app.py

import gradio as gr
import pandas as pd
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import networkx as nx
import matplotlib.pyplot as plt
import csv
import io
import matplotlib.font_manager as fm
from neo4j import GraphDatabase

# 한국어 처리를 위한 KoSentence-BERT 모델 로드
model = SentenceTransformer('jhgan/ko-sbert-sts')

# 나눔바른고딕 폰트 설정
font_path = "NanumBarunGothic.ttf"  # Hugging Face 루트에 저장된 폰트 경로
fontprop = fm.FontProperties(fname=font_path)
plt.rc('font', family=fontprop.get_name())

# Neo4j 데이터베이스 연결 클래스
class Neo4jConnection:
    def __init__(self, uri, user, pwd):
        self.driver = GraphDatabase.driver(uri, auth=(user, pwd))

    def close(self):
        self.driver.close()

    def query(self, query, parameters=None, db=None):
        session = None
        response = None
        try:
            session = self.driver.session(database=db) if db else self.driver.session() 
            response = list(session.run(query, parameters))
        except Exception as e:
            print("Query failed:", e)
        finally:
            if session:
                session.close()
        return response

# Neo4j 연결 설정
conn = Neo4jConnection(uri="bolt://localhost:7687", user="neo4j", pwd="your_password")

# 추천 결과를 실제 파일로 저장하는 함수
def save_recommendations_to_file(recommendations):
    file_path = "recommendations.csv"
    with open(file_path, mode='w', newline='', encoding='utf-8') as file:
        writer = csv.writer(file)
        writer.writerow(["Employee ID", "Employee Name", "Recommended Programs"])
        
        # 추천 결과 CSV 파일에 기록
        for rec in recommendations:
            writer.writerow(rec)
    
    return file_path

# 자동으로 열을 매칭하는 함수
def auto_match_columns(df, required_cols):
    matched_cols = {}
    for req_col in required_cols:
        matched_col = None
        for col in df.columns:
            if req_col in col.lower():
                matched_col = col
                break
        matched_cols[req_col] = matched_col
    return matched_cols

# 직원 및 프로그램 데이터의 열을 자동으로 매칭하거나, 선택하게 하는 함수
def validate_and_get_columns(employee_df, program_df):
    required_employee_cols = ["employee_id", "employee_name", "current_skills"]
    required_program_cols = ["program_name", "skills_acquired", "duration"]

    employee_cols = auto_match_columns(employee_df, required_employee_cols)
    program_cols = auto_match_columns(program_df, required_program_cols)
    
    for key, value in employee_cols.items():
        if value is None:
            return f"직원 데이터에서 '{key}' 열을 선택할 수 없습니다. 올바른 열을 선택하세요.", None, None
    
    for key, value in program_cols.items():
        if value is None:
            return f"프로그램 데이터에서 '{key}' 열을 선택할 수 없습니다. 올바른 열을 선택하세요.", None, None

    return None, employee_cols, program_cols

# 직원 데이터를 분석하여 교육 프로그램을 추천하고, 테이블과 그래프를 생성하는 함수
def hybrid_rag(employee_file, program_file):
    # 1. VectorRAG: KoSentence-BERT를 이용한 유사도 계산
    employee_df = pd.read_csv(employee_file.name)
    program_df = pd.read_csv(program_file.name)

    error_msg, employee_cols, program_cols = validate_and_get_columns(employee_df, program_df)
    if error_msg:
        return error_msg, None, None, None

    employee_skills = employee_df[employee_cols["current_skills"]].tolist()
    program_skills = program_df[program_cols["skills_acquired"]].tolist()
    employee_embeddings = model.encode(employee_skills)
    program_embeddings = model.encode(program_skills)

    similarities = cosine_similarity(employee_embeddings, program_embeddings)

