Spaces:
Runtime error
Runtime error
| import whisper | |
| import gradio as gr | |
| import openai | |
| import os | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| model = whisper.load_model("small") | |
| def transcribe(audio): | |
| model = whisper.load_model("base") | |
| result = model.transcribe(audio) | |
| return result["text"] | |
| # def transcribe(audio): | |
| # #time.sleep(3) | |
| # # load audio and pad/trim it to fit 30 seconds | |
| # audio = whisper.load_audio(audio) | |
| # audio = whisper.pad_or_trim(audio) | |
| # # make log-Mel spectrogram and move to the same device as the model | |
| # mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| # # detect the spoken language | |
| # _, probs = model.detect_language(mel) | |
| # print(f"Detected language: {max(probs, key=probs.get)}") | |
| # # decode the audio | |
| # options = whisper.DecodingOptions(fp16 = False) | |
| # result = whisper.decode(model, mel, options) | |
| # return result.text | |
| def process_text(input_text): | |
| # Apply your function here to process the input text | |
| output_text = input_text.upper() | |
| return output_text | |
| def get_completion(prompt, model='gpt-3.5-turbo'): | |
| messages = [ | |
| {"role": "system", "content": """You are a world class nurse practitioner. You are provided with the transcription following a patient's visit. \ | |
| Extract the following information from the transcription, replace curly brackets with relevant extracted information, and present as follows, one category per line: \ | |
| Date of Visit: {} | |
| Claimant: {} | |
| Client/Employer: {} | |
| Claim #: {} | |
| DOI (Date of Injury): {} | |
| Provider: {} | |
| Diagnosis Treated: {} | |
| Subjective findings: {} | |
| Objective Findings: {} | |
| Treatment plan: {} | |
| Medications: {} | |
| RTW (Return to Work) Status: {} | |
| Restrictions: {} | |
| NOV (Next Office Visit): {} | |
| Only use the information from the provided transcription. Do not make up stuff. If information is not available just put "N/A" next to the relevant line. | |
| """ | |
| }, | |
| {"role": "user", "content": prompt} | |
| ] | |
| response = openai.ChatCompletion.create( | |
| model = model, | |
| messages = messages, | |
| temperature = 0, | |
| ) | |
| return response.choices[0].message['content'] | |
| with gr.Blocks() as demo: | |
| title = "Chat with NP" | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| b1 = gr.Button("Transcribe audio") | |
| b2 = gr.Button("Prepare a report") | |
| text1 = gr.Textbox(lines=5) | |
| text2 = gr.Textbox(lines=5) | |
| prompt = text1 | |
| b1.click(transcribe, inputs=audio, outputs=text1) | |
| b2.click(get_completion, inputs=text1, outputs=text2) | |
| # b1.click(transcribe, inputs=audio, outputs=text1) | |
| # b2.click(get_completion, inputs=prompt, outputs=text2) | |
| demo.launch() | |
| # In this example, the process_text function just converts the input text to uppercase, but you can replace it with your desired function. The Gradio Blocks interface will have two buttons: "Transcribe audio" and "Process text". The first button transcribes the audio and fills the first textbox, and the second button processes the text from the first textbox and fills the second textbox. | |
| # gr.Interface( | |
| # title = 'OpenAI Whisper ASR Gradio Web UI', | |
| # fn=transcribe, | |
| # inputs=[ | |
| # gr.inputs.Audio(source="microphone", type="filepath") | |
| # ], | |
| # outputs=[ | |
| # "textbox" | |
| # ], | |
| # live=True).launch() | |