Upload handler.py

handler.py

@@ -0,0 +1,40 @@
+from typing import Dict, Any
+from textToStoryGeneration import *
+import logging
+import torch
+import soundfile as sf
+from transformers import AutoTokenizer, AutoModelForTextToWaveform
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+# Configure logging
+logging.basicConfig(level=logging.ERROR)
+# Configure logging
+logging.basicConfig(level=logging.WARNING)
+
+
+
+class CustomHandler:
+    def __init__(self):
+        
+        self.tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
+        self.model= AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng")
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Prepare the payload with input data
+        logging.warning(f"------input_data-- {str(data)}")
+        payload = str(data)
+        logging.warning(f"payload----{str(payload)}")
+        # Set headers with API token
+        inputs = self.tokenizer(payload, return_tensors="pt")
+
+        # Generate the waveform from the input text
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        # Save the audio to a file
+        sf.write("StoryAudio.wav", outputs["waveform"][0].numpy(), self.model.config.sampling_rate)
+        
+        return 'StoryAudio.wav'
+        # Check if the request was successful
+        
+