Create app.py

app.py

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+from fastapi import FastAPI
+from typing import List
+
+from pydantic import BaseModel
+
+import torch
+import transformers
+
+app = FastAPI()
+
+class HebrewText(BaseModel):
+    text: List[str]
+
+@app.post("/diacritize/")
+async def diacritize_hebrew(hebrew_text: HebrewText):
+    model_name = "sadafwalliyani/D_Nikud_model"
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    model = transformers.AutoModel.from_pretrained(model_name)
+
+    input_ids = torch.tensor(tokenizer.encode(hebrew_text.text, return_tensors="pt")).to(model.device)
+
+    # Generate a response using the model's generate function
+    response = model.generate(
+        input_ids,
+        max_length=100,
+        num_beams=5,
+        early_stopping=True,
+        return_dict_in_generate=True,
+        output_scores=True,
+        output_hidden_states=False,
+        return_attention_mask=True,
+        use_cache=True,
+    )
+
+    # Decode the output
+    output_text = tokenizer.decode(response.sequences[0])
+
+    return {"text": output_text}