Convert flask app into fastAPI

app.py

@@ -1,28 +1,36 @@
-from flask import Flask, render_template, request, jsonify
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
 from logic import synthesize_voice, plot_data, plot_waveforms
 import base64
-from flask_cors import CORS
+from typing import Dict
 
-app = Flask(__name__)
-CORS(app)
+app = FastAPI()
 
 
-# Hugging Face model URLs
-tacotron2_model_url = "your_tacotron2_model_url"
-hifi_gan_model_url = "your_hifi_gan_model_url"
-
 # You need to replace the placeholders above with the actual URLs for the models.
 
-@app.route('/')
-def index():
-    return {"message": "Hello World"}
+# Allow requests from your Vercel domain
+origins = [
+    "https://host-test-smoky.vercel.app",
+    # Add other allowed origins if needed
+]
+
+# Set up CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-@app.route('/synthesize', methods=['POST'])
-def synthesize():
-    font_type = request.json['font_select']
-    input_text = request.json['input_text']
+@app.post("/synthesize", response_model=Dict[str, str])
+async def synthesize(request_data: Dict[str, str]):
+    font_type = request_data['font_select']
+    input_text = request_data['input_text']
 
-    # Font selection logic (you can customize this based on your requirements)
+    # Font selection logic (customize based on your requirements)
     if font_type == 'Preeti':
         # Implement Preeti font logic
         pass
@@ -45,17 +53,13 @@ def synthesize():
     # Encode audio content as Base64
     with open(audio_file_path, 'rb') as audio_file:
         audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
-    
-    #You can customize the response based on what information you want to send to the frontend
+
+    # Customize the response based on the information you want to send to the frontend
     response_data = {
         'mel_spectrogram': mel_output_base64,
         'audio_data': audio_base64,
-        'waveform' : wave_base64,
+        'waveform': wave_base64,
         'some_other_data': 'example_value',
     }
 
-    return jsonify(response_data)
-
-    
-if __name__ == '__main__':
-    app.run(debug=True, threaded=True)
+    return JSONResponse(content=response_data)