app.py

from __future__ import annotations
import os
import io
import re
import time
import uuid
import torch
import cohere
import secrets
import requests
import fasttext
import replicate
import numpy as np
import gradio as gr
from PIL import Image
from groq import Groq
from TTS.api import TTS
from elevenlabs import save
from gradio.themes.base import Base
from elevenlabs.client import ElevenLabs
from huggingface_hub import hf_hub_download
from gradio.themes.utils import colors, fonts, sizes
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from prompt_examples import TEXT_CHAT_EXAMPLES, IMG_GEN_PROMPT_EXAMPLES, AUDIO_EXAMPLES, TEXT_CHAT_EXAMPLES_LABELS, IMG_GEN_PROMPT_EXAMPLES_LABELS, AUDIO_EXAMPLES_LABELS
from preambles import CHAT_PREAMBLE, AUDIO_RESPONSE_PREAMBLE, IMG_DESCRIPTION_PREAMBLE
from constants import LID_LANGUAGES, NEETS_AI_LANGID_MAP, AYA_MODEL_NAME, BATCH_SIZE, USE_ELVENLABS, USE_REPLICATE


HF_API_TOKEN =  os.getenv("HF_API_KEY")
ELEVEN_LABS_KEY = os.getenv("ELEVEN_LABS_KEY")
NEETS_AI_API_KEY = os.getenv("NEETS_AI_API_KEY")
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
IMG_COHERE_API_KEY = os.getenv("IMG_COHERE_API_KEY")
AUDIO_COHERE_API_KEY = os.getenv("AUDIO_COHERE_API_KEY")
CHAT_COHERE_API_KEY = os.getenv("CHAT_COHERE_API_KEY")

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Initialize cohere clients
img_prompt_client = cohere.Client(
    api_key=IMG_COHERE_API_KEY,
    client_name="c4ai-aya-expanse-img"
)
chat_client = cohere.Client(
    api_key=CHAT_COHERE_API_KEY,
    client_name="c4ai-aya-expanse-chat"
)
audio_response_client = cohere.Client(
    api_key=AUDIO_COHERE_API_KEY,
    client_name="c4ai-aya-expanse-audio"
)

# Initialize the Groq client
groq_client = Groq(api_key=GROQ_API_KEY)

# Initialize the ElevenLabs client
eleven_labs_client = ElevenLabs(
  api_key=ELEVEN_LABS_KEY, 
)

# Language identification 
lid_model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
LID_model = fasttext.load_model(lid_model_path)

def predict_language(text):
    text = re.sub("\n", " ", text)
    label, logit = LID_model.predict(text)
    label = label[0][len("__label__") :]
    print("predicted language:", label)
    return label

# Image Generation util functions
def get_hf_inference_api_response(payload, model_id):
    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
    MODEL_API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
    response = requests.post(MODEL_API_URL, headers=headers, json=payload)
    return response.content

def replicate_api_inference(input_prompt):
    input_params={
        "prompt": input_prompt,
        "go_fast": True,
        "megapixels": "1",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "jpg",
        "output_quality": 80,
        "enable_safety_checker": True,
        "safety_tolerance": 1,
        "num_inference_steps": 4
    }
    image = replicate.run("black-forest-labs/flux-schnell",input=input_params)
    image = Image.open(image[0])
    return image

def generate_image(input_prompt, model_id="black-forest-labs/FLUX.1-schnell"):
    if input_prompt:
        if USE_REPLICATE:
            print("using replicate for image generation")
            image = replicate_api_inference(input_prompt)
        else:
            try:
                print("using HF inference API for image generation")
                image_bytes = get_hf_inference_api_response({ "inputs": input_prompt}, model_id)
                image = np.array(Image.open(io.BytesIO(image_bytes)))
            except Exception as e:
                print("HF API error:", e)
                # generate image with help replicate in case of error
                image = replicate_api_inference(input_prompt)
        return image
    else:
        return None

def generate_img_prompt(input_prompt):
    if input_prompt:
        # clean prompt before doing language detection
        cleaned_prompt = clean_text(input_prompt, remove_bullets=True, remove_newline=True)
        text_lang_code = predict_language(cleaned_prompt)
        
        gr.Info("Generating Image", duration=2)
        
