Update app.py

app.py

@@ -1,16 +1,19 @@
+
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import torch
 import requests
+from fastapi import FastAPI, Request
+from gradio.routes import App
+import uvicorn
 
-# 💡 Load model hanya sekali
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# 🔑 Supabase credentials
+# 🔐 Konfigurasi Supabase
 SUPABASE_URL = "https://olbjfxlclotxtnpjvpfj.supabase.co"
 SUPABASE_KEY = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Im9sYmpmeGxjbG90eHRucGp2cGZqIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NTIyMzYwMDEsImV4cCI6MjA2NzgxMjAwMX0.7q_o5DCFEAAysnWXMChH4MI5qNhIVc4OgpT5JvgYxc0"
 
-# 📥 Ambil data FAQ dari Supabase
+# 🧠 Load model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
 def get_faq_from_supabase(uid):
     url = f"{SUPABASE_URL}/rest/v1/faq_texts?uid=eq.{uid}"
     headers = {
@@ -18,60 +21,44 @@ def get_faq_from_supabase(uid):
         "Authorization": f"Bearer {SUPABASE_KEY}",
         "Content-Type": "application/json"
     }
-
     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 []
-
-    try:
         data = r.json()
-        return [{"q": item["question"], "a": item["answer"]} for item in data]
+        return [{"q": d["question"], "a": d["answer"]} for d in data]
     except Exception as e:
-        print("❌ Failed to parse JSON:", e)
+        print("❌ Supabase error:", e)
         return []
 
-# 🤖 Fungsi utama chatbot
 def chatbot(uid, question):
-    print(f"\n💬 Received: UID={uid}, Question={question}")
-    
-    if not uid or not question:
-        return {"data": ["Pertanyaan atau UID tidak valid."]}
+    faqs = get_faq_from_supabase(uid)
+    if not faqs:
+        return "Tidak ada data FAQ untuk pengguna ini."
 
-    faq_list = get_faq_from_supabase(uid)
-    if not faq_list:
-        return {"data": ["FAQ belum tersedia untuk pengguna ini."]}
+    questions = [f["q"] for f in faqs]
+    answers = [f["a"] for f in faqs]
 
-    try:
-        questions = [item["q"] for item in faq_list]
-        answers = [item["a"] for item in faq_list]
+    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)
+    scores = util.pytorch_cos_sim(query_embedding, embeddings)
+    best_idx = torch.argmax(scores).item()
+    return answers[best_idx]
 
-        similarity = util.pytorch_cos_sim(query_embedding, embeddings)
-        best_idx = torch.argmax(similarity).item()
-        jawaban = answers[best_idx]
+# 🎛️ Buat UI untuk testing
+demo = gr.Interface(fn=chatbot, inputs=["text", "text"], outputs="text", title="Chatbot")
 
-        print(f"✅ Jawaban terbaik: {jawaban}")
-        return {"data": [jawaban]}
+# 🚀 Tambahkan FastAPI app agar bisa menerima POST request
+app = FastAPI()
+app = App(app, demo)
 
+# ✅ Endpoint khusus untuk Flutter/Postman
+@app.post("/predict")
+async def predict(request: Request):
+    try:
+        payload = await request.json()
+        uid, question = payload["data"]
+        result = chatbot(uid, question)
+        return {"data": [result]}
     except Exception as e:
-        print("❌ Error saat memproses pertanyaan:", e)
-        return {"data": ["Terjadi kesalahan saat memproses pertanyaan."]}
-
-# 🟢 Gradio API
-iface = gr.Interface(
-    fn=chatbot,
-    inputs=["text", "text"],  # UID, Pertanyaan
-    outputs="json",           # hasil = { "data": [...] }
-    title="Biruu Chatbot API",
-    allow_flagging="never",
-    examples=[["uid123", "Apakah bisa bayar di tempat?"]]
-)
-
-iface.launch(share=True)
+        return {"error": str(e)}