Update app.py

app.py

@@ -1,54 +1,80 @@
-from flask import Flask, render_template, request, jsonify
+from fastapi import FastAPI
+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, cross_origin
+from typing import Dict
+import httpx
 
-app = Flask(__name__)
-CORS(app, support_credentials=True)
+app = FastAPI()
 
+@app.get("/")
+def read_root():
+    return {"Voice": "Cloning"}
 
-@app.route('/')
-def index():
-    return {'Voice': 'Cloning'}
 
-@app.route('/synthesize', methods=['POST'])
-def synthesize():
-    font_type = request.json['font_select']
-    input_text = request.json['input_text']
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-    # Font selection logic (you can customize this based on your requirements)
-    if font_type == 'Preeti':
-        # Implement Preeti font logic
-        pass
-    elif font_type == 'Unicode':
-        # Implement Unicode font logic
-        pass
+hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"
 
-    # Generate mel-spectrogram using Tacotron2
-    mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned")
+@app.post("/synthesize", response_model=Dict[str, str])
+async def synthesize(request_data: Dict[str, str]):
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.post(hugging_face_api_url, json=request_data)
 
-    # Convert mel-spectrogram to base64 for display in HTML
-    mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
+        if response.status_code != 200:
+            error_message = f"Error from Hugging Face API: {response.text}"
+            print(f"Error from Hugging Face API: {response.text}")
+            return JSONResponse(content={"error": error_message}, status_code=500)
 
-    # Save the generated audio file
-    audio_file_path = 'audio_output/mel1_generated_e2e.wav'
+        # Process the response from Hugging Face API
+        hugging_face_response = response.json()
 
-    # Plot the waveform
-    wave_base64 = plot_waveforms(audio_file_path)
+        font_type = request_data['font_select']
+        input_text = request_data['input_text']
 
-    # 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
-    response_data = {
-        'mel_spectrogram': mel_output_base64,
-        'audio_data': audio_base64,
-        'waveform': wave_base64,
-        'some_other_data': 'example_value',
-    }
+        # 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
 
-    return jsonify(response_data)
+        # Generate mel-spectrogram using Tacotron2
+        mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned")
 
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8000, debug=True, threaded=True)
+        # Convert mel-spectrogram to base64 for display in HTML
+        mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
+
+        # Save the generated audio file
+        audio_file_path = 'audio_output/mel1_generated_e2e.wav'
+
+        # Plot the waveform
+        wave_base64 = plot_waveforms(audio_file_path)
+
+        # Encode audio content as Base64
+        with open(audio_file_path, 'rb') as audio_file:
+            audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
+
+        # 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,
+            'some_other_data': 'example_value',
+            'hugging_face_response': hugging_face_response,  # Include Hugging Face API response
+        }
+
+        return JSONResponse(content=response_data)
+    except Exception as e:
+        error_message = f"Error during processing: {str(e)}"
+        print(f"Error during processing: {str(e)}")
+        return JSONResponse(content={"error": error_message}, status_code=500)