        if text_lang_code!="eng_Latn":
            text = f"""
            Translate the given input prompt to English.
            Input Prompt: {input_prompt}
            Then based on the English translation of the prompt, generate a detailed image description which can be used to generate an image using a text-to-image model.
            Do not use more than 3-4 lines for the image description. Respond with only the image description.
            """
        else:
            text = f"""Generate a detailed image description which can be used to generate an image using a text-to-image model based on the given input prompt:
            Input Prompt: {input_prompt}
            Do not use more than 3-4 lines for the description.
            """
        
        response = img_prompt_client.chat(message=text, preamble=IMG_DESCRIPTION_PREAMBLE, model=AYA_MODEL_NAME)
        output = response.text
    
        return output
    else:
        return None


# Chat with Aya util functions

def trigger_example(example):
    chat, updated_history = generate_AJ_chat_response(example)
    return chat, updated_history
        
def generate_aya_chat_response(user_message, cid, token, history=None):
    if not token:
        print("no token")
        #raise gr.Error("Error loading.")
        
    if history is None:
        history = []
    if cid == "" or None:    
        cid = str(uuid.uuid4())

    print(f"cid: {cid} prompt:{user_message}")
    
    history.append(user_message)
    
    stream = chat_client.chat_stream(message=user_message,  preamble=CHAT_PREAMBLE, conversation_id=cid, model=AYA_MODEL_NAME, connectors=[], temperature=0.3)
    output = ""
    
    for idx, response in enumerate(stream):
        if response.event_type == "text-generation":
            output += response.text
        if idx == 0:
            history.append(" " + output)
        else:
            history[-1] = output
        chat = [
            (history[i].strip(), history[i + 1].strip())
            for i in range(0, len(history) - 1, 2)
        ] 
        yield chat, history, cid
        
    return chat, history, cid
    

def clear_chat():
    return [], [], str(uuid.uuid4())

# Audio Pipeline util functions

def transcribe_and_stream(inputs, model_name="groq_whisper", show_info="show_info", language="english"):
    if inputs:
        if show_info=="show_info":
            gr.Info("Processing Audio", duration=1)
        if model_name != "groq_whisper":
            print("DEVICE:", DEVICE)
            pipe = pipeline(
            task="automatic-speech-recognition",
            model=model_name,
            chunk_length_s=30,
            DEVICE=DEVICE) 
            text = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps=True)["text"] 
        else:
            text = groq_whisper_tts(inputs)
        
        # stream text output
        for i in range(len(text)):
            time.sleep(0.01)
            yield text[: i + 10]
    else:
        return ""


def aya_speech_text_response(text):
    if text:
        stream = audio_response_client.chat_stream(message=text,preamble=AUDIO_RESPONSE_PREAMBLE, model=AYA_MODEL_NAME)
        output = ""

        for event in stream:
            if event:
                if event.event_type == "text-generation":
                    output+=event.text
                    cleaned_output = clean_text(output)
                    yield cleaned_output
    else:
        return ""

def clean_text(text, remove_bullets=False, remove_newline=False):
    # Remove bold formatting
    cleaned_text = re.sub(r"\*\*", "", text)

    if remove_bullets:
        cleaned_text = re.sub(r"^- ", "", cleaned_text, flags=re.MULTILINE)

    if remove_newline:
        cleaned_text = re.sub(r"\n", " ", cleaned_text)

    return cleaned_text

def convert_text_to_speech(text, language="english"):
    
    # do language detection to determine voice of speech response
    if text:
        # clean text before doing language detection
        cleaned_text = clean_text(text, remove_bullets=True, remove_newline=True)
        text_lang_code = predict_language(cleaned_text)

        if not USE_ELVENLABS:
            if text_lang_code!= "jpn_Jpan":
                audio_path = neetsai_tts(text, text_lang_code)
            else:
                print("DEVICE:", DEVICE)
                # if language is japanese then use XTTS for TTS since neets_ai doesn't support japanese voice
                tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(DEVICE)
                speaker_wav="samples/ja-sample.wav"
                lang_code="ja"
                audio_path = "./output.wav"
                tts.tts_to_file(text=text, speaker_wav=speaker_wav, language=lang_code, file_path=audio_path)
        else:
            # use elevenlabs for TTS 
            audio_path = elevenlabs_generate_audio(text)
            
        return audio_path
    else:
        return None

def elevenlabs_generate_audio(text):
    audio = eleven_labs_client.generate(
        text=text,
        voice="River",
        model="eleven_turbo_v2_5", #"eleven_multilingual_v2"
        )
    # save audio 
    audio_path = "./audio.mp3"
    save(audio, audio_path)
    return audio_path

