import streamlit as st
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity

st.title("GNN Recommender Agent")

uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])

if uploaded_file:
    df = pd.read_csv(uploaded_file)
    st.write("Dataset:")
    st.dataframe(df)

    matrix = df.iloc[:,1:].values
    sim = cosine_similarity(matrix)

    st.subheader("Cosine Similarity")
    st.write(sim)

    users = df.iloc[:,0].tolist()

    G = nx.Graph()

    for u in users:
        G.add_node(u, bipartite=0)

    for i in range(matrix.shape[1]):
        G.add_node(f"I{i+1}", bipartite=1)

    for r in range(len(users)):
        for c in range(matrix.shape[1]):
            if matrix[r][c] == 1:
                G.add_edge(users[r], f"I{c+1}")

    fig, ax = plt.subplots()
    pos = nx.spring_layout(G)
    nx.draw(G, pos, with_labels=True, node_size=1500, ax=ax)

    st.pyplot(fig)