Update backend/api.py

backend/api.py

@@ -3,58 +3,62 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from transformers import AutoTokenizer
 import onnxruntime as ort
-import numpy as np 
+import numpy as np
 from pathlib import Path
 import traceback
-# import pandas as pd # Dihapus karena tidak dipakai
 
+# === Inisialisasi FastAPI ===
 app = FastAPI()
 
-# === CORS untuk frontend di Vercel ===
+# === CORS untuk frontend (misal dari Vercel) ===
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],      
+    allow_origins=["*"],      # bisa disesuaikan agar lebih aman
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-# === Path setup ===
+# === Path ===
 BASE_DIR = Path(__file__).resolve().parent
 MODEL_PATH = BASE_DIR / "models" / "bert_chatbot.onnx"
-TOKENIZER_PATH = BASE_DIR / "models" / "bert-base-multilingual-cased" 
-# DATASET_PATH tidak diperlukan lagi
+TOKENIZER_PATH = BASE_DIR / "models" / "bert-base-multilingual-cased"
 
-# === Global Variables ===
+# === Global variable ===
 tokenizer = None
 session = None
 
-# === Load tokenizer dan model ===
+# === Load model dan tokenizer ===
 try:
-    print("🚀 Loading ONNX model...")
-    
+    print("🚀 Loading tokenizer dan ONNX model...")
     tokenizer = AutoTokenizer.from_pretrained(str(TOKENIZER_PATH))
     session = ort.InferenceSession(str(MODEL_PATH), providers=["CPUExecutionProvider"])
-    
-    print("✅ ONNX model loaded!")
-
+    print("✅ Model dan tokenizer berhasil dimuat!")
 except Exception as e:
-    # Log ini akan muncul jika ada masalah Path/File saat startup
-    print("--------------------------------------------------")
-    print(f"❌ FATAL ERROR SAAT MEMUAT ONNX/SUMBER DAYA: {e}")
+    print("❌ ERROR saat memuat model/tokenizer:", e)
     traceback.print_exc()
-    print("--------------------------------------------------")
-    pass
 
-# === Default responses ===
+# === Label mapping (HARUS SAMA DENGAN TRAINING) ===
+id2label = {
+    0: "about_me",
+    1: "career_goal",
+    2: "experience",
+    3: "fallback",
+    4: "greeting",
+    5: "projects",
+    6: "skills",
+}
+
+label2id = {v: k for k, v in id2label.items()}
+
+# === Kamus respon SAMA dengan training ===
 responses = {
-    # ⭐ PERBAIKAN INI MEMASTIKAN JAWABAN GREETING BENAR ⭐
     "about_me": "I am a passionate developer specializing in AI and web development.",
     "skills": "My main skills are HTML5, CSS3, JavaScript, Laravel, Node.js, Database, TensorFlow, PyTorch, Firebase, and Jupyter Notebook.",
     "projects": "Some of my projects are Mobile Apps Bald Detection and Jupyter Notebook Bald Detection.",
     "experience": "I have worked as IT Support, AI Engineer, and Freelancer on multiple projects.",
     "career_goal": "My career goal is to become a Full Stack Developer and Machine Learning Engineer.",
-    "greeting": "Hello! How can I help you regarding this portfolio?", # JAWABAN GREETING YANG BENAR
+    "greeting": "Hello! How can I help you regarding this portfolio?",
     "fallback": "I'm sorry, I don't understand. Please ask another question."
 }
 
@@ -64,57 +68,35 @@ class ChatRequest(BaseModel):
 
 @app.get("/")
 async def root():
-    return {"message": "🚀 ONNX Chatbot API running on Hugging Face"}
+    return {"message": "🚀 Chatbot API (ONNX) running on Hugging Face"}
 
 @app.post("/chatbot")
 async def chatbot(req: ChatRequest):
-    # Deklarasikan intent sebagai fallback untuk mencegah NameError di blok except
-    intent = "fallback" 
-    
+    intent = "fallback"
+
     if session is None:
         return {"reply": responses["fallback"], "intent": "error_loading"}
-        
+
     try:
-        # 1. Tokenisasi (return_tensors="np")
+        # === Tokenisasi input ===
         inputs = tokenizer(req.text, return_tensors="np", padding=True, truncation=True, max_length=128)
-        
-        # 2. Perkuat Tipe Data untuk ONNX
-        input_names = [i.name for i in session.get_inputs()]
-        ort_inputs = {}
 
-        for name in input_names:
-            if name in inputs:
-                ort_inputs[name] = inputs[name].astype(np.int64) 
+        # === Siapkan input untuk ONNX ===
+        ort_inputs = {k: v.astype(np.int64) for k, v in inputs.items()}
 
-        # 3. Inferensi ONNX
+        # === Inferensi ===
         ort_outputs = session.run(None, ort_inputs)
-        
-        # 4. Ambil Logit dan Prediksi
         logits = ort_outputs[0]
         pred_id = np.argmax(logits, axis=1)[0]
-        
-        # === Mapping ID ke label ===
-        id2label = {
-    0: "about_me",
-    1: "career_goal",
-    2: "experience",
-    3: "fallback",
-    4: "greeting",
-    5: "projects",
-    6: "skills",
-}
-        
-        # Nilai 'intent' yang benar disimpan di sini
+
+        # === Ambil intent & respon ===
         intent = id2label.get(pred_id, "fallback")
-        
-        # === Ambil jawaban ===
-        # Respon diambil dari dictionary responses global
         reply = responses.get(intent, responses["fallback"])
-                    
-        return {"reply": str(reply), "intent": intent}
-        
+
+        print(f"🧠 Input: {req.text} | Intent: {intent} | Reply: {reply}")
+
+        return {"reply": reply, "intent": intent}
+
     except Exception as e:
-        import traceback
-        print(f"❌ Runtime error in /chatbot: {e}")
         traceback.print_exc()
-        return {"reply": "⚠️ Internal server error.", "intent": intent}
+        return {"reply": "⚠️ Internal server error.", "intent": intent}