Create app.py

app.py

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+from fastapi import FastAPI
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# โหลดโมเดล Sentence-Transformer
+MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModel.from_pretrained(MODEL_NAME)
+
+# สร้าง API
+app = FastAPI()
+
+# ฟังก์ชันแปลงข้อความเป็นเวกเตอร์
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    embedding = outputs.last_hidden_state.mean(dim=1)  # ใช้ค่าเฉลี่ยของ hidden states
+    return embedding.squeeze().tolist()
+
+# API Endpoint
+@app.post("/embed")
+async def embed_text(data: dict):
+    text = data.get("text", "")
+    if not text:
+        return {"error": "No text provided"}
+    vector = get_embedding(text)
+    return {"text": text, "embedding": vector}