Update app.py

app.py

@@ -7,6 +7,7 @@ nltk.download("punkt")
 from transformers import pipeline
 import scipy.io.wavfile
 import soundfile as sf
+from huggingface_hub import HfApi, CommitOperationAdd, CommitOperationDelete
 
 model_name = "Shubham09/whisper31filescheck"
 processor = WhisperProcessor.from_pretrained(model_name,task="transcribe")
@@ -28,6 +29,13 @@ def load_data(input_file):
 def write_to_file(input_file):
     fs = 16000
     sf.write("my_Audio_file.flac",input_file, fs)
+    api = HfApi()
+operations = [
+    CommitOperationAdd(path_in_repo="my_Audio_file.flac", path_or_fileobj="~/repo/my_Audio_file.flac"),
+    # CommitOperationAdd(path_in_repo="weights.h5", path_or_fileobj="~/repo/weights-final.h5"),
+    # CommitOperationDelete(path_in_repo="old-weights.h5"),
+    # CommitOperationDelete(path_in_repo="logs/"),
+]
 
 
     
@@ -39,14 +47,12 @@ def write_to_file(input_file):
     # decode_string = base64.b64decode(input_file)
     # wav_file.write(decode_string)  
 
-# def correct_casing(input_sentence):
 
-#   sentences = nltk.sent_tokenize(input_sentence)
-#   return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
+
 pipe = pipeline(model="Shubham09/whisper31filescheck")  # change to "your-username/the-name-you-picked"
 
 def asr_transcript(audio):
-    audio = "my_Audio_file.flac"
+    audio = "~/repo/my_Audio_file.flac"
     text = pipe(audio)["text"]
     return text