ortexsolution/VR4

1import torch2import spaces3 4import gradio as gr5from transformers import pipeline6 7import time8import os9import shutil10import requests11from openai import OpenAI12 13# from CGPT_tools import fill_template14 15model_ids = {16"Fast": "openai/whisper-small",17"Balanced": "openai/whisper-medium",18"Accurate": "openai/whisper-large-v3-turbo"19}20 21device = 0 if torch.cuda.is_available() else "cpu"22 23def upload_to_server(audio_file, text_file):24    url = "http://82.115.24.188:8000/upload" 25    files = {'audio_file': (os.path.basename(audio_file.name), audio_file, 'application/octet-stream'),26             'text_file': (os.path.basename(text_file.name), text_file, 'text/plain')}27    28    response = requests.post(url, files=files)29    return response.text30 31@spaces.GPU32def transcribe(model_name, inputs):33    if inputs is None:34        raise gr.Error("No audio file submitted!")35    36    pipe = pipeline(37        task="automatic-speech-recognition",38        model=model_ids[model_name],39        chunk_length_s=30,40        device=device,41    )42    id = str(int(time.time()))43    audio_files_path = "audio_files/"44    shutil.copy(inputs, audio_files_path+id+".wav")45 46    text = pipe(inputs, batch_size=8, generate_kwargs={"task": "transcribe" , "language": "en","num_beams": 3}, return_timestamps=True)["text"] 47    with open(audio_files_path+id+".txt", "w", encoding='utf-8') as f:48        f.write(text)49 50    with open(audio_files_path+id+".wav", 'rb') as audio_file, open(audio_files_path+id+".txt", 'rb') as text_file:51        upload_to_server(audio_file, text_file)52 53    return text54 55def read_template_files(folder_path):56 57    template_file_contents = []58    file_list = sorted(os.listdir(folder_path))59 60    for file_name in file_list:61        with open(folder_path+file_name, 'r', encoding='utf-8') as file:62            content = file.read()63            template_file_contents.append(content)64    return template_file_contents65 66templates_content = read_template_files("report_templates/")67 68templates = {69    "" : "",70    "Mauro Cervical Spine Levels -- 3MCS" : templates_content[0],71    "Mauro - CT Chest (With Contrastor Non-Contrast) -- MCSMCTC" : templates_content[1],72    "Houman - CT Cervical Nerve -- HECTPSI" : templates_content[2],73    "Houman - Result Consult -- HERESCON" : templates_content[3],74}75 76def show_template(tn):77    return templates[tn]78 79def fill_template(template_content, massage):80    client = OpenAI(81        api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted82    )83    reponse = client.chat.completions.create(84    messages=[85        {86            "role": "system", "content":"You are a helpful and knowledgeable assistant in a radiology center. Answer briefly and concisely.",87            "role": "user", "content": f"Complete the following template with the given text. Do not add any extra information, just insert the details from the text provided in the appropriate places\nHere is a report template:\n{template_content}\n Complete the  template with this text: \n'{massage}' "88        }89    ],90    model="gpt-4",91    temperature= 0.0,92    )93    return reponse.choices[0].message.content94 95 96 97 98demo = gr.Blocks(theme=gr.themes.Ocean())99file_transcribe_output = gr.TextArea()100mf_transcribe_output = gr.TextArea()101file_transcribe = gr.Interface(102    fn=transcribe,103    inputs=[104        gr.Radio(list(model_ids.keys()), label="Step 2. Select your model⬇️", value="Fast"),105        gr.Audio(sources="upload", type="filepath",label="Step 3. Upload your audio file, and click the submit button⬇️")106    ],107    outputs=file_transcribe_output,108    flagging_mode="never",109)110 111mf_transcribe = gr.Interface(112    fn=transcribe,113    inputs=[114        gr.Radio(list(model_ids.keys()), label="Step 2. Select your model⬇️", value="Fast"),115        gr.Audio(sources="microphone", type="filepath",label="Step 3. Record your audio, and click the submit button⬇️")116    ],117    outputs=mf_transcribe_output,118    flagging_mode="never",119)120 121with demo:122    with gr.Row():123        gr.Markdown("<div style='text-align: center;'><h2>Automated transcription of voice comments</h2></div>")124    with gr.Row():125        with gr.Column():126            dropdown = gr.Dropdown(choices=list(templates.keys()), label="Step 1. Select your report template⬇️")127            report_template = gr.TextArea()128            dropdown.change(fn=show_template, inputs=dropdown, outputs=report_template)129        with gr.Column():130            gr.TabbedInterface([file_transcribe, mf_transcribe], ["Audio File","Microphone"])131    with gr.Row():132        textbox_fill_template = gr.Textbox(label="Result", interactive=False) 133        button = gr.Button("Filling the Template")134         135        if file_transcribe_output != "":136            button.click(fill_template, inputs=[report_template, mf_transcribe_output], outputs=textbox_fill_template)137        elif mf_transcribe_output != "":138            button.click(fill_template, inputs=[report_template, file_transcribe_output], outputs=textbox_fill_template)139 140demo.queue().launch(ssr_mode=False)141 142