!pip -q install gradio==4.36.1
!pip -q install transformers==4.41.2

# the usual shorthand is to import gradio as gr
import gradio as gr

def greet(name: str, intensity: int) -> str:
    return "Hello, " + name + "!" * int(intensity)

>>> print(greet("Diego", 3))

demo = gr.Interface(
    fn=greet,
    inputs=["text", "slider"],  # the inputs are a text box and a slider ("text" and "slider" are components in Gradio)
    outputs=["text"],  # the output is a text box
)

# the launch method will fire up the interface we just created
demo.launch()

import os
import tempfile

import torch
import gradio as gr
from transformers import pipeline

device = 0 if torch.cuda.is_available() else "cpu"

AUDIO_MODEL_NAME = (
    "distil-whisper/distil-large-v3"  # faster and very close in performance to the full-size "openai/whisper-large-v3"
)
BATCH_SIZE = 8


pipe = pipeline(
    task="automatic-speech-recognition",
    model=AUDIO_MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


def transcribe(audio_input):
    """Function to convert audio to text."""
    if audio_input is None:
        raise gr.Error("No audio file submitted!")

    output = pipe(audio_input, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, return_timestamps=True)
    return output["text"]

part_1_demo = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"),  # "filepath" passes a str path to a temporary file containing the audio
    outputs=gr.Textbox(show_copy_button=True),  # give users the option to copy the results
    title="Transcribe Audio to Text",  # give our demo a title :)
)

part_1_demo.launch()

from huggingface_hub import notebook_login, InferenceClient

# running this will prompt you to enter your Hugging Face credentials
notebook_login()

# sample meeting transcript from huuuyeah/MeetingBank_Audio
# this is just a copy-paste from the output of part 1 using one of the Denver meetings
sample_transcript = """
 Good evening. Welcome to the Denver City Council meeting of Monday, May 8, 2017. My name is Kelly Velez. I'm your Council Secretary. According to our rules of procedure, when our Council President, Albus Brooks, and Council President Pro Tem, JoLynn Clark, are both absent, the Council Secretary calls the meeting to order. Please rise and join Councilman Herndon in the Pledge of Allegiance. Madam Secretary, roll call. Roll call. Here. Mark. Espinosa. Here. Platt. Delmar. Here. Here. Here. Here. We have five members present. There is not a quorum this evening. Many of the council members are participating in an urban exploration trip in Portland, Oregon, pursuant to Section 3.3.4 of the city charter. Because there is not a quorum of seven council members present, all of tonight's business will move to next week, to Monday, May 15th. Seeing no other business before this body except to wish Councilwoman Keniche a very happy birthday this meeting is adjourned Thank you. A standard model and an energy efficient model likely will be returned to you in energy savings many times during its lifespan. Now, what size do you need? Air conditioners are not a one-size-or-type fits all. Before you buy an air conditioner, you need to consider the size of your home and the cost to operate the unit per hour. Do you want a room air conditioner, which costs less but cools a smaller area, or do you want a central air conditioner, which cools your entire house but costs more? Do your homework. Now, let's discuss evaporative coolers. In low humidity areas, evaporating water into the air provides a natural and energy efficient means of cooling. Evaporative coolers, also called swamp coolers, cool outdoor air by passing it over water saturated pads, causing the water to evaporate into it. Evaporative coolers cost about one half as much to install as central air conditioners and use about one-quarter as much energy. However, they require more frequent maintenance than refrigerated air conditioners, and they're suitable only for areas with low humidity. Watch the maintenance tips at the end of this segment to learn more. And finally, fans. When air moves around in your home, it creates a wind chill effect. A mere two-mile-an-hour breeze will make your home feel four degrees cooler and therefore you can set your thermostat a bit higher. Ceiling fans and portable oscillating fans are cheap to run and they make your house feel cooler. You can also install a whole house fan to draw the hot air out of your home. A whole house fan draws cool outdoor air inside through open windows and exhausts hot room air through the attic to the outside. The result is excellent ventilation, lower indoor temperatures, and improved evaporative cooling. But remember, there are many low-cost, no-cost ways that you can keep your home cool. You should focus on these long before you turn on your AC or even before you purchase an AC. But if you are going to purchase a new cooling system, remember to get one that's energy efficient and the correct size for your home. Wait, wait, don't go away, there's more. After this segment of the presentation is over, you're going to be given the option to view maintenance tips about air conditioners and evaporative coolers. Now all of these tips are brought to you by the people at Xcel Energy. Thanks for watching.
"""

from huggingface_hub import InferenceClient

TEXT_MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"

client = InferenceClient()


def organize_text(meeting_transcript):
    messages = build_messages(meeting_transcript)
    response = client.chat_completion(messages, model=TEXT_MODEL_NAME, max_tokens=250, seed=430)
    return response.choices[0].message.content


def build_messages(meeting_transcript) -> list:
    system_input = "You are an assitant that organizes meeting minutes."
    user_input = """Take this raw meeting transcript and return an organized version.
    Here is the transcript:
    {meeting_transcript}
    """.format(
        meeting_transcript=meeting_transcript
    )

    messages = [
        {"role": "system", "content": system_input},
        {"role": "user", "content": user_input},
    ]
    return messages

part_2_demo = gr.Interface(
    fn=organize_text,
    inputs=gr.Textbox(value=sample_transcript),
    outputs=gr.Textbox(show_copy_button=True),
    title="Clean Up Transcript Text",
)
part_2_demo.launch()

def meeting_transcript_tool(audio_input):
    meeting_text = transcribe(audio_input)
    organized_text = organize_text(meeting_text)
    return organized_text


full_demo = gr.Interface(
    fn=meeting_transcript_tool,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.Textbox(show_copy_button=True),
    title="The Complete Meeting Transcription Tool",
)
full_demo.launch()

!pip install gradio_client

>>> from gradio_client import Client, handle_file

>>> client = Client("dmaniloff/meeting-transcript-tool")
>>> result = client.predict(
...     audio_input=handle_file("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav"),
...     api_name="/predict",
... )
>>> print(result)

 demo.launch(share=True)

>>> !cat flagged/log.csv

demo_with_custom_flagging = gr.Interface(
    fn=greet,
    inputs=["text", "slider"],  # the inputs are a text box and a slider ("text" and "slider" are components in Gradio)
    outputs=["text"],  # the output is a text box
    allow_flagging="manual",
    flagging_options=["Incorrect", "Ambiguous"],
)
demo_with_custom_flagging.launch()

>>> !cat flagged/log.csv