Create app.py

app.py

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+
+
+
+from numpy import True_
+import gradio as gr
+import openai, subprocess
+import os
+import soundfile as sf
+from pydub import AudioSegment
+
+%cd /content/drive/MyDrive/Colab_Notebooks/
+note_transcript = ""
+
+def transcribe(audio, history_type):
+  global note_transcript    
+     
+  if history_type == "History":
+    with open("Format_Library/Weldon_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "Physical":
+    with open("Format_Library/Weldon_PE_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "H+P":
+    with open("Format_Library/Weldon_Full_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "Impression/Plan":
+    with open("Format_Library/Weldon_Impression_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "Handover":
+    with open("Format_Library/Weldon_Handover_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "Meds Only":
+    with open("Format_Library/Medications.txt", "r") as f:
+      role = f.read()
+  elif history_type == "EMS":
+    with open("Format_Library/EMS_Handover_Note_Format.txt", "r") as f:
+      role = f.read()
+  elif history_type == "Triage":
+    with open("Format_Library/Triage_Note_Format.txt", "r") as f:
+      role = f.read()
+  else:
+    with open("Format_Library/Weldon_Full_Note_Format.txt", "r") as f:
+      role = f.read()
+
+  messages = [{"role": "system", "content": role}]
+
+  ###### Create Dialogue Transcript from Audio Recording and Append(via Whisper)
+  # Load the audio file (from filepath)
+  audio_data, samplerate = sf.read(audio)
+
+  #### Massage .wav and save as .mp3
+  #audio_data = audio_data.astype("float32")
+  #audio_data = (audio_data * 32767).astype("int16")
+  #audio_data = audio_data.mean(axis=1)
+  sf.write("Audio_Files/test.wav", audio_data, samplerate, subtype='PCM_16')
+  sound = AudioSegment.from_wav("Audio_Files/test.wav")
+  sound.export("Audio_Files/test.mp3", format="mp3")
+
+
+  #Send file to Whisper for Transcription
+  audio_file = open("Audio_Files/test.mp3", "rb")
+  audio_transcript = openai.Audio.transcribe("whisper-1", audio_file)
+  print(audio_transcript)
+  messages.append({"role": "user", "content": audio_transcript["text"]})
+  
+  #Create Sample Dialogue Transcript from File (for debugging)
+  #with open('Audio_Files/Test_Elbow.txt', 'r') as file:
+  #  audio_transcript = file.read()
+  #messages.append({"role": "user", "content": audio_transcript})
+  
+
+  ### Word and MB Count
+  file_size = os.path.getsize("Audio_Files/test.mp3")
+  mp3_megabytes = file_size / (1024 * 1024)
+  mp3_megabytes = round(mp3_megabytes, 2)
+
+  audio_transcript_words = audio_transcript["text"].split() # Use when using mic input
+  #audio_transcript_words = audio_transcript.split() #Use when using file
+
+  num_words = len(audio_transcript_words)
+
+
+  #Ask OpenAI to create note transcript
+  response = openai.ChatCompletion.create(model="gpt-3.5-turbo", temperature=0, messages=messages)
+  note_transcript = (response["choices"][0]["message"]["content"])
+   
+  return [note_transcript, num_words,mp3_megabytes]
+
+#Define Gradio Interface
+my_inputs = [
+    gr.Audio(source="microphone", type="filepath"),
+    gr.Radio(["History","Physical", "H+P","Impression/Plan","Handover","EMS","Triage","Meds Only"], show_label=False),
+]
+
+ui = gr.Interface(fn=transcribe, 
+                  inputs=my_inputs, 
+                  outputs=[gr.Textbox(label="Your Note").style(show_copy_button=True),
+                           gr.Number(label="Audio Word Count"),
+                           gr.Number(label=".mp3 MB")])
+
+
+ui.launch(share=False, debug=True)