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:
    
    gr.Markdown("""
    # Chat with NP <br>
    
    This is to make life of NPs easier. 
    Record post visit summary in natural language, press "transcribe audio", and then "prepare a report".
    """)

    
    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()

#demo.launch(share=True, auth=("username", "password"))

# 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()