app.py

# MetaDiscovery Agent - LOC API with Enhanced Completeness and Quality Analysis
import requests
import pandas as pd
import numpy as np
import streamlit as st
import plotly.express as px
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Custom CSS for white background, styled sidebar, banner, and dark grey font
st.markdown("""
    <style>
          
        .main {
            background-color: #D3D3D3 !important;
            color: #1A1A1A!important;

        }
        .block-container {
            background-color: gray !important;
            color: #808080!important;
        }
        section[data-testid="stSidebar"] > div:first-child {
            background-color: #808080 !important;
            padding: 1rem;
            border-radius: 0.5rem;
            color: #808080 !important;
        }
        .stMarkdown, .stTextInput, .stDataFrame {
            color: #1A1A1A!important;
        }
        img.banner {
            width: 100%;
            border-radius: 12px;
            margin-bottom: 1rem;
        }
         .stAlert {
            background-color: #f0f0f5 !important;
            color: #333333 !important;
            padding: 1.25rem !important;
            font-size: 1rem !important;
            border-radius: 0.5rem !important;
            box-shadow: 0 2px 5px rgba(0, 0, 0, 0.05) !important;
        }
        header[data-testid="stHeader"] {
    background-color: gray !important;
}
        section[data-testid="stSidebar"] > div:first-child {
    background-color: #1A1A1A !important;
    color: #D3D3D3 !important;
    padding: 2rem 1.5rem 1.5rem 1.5rem !important;
    border-radius: 12px;
    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.08);
    font-size: 0.95rem;
    line-height: 1.5;
}
            .block-container {
    background-color: gray !important;
    color: #1A1A1A !important;
    padding-left: 2rem !important;
    padding-right: 2rem !important;
    box-shadow: none !important;

}
</style>
""", unsafe_allow_html=True)

# Optional: Add a banner image (replace with your image URL)
st.markdown('<img src="https://cdn-uploads.huggingface.co/production/uploads/67351c643fe51cb1aa28f2e5/7ThcAOjbuM8ajrP85bGs4.jpeg" class="banner">', unsafe_allow_html=True)

# Streamlit app header
st.title("LOC MetaDiscovery Agent")
st.markdown("""
This tool connects to the LOC API, retrieves metadata from a selected collection, and performs
an analysis of metadata completeness, suggests enhancements, and identifies authority gaps.
""")

# Updated collection URLs using the correct LOC API format
collections = {
    "American Revolutionary War Maps": "american+revolutionary+war+maps",
    "Civil War Maps": "civil+war+maps",
    "Women's Suffrage": "women+suffrage",
    "World War I Posters": "world+war+posters"
}

# Sidebar for selecting collection
st.sidebar.markdown("## Settings")
selected = st.sidebar.selectbox("Select a collection", list(collections.keys()))
search_query = collections[selected]

# Use the main search endpoint (most reliable)
collection_url = f"https://www.loc.gov/search/?q={search_query}&fo=json"
st.sidebar.write(f"Selected Collection: {selected}")
st.sidebar.markdown(f"<span style='color: lightgray;'>API URL: {collection_url}</span>", unsafe_allow_html=True)


# Fetch data from LOC API with spoofed User-Agent header
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 Chrome/110.0.0.0 Safari/537.36"
}

try:
    response = requests.get(collection_url, headers=headers)
    response.raise_for_status()
    data = response.json()

    if "results" in data:
        records = data.get("results", [])
    elif "items" in data:
        records = data.get("items", [])
    else:
        records = []
        st.error("Unexpected API response structure. No records found.")
    st.write(f"Retrieved {len(records)} records")

except requests.exceptions.RequestException as e:
    st.error(f"API Connection Error: {e}")
    records = []
except ValueError:
    st.error("Failed to parse API response as JSON")
    records = []

# Extract selected metadata fields
items = []
for record in records:
    if isinstance(record, dict):
        description = record.get("description", "")
        if isinstance(description, list):
            description = " ".join([str(d) for d in description])
        item = {
            "id": record.get("id", ""),
            "title": record.get("title", ""),
            "date": record.get("date", ""),
            "subject": ", ".join(record.get("subject", [])) if isinstance(record.get("subject"), list) else record.get("subject", ""),
            "creator": record.get("creator", ""),
            "description": description
        }
        if not item["title"] and "item" in record:
            item["title"] = record.get("item", {}).get("title", "")
        if not item["date"] and "item" in record:
            item["date"] = record.get("item", {}).get("date", "")
        items.append(item)

metadata_df = pd.DataFrame(items)

# Utility functions for deeper metadata quality analysis
def is_incomplete(value):
    return pd.isna(value) or value in ["", "N/A", "null", None]

def is_valid_date(value):
    try:
        pd.to_datetime(value)
        return True
    except:
        return False

if not metadata_df.empty:
    st.subheader("Retrieved Metadata Sample")
    st.dataframe(metadata_df.head())

    # Metadata completeness analysis (enhanced)
    st.subheader("Metadata Completeness Analysis")
    completeness = metadata_df.map(lambda x: not is_incomplete(x)).mean() * 100
    completeness_df = pd.DataFrame({"Field": completeness.index, "Completeness (%)": completeness.values})
    fig = px.bar(completeness_df, x="Field", y="Completeness (%)", title="Metadata Completeness by Field")
    st.plotly_chart(fig)

    # Identify incomplete records
    incomplete_mask = metadata_df.map(is_incomplete).any(axis=1)
    incomplete_records = metadata_df[incomplete_mask]

    st.subheader("Records with Incomplete Metadata")
    if not incomplete_records.empty:
        st.dataframe(incomplete_records.astype(str))
    else:
        st.success("All metadata fields are complete in this collection!")

    st.subheader("Identifiers of Items Needing Metadata Updates")
    if not incomplete_records.empty:
        st.write(incomplete_records[['id', 'title']])
    else:
        st.success("All records are complete!")

    st.subheader("Suggested Metadata Enhancements")
    filled_descriptions = metadata_df[metadata_df['description'].notnull()]['description'].astype(str)
    if len(filled_descriptions) > 1:
        try:
            tfidf = TfidfVectorizer(stop_words='english')
            tfidf_matrix = tfidf.fit_transform(filled_descriptions)
            suggestions = []
            for idx, row in incomplete_records.iterrows():
                if pd.isna(row['subject']) and pd.notna(row['description']):
                    desc_vec = tfidf.transform([str(row['description'])])
                    sims = cosine_similarity(desc_vec, tfidf_matrix).flatten()
                    top_idx = sims.argmax()
                    suggested_subject = metadata_df.iloc[top_idx]['subject']
                    if pd.notna(suggested_subject) and suggested_subject:
                        suggestions.append((row['title'], suggested_subject))
            if suggestions:
                suggestions_df = pd.DataFrame(suggestions, columns=["Title", "Suggested Subject"])
                st.dataframe(suggestions_df)
            else:
                st.info("No metadata enhancement suggestions available.")
        except Exception as e:
            st.error(f"Error generating metadata suggestions: {e}")
    else:
        st.info("Not enough descriptive data to generate metadata suggestions.")
else:
    st.warning("No metadata records found for this collection. Try selecting another one.")