def neetsai_tts(input_text, text_lang_code):

    if text_lang_code in LID_LANGUAGES.keys():
        language = LID_LANGUAGES[text_lang_code]
    else:
        # use english voice as default for languages outside 23 languages of AJ
        language = "english"

    neets_lang_id = NEETS_AI_LANGID_MAP[language]
    neets_vits_voice_id = f"vits-{neets_lang_id}"
    
    response = requests.request(
    method="POST",
    url="https://api.neets.ai/v1/tts",
    headers={
        "Content-Type": "application/json",
        "X-API-Key": NEETS_AI_API_KEY
    },
    json={
        "text": input_text,
        "voice_id": neets_vits_voice_id,
        "params": {
        "model": "vits"
        }
    }
    )
    # save audio file
    audio_path = "neets_demo.mp3"
    with open(audio_path, "wb") as f:
        f.write(response.content)
    return audio_path

def groq_whisper_tts(filename):
    with open(filename, "rb") as file:
        transcriptions = groq_client.audio.transcriptions.create(
        file=(filename, file.read()), 
        model="whisper-large-v3-turbo",
        response_format="json", 
        temperature=0.0 
        )
    print("transcribed text:", transcriptions.text)
    print("********************************")
    return transcriptions.text


# setup gradio app theme    
theme = gr.themes.Base(
    primary_hue=gr.themes.colors.teal, 
    secondary_hue=gr.themes.colors.blue,
    neutral_hue=gr.themes.colors.gray,
    text_size=gr.themes.sizes.text_lg,
).set(
    # Primary Button Color
    button_primary_background_fill="#114A56",
    button_primary_background_fill_hover="#114A56",
    # Block Labels
    block_title_text_weight="600",
    block_label_text_weight="600",
    block_label_text_size="*text_md",
)


demo = gr.Blocks(theme=theme, analytics_enabled=False)

with demo:
    with gr.Row(variant="panel"):
        with gr.Column(scale=1):
            gr.Image("AyaExpanse.png", elem_id="logo-img", show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False)
        with gr.Column(scale=30):
            gr.Markdown("""C4AI Aya Expanse is a state-of-art model with highly advanced capabilities to connect the world across languages. 
            <br/> 
            You can use this space to chat, speak and visualize with Aya Expanse in 23 languages.
            <br/> 
            **Model**: [aya-expanse-32B](https://huggingface.co/CohereForAI/aya-expanse-32b)
            <br/>
            **Developed by**: [Cohere for AI](https://cohere.com/research) and [Cohere](https://cohere.com/)
            <br/>
            **License**: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
            """
            )
    
    with gr.TabItem("Chat with Aya") as chat_with_AJ:
        cid = gr.State("")
        token = gr.State(value=None)

        with gr.Column():
            with gr.Row():
                chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, height=300)
            
            with gr.Row():
                user_message = gr.Textbox(lines=1, placeholder="Ask anything in our 23 languages ...", label="Input", show_label=False)

        
            with gr.Row():
                submit_button = gr.Button("Submit",variant="primary")
                clear_button = gr.Button("Clear")

                            
            history = gr.State([])
            
            user_message.submit(fn=generate_aya_chat_response, inputs=[user_message, cid, token, history], outputs=[chatbot, history, cid], concurrency_limit=32)
            submit_button.click(fn=generate_aya_chat_response, inputs=[user_message, cid, token, history], outputs=[chatbot, history, cid], concurrency_limit=32)
            
            clear_button.click(fn=clear_chat, inputs=None, outputs=[chatbot, history, cid], concurrency_limit=32)

            user_message.submit(lambda x: gr.update(value=""), None, [user_message], queue=False)
            submit_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)
            clear_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)

            with gr.Row():
                gr.Examples(
                    examples=TEXT_CHAT_EXAMPLES,
                    inputs=user_message,
                    cache_examples=False,
                    fn=trigger_example,
                    outputs=[chatbot],
                    examples_per_page=25,
                    label="Load example prompt for:",
                    example_labels=TEXT_CHAT_EXAMPLES_LABELS,
                )

