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from fastapi import FastAPI |
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from fastapi.responses import HTMLResponse |
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from dotenv import load_dotenv |
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from .core.nlp_handler import NLPHandler |
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import os |
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load_dotenv() |
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from api.predict import predict_endpoint |
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from api.quiz import get_quiz_questions, submit_quiz |
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from api.core.chatbot import MBTIChatbot |
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from pydantic import BaseModel |
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chatbot = MBTIChatbot() |
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class ChatRequest(BaseModel): |
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message: str |
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lang: str = "id" |
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from fastapi.middleware.cors import CORSMiddleware |
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app = FastAPI() |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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@app.on_event("startup") |
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async def startup_event(): |
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api_key = os.getenv("YOUTUBE_API_KEY") |
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print("\n" + "="*40) |
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if api_key: |
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print(f"[OK] API KEY DITEMUKAN: {api_key[:5]}...******") |
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print("[MODE] Mode: OFFICIAL API (Anti-Blokir)") |
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else: |
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print("[ERR] API KEY TIDAK DITEMUKAN!") |
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print("[WARN] Mode: FALLBACK SCRAPING (Rawan Error)") |
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print("\n[WAIT] PRE-LOADING MODELS (Transformer + Emotions)...") |
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try: |
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NLPHandler.load_models() |
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print("[OK] Models Loaded Successfully!") |
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except Exception as e: |
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print(f"[ERR] Model Preload Failed: {e}") |
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print("="*40 + "\n") |
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app.add_api_route("/api/predict", predict_endpoint, methods=["POST"]) |
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app.add_api_route("/api/quiz", get_quiz_questions, methods=["GET"]) |
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app.add_api_route("/api/quiz", submit_quiz, methods=["POST"]) |
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@app.post("/api/chat") |
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async def chat_endpoint(request: ChatRequest): |
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return {"response": chatbot.generate_response(request.message, request.lang)} |
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@app.get("/api/hello") |
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def health_check(): |
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has_key = bool(os.getenv("YOUTUBE_API_KEY")) |
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return { |
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"status": "online", |
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"mode": "youtube_ready", |
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"api_key_detected": has_key |
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} |
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@app.get("/", response_class=HTMLResponse) |
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def read_root(): |
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return """ |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Sentimind API</title> |
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<style> |
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body, html { margin: 0; padding: 0; height: 100%; overflow: hidden; } |
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iframe { width: 100%; height: 100%; border: none; } |
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</style> |
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</head> |
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<body> |
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<iframe src="https://sentimind.vercel.app"></iframe> |
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</body> |
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</html> |
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""" |
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@app.get("/api/youtube/{video_id}") |
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def analyze_youtube_video(video_id: str): |
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data = NLPHandler.fetch_youtube_transcript(video_id) |
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if not data: |
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return { |
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"success": False, |
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"error": "NO_TRANSCRIPT" |
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} |
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if isinstance(data, dict) and "text_for_analysis" in data: |
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text_for_analysis = data["text_for_analysis"] |
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result = NLPHandler.predict_all(text_for_analysis) |
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return { |
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"success": True, |
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"mbti_type": result["mbti"], |
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"emotion": result["emotion"], |
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"keywords": result["keywords"], |
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"reasoning": result["reasoning"], |
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"video": data.get("video"), |
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"comments": data.get("comments", []), |
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"fetched_text": text_for_analysis |
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} |
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result = NLPHandler.predict_all(data) |
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return { |
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"success": True, |
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"mbti_type": result["mbti"], |
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"emotion": result["emotion"], |
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"keywords": result["keywords"], |
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"reasoning": result["reasoning"], |
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"fetched_text": data |
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} |
