import streamlit as st
import pandas as pd
import time
from scholarly import scholarly
import plotly.express as px

# Set layout to wide
st.set_page_config(
    page_title="Automatic Faculty Research & Publication Inspector",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Known Scopus publishers or identifiers
SCOPUS_PUBLISHERS = ["Elsevier", "Springer", "IEEE", "Wiley", "Taylor & Francis", "SAGE"]

# CSS for dark theme styling
dark_theme_css = """
<style>
body {
    background-color: #121212;
    color: #E0E0E0;
}
h1, h2, h3, h4, h5, h6 {
    color: #BB86FC;
}
.stApp {
    background-color: #121212;
    color: #E0E0E0;
}
.stMetric {
    background-color: #1F1B24;
    border: 1px solid #BB86FC;
    border-radius: 8px;
    padding: 10px;
    color: #E0E0E0;
    box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.5);
}
.stButton > button {
    background-color: #BB86FC;
    color: #121212;
    border: none;
    border-radius: 8px;
    padding: 10px 20px;
    cursor: pointer;
    transition: transform 0.2s ease;
}
.stButton > button:hover {
    transform: scale(1.1);
    background-color: #3700B3;
}
.stDataFrame {
    background-color: #1F1B24;
    border: 1px solid #BB86FC;
    border-radius: 8px;
    color: #E0E0E0;
}
.stMarkdown {
    color: #E0E0E0;
}
</style>
"""

# Apply the CSS
st.markdown(dark_theme_css, unsafe_allow_html=True)

# Function to classify publications
def classify_publications(publications):
    scopus_count, non_scopus_count, ieee_count, springer_count, patent_count = 0, 0, 0, 0, 0
    yearly_publications = {year: 0 for year in range(2019, 2025)}  # Years 2019–2024

    for pub in publications:
        try:
            pub_details = scholarly.fill(pub)
            time.sleep(1)
            source = pub_details.get("bib", {}).get("source", "").lower()
            publisher = pub_details.get("bib", {}).get("publisher", "").lower()
            title = pub_details.get("bib", {}).get("title", "").lower()
            pub_year = pub_details.get("bib", {}).get("pub_year", None)

            if pub_year in yearly_publications:
                yearly_publications[pub_year] += 1

            if "ieee" in source or "ieee" in publisher:
                ieee_count += 1

            if "springer" in source or "springer" in publisher:
                springer_count += 1

            if any(publisher_keyword.lower() in publisher for publisher_keyword in SCOPUS_PUBLISHERS):
                scopus_count += 1
            elif "journal" in source or "conference" in source:
                scopus_count += 1
            else:
                non_scopus_count += 1

            if "patent" in title:
                patent_count += 1
        except Exception as e:
            st.warning(f"Skipping publication due to error: {e}")

    return scopus_count, non_scopus_count, ieee_count, springer_count, patent_count, yearly_publications

# Function to extract scholar data
def extract_scholar_data_by_name(scholar_name):
    try:
        search_results = scholarly.search_author(scholar_name)
        scholar_data = None

        for result in search_results:
            scholar_data = scholarly.fill(result)
            time.sleep(1)
            break

        if not scholar_data:
            return None

        metrics = scholar_data.get("cites_per_year", {})
        area_of_interest = scholar_data.get("interests", [])
        h_index = scholar_data.get("hindex", 0)
        i10_index = scholar_data.get("i10index", 0)

        scopus_pubs, non_scopus_pubs, ieee_pubs, springer_pubs, patent_count, yearly_publications = classify_publications(
            scholar_data.get("publications", [])
        )

        return {
            "Scholar Name": scholar_data.get("name", "N/A"),
            "Citations": sum(metrics.values()),
            "h-index": h_index,
            "i10-index": i10_index,
            "Citation Impact": round(sum(metrics.values()) / len(metrics), 2) if metrics else 0,
            "Publications": yearly_publications,
            "Metrics": metrics,
            "Area of Interest": area_of_interest,
            "Scopus Publications": scopus_pubs,
            "Non-Scopus Publications": non_scopus_pubs,
            "IEEE Publications": ieee_pubs,
            "Springer Publications": springer_pubs,
            "Patents": patent_count,
            "Publications Data": scholar_data.get("publications", []),
        }
    except Exception as e:
        st.error(f"Error occurred: {e}")
        return None

