Update app.py

app.py

@@ -1,16 +1,16 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import torch
+import requests
 
+# 💡 Load model hanya sekali
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-faq = {}
-
-import requests
-
+# 🔑 Supabase credentials
 SUPABASE_URL = "https://olbjfxlclotxtnpjvpfj.supabase.co"
-SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im9sYmpmeGxjbG90eHRucGp2cGZqIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTIyMzYwMDEsImV4cCI6MjA2NzgxMjAwMX0.7q_o5DCFEAAysnWXMChH4MI5qNhIVc4OgpT5JvgYxc0"  # anon key
+SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im9sYmpmeGxjbG90eHRucGp2cGZqIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTIyMzYwMDEsImV4cCI6MjA2NzgxMjAwMX0.7q_o5DCFEAAysnWXMChH4MI5qNhIVc4OgpT5JvgYxc0"
 
+# 📥 Ambil data FAQ dari Supabase
 def get_faq_from_supabase(uid):
     url = f"{SUPABASE_URL}/rest/v1/faq_texts?uid=eq.{uid}"
     headers = {
@@ -18,43 +18,60 @@ def get_faq_from_supabase(uid):
         "Authorization": f"Bearer {SUPABASE_KEY}",
         "Content-Type": "application/json"
     }
-    
-    r = requests.get(url, headers=headers)
 
-    # 🔍 Tambahkan debug log di sini
-    print("📡 Supabase GET:", url)
-    print("🔐 Status:", r.status_code)
-    print("📦 Body:", r.text)
+    try:
+        r = requests.get(url, headers=headers)
+        print(f"📡 Supabase GET {url}")
+        print(f"📦 Status: {r.status_code}, Body: {r.text}")
+        r.raise_for_status()
+    except Exception as e:
+        print(f"❌ Error fetching from Supabase: {e}")
+        return []
 
-    if r.status_code != 200:
+    try:
+        data = r.json()
+        return [{"q": item["question"], "a": item["answer"]} for item in data]
+    except Exception as e:
+        print("❌ Failed to parse JSON:", e)
         return []
-    data = r.json()
-    return [{"q": item["question"], "a": item["answer"]} for item in data]
 
+# 🤖 Fungsi utama chatbot
 def chatbot(uid, question):
-    user_faq = get_faq_from_supabase(uid)
-    if not user_faq:
-        return "FAQ belum tersedia."
+    print(f"\n💬 Received: UID={uid}, Question={question}")
+    
+    if not uid or not question:
+        return {"data": ["Pertanyaan atau UID tidak valid."]}
+
+    faq_list = get_faq_from_supabase(uid)
+    if not faq_list:
+        return {"data": ["FAQ belum tersedia untuk pengguna ini."]}
+
+    try:
+        questions = [item["q"] for item in faq_list]
+        answers = [item["a"] for item in faq_list]
 
-    questions = [item["q"] for item in user_faq]
-    answers = [item["a"] for item in user_faq]
+        embeddings = model.encode(questions, convert_to_tensor=True)
+        query_embedding = model.encode(question, convert_to_tensor=True)
 
-    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()
+        jawaban = answers[best_idx]
 
-    similarity = util.pytorch_cos_sim(query_embedding, embeddings)
-    best_idx = torch.argmax(similarity).item()
+        print(f"✅ Jawaban terbaik: {jawaban}")
+        return {"data": [jawaban]}
 
-    return {"data": [answers[best_idx]]}
+    except Exception as e:
+        print("❌ Error saat memproses pertanyaan:", e)
+        return {"data": ["Terjadi kesalahan saat memproses pertanyaan."]}
 
-# Gradio Interface (bisa dipanggil via API juga)
+# 🟢 Gradio API
 iface = gr.Interface(
     fn=chatbot,
-    inputs=["text", "text"],
-    outputs="text",
-    examples=[["uid123", "Apakah bisa bayar di tempat?"]],
+    inputs=["text", "text"],  # UID, Pertanyaan
+    outputs="json",           # hasil = { "data": [...] }
+    title="Biruu Chatbot API",
     allow_flagging="never",
-    title="Biruu Chatbot API"
+    examples=[["uid123", "Apakah bisa bayar di tempat?"]]
 )
 
-iface.launch()
+iface.launch(share=True)