app.py

import gradio as gr
import os
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer


HUGGINGFACE_API_KEY = os.environ["HUGGINGFACE_API_KEY"]


def transcript_audio(audio_file) -> str:
    model = "openai/whisper-large-v3"
    api = InferenceClient(model, token=HUGGINGFACE_API_KEY)
    text = api.automatic_speech_recognition(audio_file)
    return text


def summarize_text(text: str, bullet_points: int, conclusion: bool) -> str:
    llm_model = "meta-llama/Meta-Llama-3-70B-Instruct"
    api = InferenceClient(llm_model, token=HUGGINGFACE_API_KEY)
    tokenizer = AutoTokenizer.from_pretrained(llm_model, token=HUGGINGFACE_API_KEY)
    if conclusion:
        user_chat = f"Summarize the following text, into {bullet_points} bullet points and a conclusion:\n{text}"
    else:
        user_chat = (
            f"Summarize the following text, into {bullet_points} bullet points:\n{text}"
        )
    chat = [
        {"role": "system", "content": "You are a Meeting Summarizer AI. You well help summarize the text into bullet points and a conclusion. Please return a Markdown formatted text. Remember to give it a title."},
        {"role": "user", "content": user_chat},
    ]
    prompt = tokenizer.apply_chat_template(
        chat, tokenize=False, add_generation_prompt=True
    )
    
    summary = api.text_generation(prompt, max_new_tokens=250, do_sample=True)
    print(summary)
    return summary


def control(audio_file, text: str, bullet_points: int, conclusion: bool) -> str:
    if audio_file:
        text = transcript_audio(audio_file)
    summary = summarize_text(text, bullet_points, conclusion)
    return summary


# make a simeple interface, where the user can input a text and get a summary or input an audio file and get a transcript and a summary
iface = gr.Interface(
    fn=control,
    inputs=[
        gr.components.Audio(label="Audio file", type="filepath"),
        gr.components.Textbox(lines=5, label="Text"),
        gr.components.Slider(
            minimum=1, maximum=10, value=5, step=1, label="Number of bullet points"
        ),
        gr.components.Checkbox(label="Add conclusion"),
    ],
    outputs=gr.components.Markdown(label="Summary"),
)

iface.launch()