Update app.py

app.py

@@ -1,17 +1,21 @@
-import gradio as gr
+from fastapi import FastAPI, Request
+from fastapi.responses import FileResponse
 import torch
 import numpy as np
 import scipy.io.wavfile
 from transformers import VitsModel, AutoTokenizer
 import re
 
-# Load fine-tuned model from Hugging Face Hub or local path
+app = FastAPI()
+
+# Load your model
 model = VitsModel.from_pretrained("Somali-tts/somali_tts_model")
 tokenizer = AutoTokenizer.from_pretrained("saleolow/somali-mms-tts")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 model.eval()
 
+# Number conversion (keep your existing number_words + number_to_words)
 number_words = {
     0: "eber", 1: "koow", 2: "labo", 3: "seddex", 4: "afar", 5: "shan",
     6: "lix", 7: "todobo", 8: "sideed", 9: "sagaal", 10: "toban",
@@ -75,23 +79,13 @@ def normalize_text(text):
     text = text.replace("ZamZam", "SamSam")
     return text
 
-def tts(text):
+@app.post("/tts")
+async def tts(request: Request):
+    data = await request.json()
+    text = normalize_text(data["text"])
     inputs = tokenizer(text, return_tensors="pt").to(device)
     with torch.no_grad():
         waveform = model(**inputs).waveform.squeeze().cpu().numpy()
     filename = "output.wav"
     scipy.io.wavfile.write(filename, rate=model.config.sampling_rate, data=(waveform * 32767).astype(np.int16))
-    return filename  # ✅ Audio file path
-
-
-
-gr.Interface(
-    fn=tts,
-    inputs=gr.Textbox(label="Geli qoraal Soomaali ah"),
-    outputs=gr.Audio(label="Codka TTS", type="filepath"),  # ✅ not gr.File
-    title="Somali TTS",
-    description="Ku qor qoraal Soomaaliyeed si aad u maqasho cod dabiici ah.",
-).launch()
-
-
- 
+    return FileResponse(filename, media_type="audio/wav")