Update app.py

app.py

@@ -7,6 +7,7 @@ import uuid
 from io import StringIO
 from io import BytesIO
 import base64
+import requests
 
 import gradio as gr
 import spaces
@@ -179,10 +180,12 @@ def predict(
         # torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
         torchaudio.save(buffer, torch.tensor(out["wav"]).unsqueeze(0), 24000, format='wav')
 
-        buffer.seek(0)
-        base_gr = base64.b64encode(buffer.read()).decode('utf-8')
-        print(base_gr)
-        base_gr
+        output_path = "output.wav"
+        with open(output_path, "wb") as f:
+            f.write(buffer.getbuffer())
+        
+        upload_url = "https://temp.sh/upload"
+        response = requests.post(upload_url, files={"file": open(output_path, "rb")})
 
     except RuntimeError as e:
         if "device-side assert" in str(e):
@@ -248,7 +251,7 @@ def predict(
                     "Something unexpected happened please retry again."
                 )
             return (None, metrics_text)
-    return ("output.wav", metrics_text, base_gr)
+    return ("output.wav", metrics_text, response)
 
 
 with gr.Blocks(analytics_enabled=False) as demo:
@@ -317,7 +320,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
         with gr.Column():
             audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
             out_text_gr = gr.Text(label="Metrics")
-            base_gr = gr.Text(label="Base64")
+            response = gr.Text(label="temp")
 
     tts_button.click(
         predict,
@@ -327,7 +330,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
             ref_gr,
             normalize_text,
         ],
-        outputs=[audio_gr, out_text_gr, base_gr],
+        outputs=[audio_gr, out_text_gr, response],
         api_name="predict",
     )