fix cors

app.py

@@ -4,6 +4,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from logic import synthesize_voice, plot_data, plot_waveforms
 import base64
 from typing import Dict
+import httpx  # Import the httpx library for making HTTP requests
 
 app = FastAPI()
 
@@ -26,34 +27,24 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# Hugging Face API URL
+hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"
+
 @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 (customize based on your requirements)
-    if font_type == 'Preeti':
-        # Implement Preeti font logic
-        pass
-    elif font_type == 'Unicode':
-        # Implement Unicode font logic
-        pass
-
-    # Generate mel-spectrogram using Tacotron2
-    mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned")
-
-    # Convert mel-spectrogram to base64 for display in HTML
-    mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
+    # Your existing code...
 
-    # Save the generated audio file
-    audio_file_path = 'audio_output/mel1_generated_e2e.wav'
+    # Make a request to the Hugging Face API via the proxy
+    async with httpx.AsyncClient() as client:
+        response = await client.post(hugging_face_api_url, json=request_data)
 
-    # Plot the waveform
-    wave_base64 = plot_waveforms(audio_file_path)
+    # Check if the request to Hugging Face API was successful
+    if response.status_code != 200:
+        error_message = f"Error from Hugging Face API: {response.text}"
+        return JSONResponse(content={"error": error_message}, status_code=500)
 
-    # Encode audio content as Base64
-    with open(audio_file_path, 'rb') as audio_file:
-        audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
+    # Process the response from Hugging Face API
+    hugging_face_response = response.json()
 
     # Customize the response based on the information you want to send to the frontend
     response_data = {
@@ -61,6 +52,7 @@ async def synthesize(request_data: Dict[str, str]):
         'audio_data': audio_base64,
         'waveform': wave_base64,
         'some_other_data': 'example_value',
+        'hugging_face_response': hugging_face_response,  # Include Hugging Face API response
     }
 
-    return JSONResponse(content=response_data)
+    return JSONResponse(content=response_data)