    recommendations = []
    recommendation_rows = []  # 테이블 및 CSV로 저장할 데이터
    for i, employee in employee_df.iterrows():
        recommended_programs = []
        for j, program in program_df.iterrows():
            if similarities[i][j] > 0.5:
                recommended_programs.append(f"{program[program_cols['program_name']]} ({program[program_cols['duration']]})")
        
        if recommended_programs:
            recommendation = f"직원 {employee[employee_cols['employee_name']]}의 추천 프로그램: {', '.join(recommended_programs)}"
            recommendation_rows.append([employee[employee_cols['employee_id']], employee[employee_cols['employee_name']], ", ".join(recommended_programs)])
        else:
            recommendation = f"직원 {employee[employee_cols['employee_name']]}에게 적합한 프로그램이 없습니다."
            recommendation_rows.append([employee[employee_cols['employee_id']], employee[employee_cols['employee_name']], "적합한 프로그램 없음"])

        recommendations.append(recommendation)

    # 2. GraphRAG: Neo4j에서 프로그램 추천을 가져옴
    query = """
    MATCH (e:Employee)-[:HAS_SKILL]->(p:Program)
    RETURN e.name AS employee_name, p.name AS program_name, p.duration AS duration
    """
    graph_rag_results = conn.query(query)

    # GraphRAG 결과 추가
    for record in graph_rag_results:
        for row in recommendation_rows:
            if record['employee_name'] == row[1]:
                row[2] += f", {record['program_name']} (GraphRAG)"

    G = nx.Graph()
    for employee in employee_df[employee_cols['employee_name']]:
        G.add_node(employee, type='employee')

    for program in program_df[program_cols['program_name']]:
        G.add_node(program, type='program')

    for i, employee in employee_df.iterrows():
        for j, program in program_df.iterrows():
            if similarities[i][j] > 0.5:
                G.add_edge(employee[employee_cols['employee_name']], program[program_cols['program_name']])

    plt.figure(figsize=(10, 8))
    pos = nx.spring_layout(G)
    nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=3000, font_size=10, font_weight='bold', edge_color='gray', fontproperties=fontprop)
    plt.title("직원과 프로그램 간의 관계", fontsize=14, fontweight='bold', fontproperties=fontprop)
    plt.tight_layout()

    # CSV 파일로 추천 결과 반환
    csv_output = save_recommendations_to_file(recommendation_rows)
    
    # 결과 테이블 데이터프레임 생성
    result_df = pd.DataFrame(recommendation_rows, columns=["Employee ID", "Employee Name", "Recommended Programs"])
    
    return result_df, plt.gcf(), csv_output

# Gradio 블록
with gr.Blocks(css=".gradio-button {background-color: #007bff; color: white;} .gradio-textbox {border-color: #6c757d;}") as demo:
    gr.Markdown("<h1 style='text-align: center; color: #2c3e50;'>💼 HybridRAG 시스템</h1>")
    
    with gr.Row():
        with gr.Column(scale=1, min_width=300):
            gr.Markdown("<h3 style='color: #34495e;'>1. 직원 및 프로그램 데이터를 업로드하세요</h3>")
            employee_file = gr.File(label="직원 데이터 업로드", interactive=True)
            program_file = gr.File(label="교육 프로그램 데이터 업로드", interactive=True)
            analyze_button = gr.Button("분석 시작", elem_classes="gradio-button")
            output_table = gr.DataFrame(label="분석 결과 (테이블)")
            csv_download = gr.File(label="추천 결과 다운로드")

        with gr.Column(scale=2, min_width=500):
            gr.Markdown("<h3 style='color: #34495e;'>2. 분석 결과 및 시각화</h3>")
            chart_output = gr.Plot(label="시각화 차트")

    # 분석 버튼 클릭 시 테이블, 차트, 파일 다운로드를 업데이트
    analyze_button.click(hybrid_rag, inputs=[employee_file, program_file], outputs=[output_table, chart_output, csv_download])

# Gradio 인터페이스 실행
demo.launch()