Update app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, Request, Response
+from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse, StreamingResponse
 from fastapi.middleware.cors import CORSMiddleware
 from logic import synthesize_voice, plot_data, plot_waveforms
@@ -7,15 +7,10 @@ import sys
 import numpy as np
 from io import BytesIO
 from hifigan.inference_e2e import hifi_gan_inference
-import asyncio
 
 app = FastAPI()
 
-@app.get("/")
-def read_root():
-    data = {"Voice": "Cloning", "Status": "Success"}
-    return JSONResponse(content=data)
-
+# Enable CORS
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -24,11 +19,15 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"
+# SSE initialization
+async def send_progress(progress: int):
+    data = f"data: {progress}\n\n"
+    return StreamingResponse(content=data.encode(), media_type="text/event-stream")
 
 @app.post("/synthesize")
 async def synthesize(request: Request):
     print("call successful")
+    await send_progress(0)  # Send initial progress
     
     json = await request.json()
     print(json)
@@ -36,26 +35,9 @@ async def synthesize(request: Request):
     font_type = json['font_select']
     input_text = json['input_text']     
     
-    # SSE initialization
-    async def generate():
-        yield "data: 0\n\n"  # Initial progress
-
-    response = StreamingResponse(generate(), media_type="text/event-stream")
-
-    # SSE function to send progress updates
-    async def send_progress(progress):
-        await response.send(f"data: {progress}\n\n")
-
-    await send_progress(0)  # Send initial progress
-    
     # Generate mel-spectrogram using Tacotron2
     mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned.pt")
     print("mel generation successful")
-    
-    # Simulate progress updates for demonstration purposes
-    for progress in range(10, 101, 10):
-        await send_progress(progress)
-        await asyncio.sleep(1)
         
     # Convert mel-spectrogram to base64 for display in HTML
     mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
@@ -84,6 +66,5 @@ async def synthesize(request: Request):
         'some_other_data': 'example_value',
         # 'hugging_face_response': hugging_face_response,  # Include Hugging Face API response
     }
-    
-    await send_progress(100)  # Send 100% progress to indicate completion
+
     return JSONResponse(content=response_data)