streamlit_app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForTokenClassification
from sentence_transformers import SentenceTransformer
from youtube_transcript_api import YouTubeTranscriptApi
from googleapiclient.discovery import build
from torch.nn.functional import cosine_similarity
import torch

# Hugging Face 모델
MODEL_REPO = "zhixiusue/EduTubeNavigator"

# YouTube API Key
YOUTUBE_API_KEY = "AIzaSyA8SG7--MfQvWET6UOam0PVAcC5MDm4sbc"
youtube = build("youtube", "v3", developerKey=YOUTUBE_API_KEY)

# ID → 라벨 매핑
token_label_map = {
    0: 'O', 1: 'B-TOPIC', 2: 'I-TOPIC', 3: 'B-STYLE', 4: 'I-STYLE',
    5: 'B-LENGTH', 6: 'I-LENGTH', 7: 'B-LANGUAGE', 8: 'I-LANGUAGE'
}

@st.cache_resource
def load_model():
    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
    model = AutoModelForTokenClassification.from_pretrained(MODEL_REPO)
    return tokenizer, model

def predict_entities(text, tokenizer, model):
    tokens = list(text)
    inputs = tokenizer(tokens, is_split_into_words=True, return_tensors="pt", truncation=True, max_length=128)
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predictions = torch.argmax(logits, dim=-1)[0]
    word_ids = inputs.word_ids(batch_index=0)
    entities = {}
    current_entity = ""
    current_type = ""
    for idx, word_id in enumerate(word_ids):
        if word_id is None:
            continue
        label = token_label_map[predictions[idx].item()]
        if label.startswith("B-"):
            if current_type:
                entities[current_type] = current_entity
            current_type = label[2:]
            current_entity = tokens[word_id]
        elif label.startswith("I-") and label[2:] == current_type:
            current_entity += tokens[word_id]
        else:
            if current_type:
                entities[current_type] = current_entity
                current_type = ""
                current_entity = ""
    if current_type:
        entities[current_type] = current_entity
    return entities

def search_youtube_videos(query, max_results=10):
    response = youtube.search().list(q=query, part="snippet", type="video", maxResults=max_results).execute()
    results = []
    for item in response['items']:
        results.append({
            'video_id': item['id']['videoId'],
            'title': item['snippet']['title'],
            'description': item['snippet']['description']
        })
    return results

def get_transcript(video_id):
    try:
        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=['ko', 'en'])
        return " ".join([t['text'] for t in transcript])
    except:
        return ""

@st.cache_resource
def load_embedder():
    return SentenceTransformer('snunlp/KR-SBERT-V40K-klueNLI-augSTS')

def embed_texts(embedder, texts):
    return embedder.encode(texts, convert_to_tensor=True)

def recommend_video(embedder, user_conditions, video_infos):
    user_text = " ".join([v for v in user_conditions.values() if v])
    user_embedding = embed_texts(embedder, [user_text])[0]

    scored = []
    for video in video_infos:
        video_text = video['title'] + " " + video['description'] + " " + get_transcript(video['video_id'])
        video_embedding = embed_texts(embedder, [video_text])[0]
        score = cosine_similarity(user_embedding, video_embedding, dim=0).item()
        scored.append((score, video))
    return sorted(scored, reverse=True, key=lambda x: x[0])[:3]

# Streamlit UI
st.title("EduTube Navigator")
st.write("학습 목표를 입력하면 조건을 추출하고 유튜브 영상을 추천합니다.")

user_input = st.text_input("학습 목표를 입력하세요", "딥러닝을 실습 위주로 30분 안에 배우고 싶어요")
if st.button("추천 시작"):
    tokenizer, model = load_model()
    embedder = load_embedder()

    entities = predict_entities(user_input, tokenizer, model)
    #st.subheader("📌 추출된 조건")
    #st.json(entities)

    search_query = " ".join([v for v in entities.values() if v])
    video_candidates = search_youtube_videos(search_query)
    top_recommendations = recommend_video(embedder, entities, video_candidates)

    st.subheader("추천 유튜브 영상")
    for score, video in top_recommendations:
        st.markdown(f"**{video['title']}**  ")
        st.markdown(f"🔗[링크](https://www.youtube.com/watch?v={video['video_id']})")