Update app.py

app.py

@@ -1,62 +1,62 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-# Daftar model yang dipakai
-MODELS = {
-    "in2bg": "rahmanansah/t5-id-bugis",
-    "bg2id": "rahmanansah/t5-bugis-id"
-}
-
-# Simpan tokenizer & model yang sudah diload
-loaded_models = {}
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-def load_model(model_id):
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_id).to(device)
-    return tokenizer, model
-
-# Preload semua model
-for key, model_id in MODELS.items():
-    print(f"🔄 Loading {key} -> {model_id}")
-    loaded_models[key] = load_model(model_id)
-print("✅ Semua model sudah diload")
-
-app = FastAPI()
-
-class InputText(BaseModel):
-    text: str
-    model: str  # "in2bg" atau "bg2id"
-
-@app.post("/translate")
-def translate(input: InputText):
-    if input.model not in loaded_models:
-        return {"error": f"Model '{input.model}' tidak tersedia. Pilihan: {list(loaded_models.keys())}"}
-
-    tokenizer, model = loaded_models[input.model]
-
-    if not input.text.strip():
-        return {"result": ""}
-
-    text = input.text.strip()
-
-    # Tambahkan prefix sesuai arah model
-    if input.model == "in2bg":
-        prefixed_text = f"translate id2bg: {text}"
-    elif input.model == "bg2id":
-        prefixed_text = f"translate bg2id: {text}"
+from flask import Flask, request, jsonify
+import requests
+import os
+
+app = Flask(__name__)
+
+# Token Hugging Face dari environment
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Endpoint untuk model
+QWEN_MODEL = "Qwen/Qwen2.5-1.5B-Instruct"
+TRANS_MODEL = "rahmanansah/t5-id-bugis"  # ganti sesuai repo kamu
+
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def query_hf(model_id, inputs, parameters=None):
+    """Panggil Hugging Face Inference API"""
+    url = f"https://api-inference.huggingface.co/models/{model_id}"
+    payload = {"inputs": inputs}
+    if parameters:
+        payload["parameters"] = parameters
+    response = requests.post(url, headers=HEADERS, json=payload)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": f"{response.status_code}: {response.text}"}
+
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    data = request.json
+    user_input = data.get("text", "").strip()
+
+    if not user_input:
+        return jsonify({"reply": "Teks kosong, silakan masukkan sesuatu."})
+
+    # --- Logika Pemisah ---
+    if user_input.lower().startswith("terjemahkan:"):
+        # Hapus prefix "terjemahkan:" → ambil teks mentah
+        clean_text = user_input[len("terjemahkan:"):].strip()
+        if not clean_text:
+            return jsonify({"reply": "Silakan masukkan teks setelah 'terjemahkan:'"})
+        
+        # Panggil model translator
+        result = query_hf(TRANS_MODEL, f"translate id2bg: {clean_text}")
+        if isinstance(result, list) and "generated_text" in result[0]:
+            reply = result[0]["generated_text"]
+        else:
+            reply = result.get("error", "Terjadi kesalahan pada model terjemahan.")
     else:
-        prefixed_text = text
+        # Panggil Qwen sebagai chatbot interaktif
+        result = query_hf(QWEN_MODEL, user_input, parameters={"max_new_tokens": 200})
+        if isinstance(result, list) and "generated_text" in result[0]:
+            reply = result[0]["generated_text"]
+        else:
+            reply = result.get("error", "Terjadi kesalahan pada model interaktif.")
 
-    inputs = tokenizer(prefixed_text, return_tensors="pt").to(device)
-    outputs = model.generate(**inputs, max_length=64)
-    decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return jsonify({"reply": reply})
 
-    return {"result": decoded}
 
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run("app:app", host="0.0.0.0", port=7860)
+    app.run(host="0.0.0.0", port=5000)