    # End to End Testing Pipeline for speak with AJ  
    with gr.TabItem("Speak with Aya") as speak_with_aya:
        
        with gr.Row():
            with gr.Column():
                e2e_audio_file = gr.Audio(sources="microphone", type="filepath", min_length=None)
                e2_audio_submit_button = gr.Button(value="Get Aya's Response", variant="primary")
                
                clear_button_microphone = gr.ClearButton()
                gr.Examples(
                    examples=AUDIO_EXAMPLES,
                    inputs=e2e_audio_file,
                    cache_examples=False,
                    examples_per_page=25,
                    label="Load example audio for:",
                    example_labels=AUDIO_EXAMPLES_LABELS,
                )
                                    
            with gr.Column():
                e2e_audio_file_trans = gr.Textbox(lines=3,label="Your Input", autoscroll=False, show_copy_button=True, interactive=False)
                e2e_audio_file_aya_response = gr.Textbox(lines=3,label="Aya's Response", show_copy_button=True, container=True, interactive=False)
                e2e_aya_audio_response = gr.Audio(type="filepath", label="Aya's Audio Response")
            
        # show_info = gr.Textbox(value="show_info", visible=False)
        # stt_model = gr.Textbox(value="groq_whisper", visible=False)

        with gr.Accordion("See Details", open=False):
            gr.Markdown("To enable voice interaction with Aya Expanse, this space uses [Whisper large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) and [Groq](https://groq.com/) for STT and [neets.ai](http://neets.ai/) for TTS.")
                    

    # Generate Images
    with gr.TabItem("Visualize with AJ") as visualize_with_aya:
        with gr.Row():
            with gr.Column():
                input_img_prompt = gr.Textbox(placeholder="Ask anything in our 23 languages ...", label="Describe an image", lines=3)
                # generated_img_desc = gr.Textbox(label="Image Description generated by Aya", interactive=False, lines=3, visible=False) 
                submit_button_img = gr.Button(value="Submit", variant="primary")
                clear_button_img = gr.ClearButton()
                
            
            with gr.Column():
                generated_img = gr.Image(label="Generated Image", interactive=False)

        with gr.Row():
            gr.Examples(
                examples=IMG_GEN_PROMPT_EXAMPLES,
                inputs=input_img_prompt,
                cache_examples=False,
                examples_per_page=25,
                label="Load example prompt for:",
                example_labels=IMG_GEN_PROMPT_EXAMPLES_LABELS
            )
            generated_img_desc = gr.Textbox(label="Image Description generated by Aya", interactive=False, lines=3, visible=False)
        
        # increase spacing between examples and Accordion components
        with gr.Row():
            pass
        with gr.Row():
            pass
        with gr.Row():
            pass

        with gr.Row():
            with gr.Accordion("See Details", open=False):
                gr.Markdown("This space uses AJ.Chat for translating multilingual prompts and generating detailed image descriptions and [Flux Schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) for Image Generation.")
    
    # Image Generation
    clear_button_img.click(lambda: None, None, input_img_prompt)
    clear_button_img.click(lambda: None, None, generated_img_desc)
    clear_button_img.click(lambda: None, None, generated_img)

    submit_button_img.click(
        generate_img_prompt,
        inputs=[input_img_prompt],
        outputs=[generated_img_desc],
    )

    generated_img_desc.change(
        generate_image, #run_flux,
        inputs=[generated_img_desc],
        outputs=[generated_img],
        show_progress="full",
    )

   # Audio Pipeline
    clear_button_microphone.click(lambda: None, None, e2e_audio_file)
    clear_button_microphone.click(lambda: None, None, e2e_aya_audio_response)
    clear_button_microphone.click(lambda: None, None, e2e_audio_file_aya_response)
    clear_button_microphone.click(lambda: None, None, e2e_audio_file_trans)

    #e2e_audio_file.change(
    e2_audio_submit_button.click(
        transcribe_and_stream,
        inputs=[e2e_audio_file],
        outputs=[e2e_audio_file_trans],
        show_progress="full",
    ).then(
        aya_speech_text_response,
        inputs=[e2e_audio_file_trans],
        outputs=[e2e_audio_file_aya_response],
        show_progress="full",
    ).then(
        convert_text_to_speech,
        inputs=[e2e_audio_file_aya_response],
        outputs=[e2e_aya_audio_response],
        show_progress="full",
    )
    
    demo.load(lambda: secrets.token_hex(16), None, token)

demo.queue(api_open=False, max_size=20, default_concurrency_limit=4).launch(show_api=False, allowed_paths=['/home/user/app'])