Update app.py

app.py

@@ -7,6 +7,13 @@ from typing import Dict
 import httpx
 
 app = FastAPI()
+
+@app.get("/")
+def read_root():
+    data = {"Voice": "Cloning", "Status": "Success"}
+    return JSONResponse(content=data)
+
+
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -15,36 +22,38 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-@app.get("/")
-def read_root():
-    data = {"Voice": "Cloning", "Status": "Success"}
-    return JSONResponse(content=data)
-
-hugging_face_api_url = "https://lord-reso-host.hf.space/synthesize"
+hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"
 
 @app.post("/synthesize", response_model=Dict[str, str])
-def synthesize(request_data: Dict[str, str]):
+async def synthesize(request_data: Dict[str, str]):
+    print("call successful")
+    
     try:
         with httpx.Client() as client:
             print('try successful')
-            response = client.post(hugging_face_api_url, json=request_data, timeout=300.0)
-
+            response = client.post(hugging_face_api_url, json=request_data, timeout=30.0)
             response.raise_for_status()  # Raises an HTTPError for bad responses
-
             # Process the response from Hugging Face API
             hugging_face_response = response.json()
-
+            
         font_type = request_data['font_select']
         input_text = request_data['input_text']
         print('fetch successful')
         
+        # 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")
-        print('Mel generation successful')
-
+        print("mel generation successful")
+        
         # Convert mel-spectrogram to base64 for display in HTML
         mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
-        print("image conv successful")
 
         # Save the generated audio file
         audio_file_path = 'audio_output/mel1_generated_e2e.wav'
@@ -90,4 +99,4 @@ def synthesize(request_data: Dict[str, str]):
         print("Caught exception")
         error_message = f"Error during processing: {str(e)}"
         print(error_message)
-        return JSONResponse(content={"error": error_message}, status_code=500)
+        return JSONResponse(content={"error": error_message}, status_code=500)