app.py

import os
import openai
import torch 

import gradio as gr
import pytube as pt
from transformers import pipeline
from huggingface_hub import model_info

openai.api_key = os.getenv('OPEN_AI_KEY')
hf_t_key = ('HF_TOKEN_KEY')

MODEL_NAME = "openai/whisper-small" 
lang = "en"

device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")

def transcribe(microphone, file_upload):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "WARNING: You've uploaded a recorded audio file . "
            "The recorded file from the microphone uploaded, transcribed and immediately discarded.\n"
        )

    elif (microphone is None) and (file_upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    file = microphone if microphone is not None else file_upload

    text = pipe(file)["text"]

    return warn_output + text


def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str


def yt_transcribe(yt_url):
    yt = pt.YouTube(yt_url)
    html_embed_str = _return_yt_html_embed(yt_url)
    stream = yt.streams.filter(only_audio=True)[0]
    stream.download(filename="audio.mp3")

    text = pipe("audio.mp3")["text"]

    return html_embed_str, text


def predict(message, history):
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": message})

    response = openai.ChatCompletion.create(
        model= 'ft:gpt-3.5-turbo-0125:2292030-peach-tech:colleague-ai:9Ag6BysG',
        messages= history_openai_format,
        temperature=1.0,
        stream=True
    )

    partial_message = ""
    for chunk in response:
        if len(chunk['choices'][0]['delta']) != 0:
            partial_message = partial_message + chunk['choices'][0]['delta']['content']
            yield partial_message

A1 = gr.ChatInterface(predict,
                title="COLLEAGUE",
                description="An All-In-One AI Productivity Suite By Peach State Innovation and Technology. Select The Corresponding Tab For Accessibility",
                textbox=gr.Textbox(placeholder="Enter your question/prompt here..."),
                theme= gr.themes.Glass(primary_hue="neutral", neutral_hue="slate"),
                retry_btn=None,
                clear_btn="Clear Conversation")


A3 = gr.load(
             "models/Salesforce/blip-image-captioning-large",
              title=" ",
              description="Upload Any Type of Imagery (photos, medical imagery, etc.), I'll Give You Its Description",
              outputs=[gr.Textbox(label="I see...")],
              theme= gr.themes.Glass(primary_hue="neutral", neutral_hue="slate"))

A4 = gr.load(
             "models/stabilityai/stable-diffusion-xl-base-1.0",
              inputs=[gr.Textbox(label="Enter Your Image Description")],
              outputs=[gr.Image(label="Image")],
              title=" ",
              description="Bring Your Imagination Into Existence, Create Unique Images With COLLEAGUE",
              allow_flagging="never", 
              examples=["A gigantic celtic leprechaun wandering the streets of downtown Atlanta","A child eating pizza in a Brazilian favela"])

A5 = gr.HTML(
            value=("""
                    <iframe
	                src="https://peachtechai-colleague-scribe.hf.space"
	                frameborder="0"
	                width="1245"
	                height="1450"
                    ></iframe>"""),
                 )

A6 = gr.load(
             "models/Falconsai/text_summarization",
             title="",
             description="Enter Text From Documents (from paragraphs to pages) and Instantly Create A Brief Summarization.",
             theme= gr.themes.Glass(primary_hue="neutral", neutral_hue="slate"))
    
mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Microphone(type="filepath"),
        gr.Audio(type="filepath"),
    ],
    outputs="text",
    title=" ",
    description=(
        "Transcribe real-time speech and audio files of any length at the click of a button."
    ),
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[gr.Textbox(lines=1, placeholder="Paste your YouTube video URL/web address here", label="YouTube Video URL")],
    outputs=["html", "text"],
    title=" ",
    description=(
        "Short on Time? Get The Core Details and Transcribe YouTube videos at the click of a button."
      
    ),
    allow_flagging="never",
)

clp = gr.TabbedInterface([A1, A5, A6, mf_transcribe, yt_transcribe, A3, A4], ["Chat", "Write", "Summarize", "Audio Transcription", "Video Transcription", "Describe", "Create"], theme= gr.themes.Glass(primary_hue="neutral", neutral_hue="slate"))
clp.queue().launch()