app.py

import gradio as gr
import whisper
from langchain_openai import ChatOpenAI
from langchain_openai import AzureChatOpenAI
from utils import RefineDataSummarizer
from utils import (
    prompt_template,
    refine_template,
    prompt_template_bullet_point,
    refine_prompt_template_bullet_point
)
import os


def get_prompt_examples():
    examples=[
        ["Regular Template: ", prompt_template, refine_template],
        ["Bullet Point Template: ", prompt_template_bullet_point, refine_prompt_template_bullet_point],
        ["Empty Template: ", '{text}', '{text}'],
    ]
    return examples


def convert_to_time_format(seconds_float):
    # Split the input into whole seconds and fractional part (milliseconds)
    seconds, milliseconds = divmod(seconds_float, 1)
    milliseconds = round(milliseconds * 1000)  # Convert fractional part to milliseconds

    # Convert the whole seconds into hours, minutes, and remaining seconds
    minutes, seconds = divmod(int(seconds), 60)
    hours, minutes = divmod(minutes, 60)

    # Format the time components into HH:MM:SS:OO
    time_format = f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}"
    
    return time_format


def time_stamped_text(transcript_result):
    text = ''
    for idx, segment in enumerate(transcript_result['segments']):
        start_stamp = segment["start"]
        end_stamp = segment["end"]
        sentence = segment["text"].strip()
        text += f"{idx + 1}\n"
        text += f"{convert_to_time_format(start_stamp)} --> {convert_to_time_format(end_stamp)}\n{sentence}\n\n"
    return text.strip()
    

def transcript(file_dir, model_type, time_stamp):
    model_dir = os.path.join('models', model_type)
    # model_dir = "E:\\Whisper\\" + model_type
    model = whisper.load_model(model_dir)
    result = model.transcribe(file_dir, language='English', task='transcribe')

    if time_stamp:
        text = time_stamped_text(result)
    else:
        lines = [s['text'] for s in result['segments']]
        text = ''
        for line in lines:
            text += f"{line}\n"
    text = text.strip()
    with open("Transcript.txt", 'w') as file:
        file.write(text)
    return [text, "Transcript.txt"]


def upload_file(file_paths):
    return file_paths


def summary(text, chunk_num, chunk_overlap, llm_type, prompt, refine_prompt):
    #if user_api == "Not Provided":
        # api_key = os.getenv("openai_api")
        #deployment_name = llm_type
    #else:
    #    api_key = user_api
    #api_key = api_key.strip()
    # llm = ChatOpenAI(temperature=1, openai_api_key=api_key, model_name=llm_type)

    os.environ["AZURE_OPENAI_API_KEY"] = os.getenv("azure_api")
    os.environ["AZURE_OPENAI_ENDPOINT"] =  os.getenv("azure_endpoint")
    openai_api_version=os.getenv("azure_api_version")
    deployment_name = llm_type

    llm = AzureChatOpenAI(
        openai_api_version=openai_api_version,
        azure_deployment=deployment_name
    )

    rds = RefineDataSummarizer(llm=llm, prompt_template=prompt, refine_template=refine_prompt)
    result = rds.get_summarization(text, chunk_num=chunk_num, chunk_overlap=chunk_overlap)
    text = result["output_text"]
    with open("Summary.txt", 'w') as file:
        file.write(text)
    return [text, "Summary.txt"]

with gr.Blocks() as demo:
    with gr.Row(equal_height=False):
        with gr.Column():
            file_output = gr.File()
            upload_button = gr.UploadButton("Click to Upload a File", file_types=["audio", "video"], file_count="single")
            upload_button.upload(upload_file, upload_button, file_output)
            model_type = gr.Dropdown(
                [
                    "tiny.en.pt", 
                    "tiny.pt", 
                    "small.en.pt", 
                    "small.pt",
                    "base.en.pt", 
                    "base.pt",
                    "medium.en.pt", 
                    "medium.pt",
                    "large-v1.pt", 
                    "large-v2.pt",], label="Model Type", value="medium.pt")
            time_stamp = gr.Checkbox(label="SRT Format", info="SRT format with timestamps")
            TranscriptButton = gr.Button("Transcript", variant="primary")
            transcript_text = gr.Textbox(placeholder="Transcript Result", label="Transcript")
            with gr.Accordion(open=False, label=["Download Transcript"]):
                transcript_file = gr.File()
        
        with gr.Column():
            with gr.Accordion(open=True, label=["summary settings"]):
                chunk_num = gr.Number(precision=0, minimum=1, maximum=9999, step=1, label="Chunk Number", value=1)
                chunk_overlap = gr.Number(precision=0, minimum=1, maximum=9999, step=1, label="Chunk Overlap", value=100)
                placeholder = gr.Textbox(visible=False)
                prompt = gr.Textbox(placeholder="summary prompt", label="Summary Template", lines=5, value=prompt_template)
                refine_prompt = gr.Textbox(placeholder="refine summary prompt", label="Refine Summary Template", lines=10, value=refine_template)
                with gr.Accordion(open=False, label=["Templates"]):
                    gr.Examples(
                            examples=get_prompt_examples(),
                            inputs=[placeholder, prompt, refine_prompt],
                            fn=None,
                            outputs=None,
                            cache_examples=False,
                            label="Prompt Template"
                    )       

            with gr.Accordion(open=False, label=["llm settings"]):
                # user_api = gr.Textbox(placeholder="If Empty, Use Default Key", label="Your API Key", value="Not Provided")
                # llm_type =  gr.Dropdown(
                #     [
                #     "gpt-3.5-turbo", 
                #     "gpt-3.5-turbo-16k", 
                #     "gpt-4-1106-preview"
                #     ], label="LLM Type", value="gpt-4-1106-preview")
                llm_type =  gr.Dropdown(
                    [
                        "gpt-4-32k",
                        "gpt-4",
                        "gpt-4-1106-preview",
                        "gpt-35-turbo", 
                        "gpt-35-turbo-16k"
                    ], label="LLM Type", value="gpt-4-1106-preview")
            SunmmaryButton = gr.Button("Summary", variant="primary")
            summary_text = gr.Textbox(placeholder="Summary Result", label="Summary")
            with gr.Accordion(open=False, label=["Download Summary"]):
                summary_file = gr.File()


    TranscriptButton.click(
        fn=transcript, 
        inputs=[
            file_output,
            model_type,
            time_stamp
        ],
        outputs=[transcript_text, transcript_file]
    )
    SunmmaryButton.click(
        fn=summary, 
        inputs=[
            transcript_text,
            chunk_num,
            chunk_overlap,
            #user_api,
            llm_type,
            prompt,
            refine_prompt
        ],
        outputs=[summary_text, summary_file]
    )

demo.launch()