# Streamlit App
st.title("🔍 **Automatic Faculty Research & Publication Inspector**")
st.markdown("---")

# Input field for faculty name
st.subheader("**Input Faculty Name**")
scholar_name = st.text_input("Enter Faculty Name:")

if st.button("Fetch Data"):
    if scholar_name:
        st.info("Fetching data... Please wait.")
        scholar_data = extract_scholar_data_by_name(scholar_name)

        if scholar_data:
            # Display Key Metrics
            st.markdown("### **📊 Key Metrics**")
            col1, col2, col3, col4 = st.columns(4)
            col1.metric("📌 Total Citations", scholar_data["Citations"])
            col2.metric("📌 h-index", scholar_data["h-index"])
            col3.metric("📌 i10-index", scholar_data["i10-index"])
            col4.metric("📌 Citation Impact", scholar_data["Citation Impact"])

            # Display Trends
            st.markdown("### **📈 Trends**")
            trend_col1, trend_col2 = st.columns(2)
            with trend_col1:
                st.markdown("**Citations Over the Years**")
                citation_trend = pd.DataFrame(scholar_data["Metrics"].items(), columns=["Year", "Citations"])
                st.bar_chart(citation_trend.set_index("Year"))
            with trend_col2:
                st.markdown("**Publications Over the Years**")
                publication_trend = pd.DataFrame(scholar_data["Publications"].items(), columns=["Year", "Publications"])
                st.bar_chart(publication_trend.set_index("Year"))

            # Scholar Detailed Metrics
            st.markdown("### **📋 Scholar Metrics**")
            scholar_details_df = pd.DataFrame([{
                "Scholar Name": scholar_data["Scholar Name"],
                "Total Citations": scholar_data["Citations"],
                "h-index": scholar_data["h-index"],
                "i10-index": scholar_data["i10-index"],
                "Citation Impact": scholar_data["Citation Impact"],
                "Scopus Publications": scholar_data["Scopus Publications"],
                "Non-Scopus Publications": scholar_data["Non-Scopus Publications"],
                "IEEE Publications": scholar_data["IEEE Publications"],
                "Springer Publications": scholar_data["Springer Publications"],
                "Patents": scholar_data["Patents"],
            }])
            st.dataframe(scholar_details_df)

            # Area of Interest & Pie Charts
            st.markdown("### **📌 Insights**")
            left, middle, right = st.columns([1, 1.5, 1.5])

            with left:
                st.markdown("**Areas of Interest**")
                if scholar_data["Area of Interest"]:
                    for interest in scholar_data["Area of Interest"]:
                        st.button(interest)
                else:
                    st.info("No areas of interest available.")

            with middle:
                st.markdown("**Scopus vs Non-Scopus Publications**")
                scopus_data = {
                    "Type": ["Scopus", "Non-Scopus"],
                    "Count": [scholar_data["Scopus Publications"], scholar_data["Non-Scopus Publications"]],
                }
                fig1 = px.pie(scopus_data, names="Type", values="Count", title="Scopus vs Non-Scopus")
                st.plotly_chart(fig1)

            with right:
                st.markdown("**IEEE vs Springer Publications**")
                ieee_data = {
                    "Type": ["IEEE", "Springer"],
                    "Count": [scholar_data["IEEE Publications"], scholar_data["Springer Publications"]],
                }
                fig2 = px.pie(ieee_data, names="Type", values="Count", title="IEEE vs Springer")
                st.plotly_chart(fig2)

            # Display Publications Table
            st.markdown("### **📚 Publications**")
            pub_data = []
            for pub in scholar_data["Publications Data"][:5]:
                pub_details = scholarly.fill(pub)
                pub_data.append({
                    "Publication": pub_details.get("bib", {}).get("title", "N/A"),
                    "Citations": pub_details.get("num_citations", 0),
                    "Year": pub_details.get("bib", {}).get("pub_year", "N/A"),
                })
                time.sleep(1)

            if pub_data:
                pub_df = pd.DataFrame(pub_data)
                st.dataframe(pub_df)

                st.download_button(
                    label="Download Publications Data as CSV",
                    data=pub_df.to_csv(index=False),
                    file_name="publications.csv",
                    mime="text/csv",
                )
        else:
            st.warning("No data found for the provided faculty name.")


    else:
        st.error("Please enter a valid faculty name.")