Update app.py

app.py

@@ -1,66 +1,16 @@
 import os
 import shutil
 from fastapi import FastAPI, Request
-from sentence_transformers import SentenceTransformer, util
-import torch
 import requests
 
-# 🔐 Paksa semua cache ke path aman di Hugging Face Spaces
-HF_CACHE = "/tmp/hf"
-os.environ["TRANSFORMERS_CACHE"] = HF_CACHE
-os.environ["HF_HOME"] = HF_CACHE
-os.makedirs(HF_CACHE, exist_ok=True)
-
-# ✅ Bersihkan cache jika terkunci
-if os.path.exists(f"{HF_CACHE}/models--sentence-transformers--paraphrase-MiniLM-L3-v2.lock"):
-    os.remove(f"{HF_CACHE}/models--sentence-transformers--paraphrase-MiniLM-L3-v2.lock")
-if os.path.exists(f"{HF_CACHE}/models--sentence-transformers--paraphrase-MiniLM-L3-v2"):
-    shutil.rmtree(f"{HF_CACHE}/models--sentence-transformers--paraphrase-MiniLM-L3-v2", ignore_errors=True)
-
-# 🔐 Supabase
-SUPABASE_URL = "https://olbjfxlclotxtnpjvpfj.supabase.co"
-SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im9sYmpmeGxjbG90eHRucGp2cGZqIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTIyMzYwMDEsImV4cCI6MjA2NzgxMjAwMX0.7q_o5DCFEAAysnWXMChH4MI5qNhIVc4OgpT5JvgYxc0"
-
-# 🔄 Gunakan model kecil dan cepat
-model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L3-v2")
-
 app = FastAPI()
 
-def get_faq_from_supabase(uid):
-    url = f"{SUPABASE_URL}/rest/v1/faq_texts?uid=eq.{uid}"
-    headers = {
-        "apikey": SUPABASE_KEY,
-        "Authorization": f"Bearer {SUPABASE_KEY}",
-        "Content-Type": "application/json"
-    }
-    try:
-        r = requests.get(url, headers=headers)
-        r.raise_for_status()
-        data = r.json()
-        return [{"q": d["question"], "a": d["answer"]} for d in data]
-    except Exception as e:
-        print("❌ Supabase error:", e)
-        return []
-
 @app.post("/predict")
 async def predict(request: Request):
     body = await request.json()
-    uid, question = body.get("data", [None, None])
+    uid = body.get("data", [None])
     
-    if not uid or not question:
-        return {"data": ["UID atau pertanyaan tidak valid."]}
-    
-    faqs = get_faq_from_supabase(uid)
-    if not faqs:
-        return {"data": ["FAQ tidak ditemukan untuk UID ini."]}
-
-    questions = [f["q"] for f in faqs]
-    answers = [f["a"] for f in faqs]
-
-    embeddings = model.encode(questions, convert_to_tensor=True)
-    query_embedding = model.encode(question, convert_to_tensor=True)
-
-    similarity = util.pytorch_cos_sim(query_embedding, embeddings)
-    best_idx = torch.argmax(similarity).item()
+    if not uid:
+        return {"data": ["UID not valid."]}
 
-    return {"data": [answers[best_idx]]}
+    return {"data": []}