata_df.isnull().any(axis=1)]
+st.dataframe(incomplete_records)