Update app.py

app.py

@@ -1,45 +1,26 @@
-import os
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf"
-os.makedirs("/tmp/hf", exist_ok=True)
+import os from fastapi import FastAPI, Request from sentence_transformers import SentenceTransformer, util import torch import requests
 
-from sentence_transformers import SentenceTransformer, util
-import torch
-import requests
-from fastapi import FastAPI, Request
+✅ Pastikan cache model tersimpan di lokasi yang bisa ditulis
 
-# 🔐 Supabase config
-SUPABASE_URL = "https://olbjfxlclotxtnpjvpfj.supabase.co"
-SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf" os.makedirs("/tmp/hf", exist_ok=True)
 
-# 🔍 Load model sekali
-model = SentenceTransformer("all-MiniLM-L6-v2")
+🔐 Supabase config
+
+SUPABASE_URL = "https://olbjfxlclotxtnpjvpfj.supabase.co" SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im9sYmpmeGxjbG90eHRucGp2cGZqIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTIyMzYwMDEsImV4cCI6MjA2NzgxMjAwMX0.7q_o5DCFEAAysnWXMChH4MI5qNhIVc4OgpT5JvgYxc0"
+
+✅ Load model (gunakan versi lebih kecil untuk pengujian)
+
+model = SentenceTransformer("paraphrase-MiniLM-L3-v2")
+
+🚀 FastAPI app
 
-# 🚀 FastAPI app
 app = FastAPI()
 
-# 🔽 Ambil data FAQ dari Supabase
-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])
-
-    if not uid or not question:
+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): try: body = await request.json() uid, question = body.get("data", [None, None])
+
+if not uid or not question:
         return {"data": ["UID atau pertanyaan tidak valid."]}
 
     faqs = get_faq_from_supabase(uid)
@@ -55,4 +36,8 @@ async def predict(request: Request):
     similarity = util.pytorch_cos_sim(query_embedding, embeddings)
     best_idx = torch.argmax(similarity).item()
 
-    return {"data": [answers[best_idx]]}
+    return {"data": [answers[best_idx]]}
+
+except Exception as e:
+    print("❌ Error processing request:", e)
+    return {"data": ["Terjadi kesalahan pada server."]}