from transformers import pipeline
from datasets import load_dataset
import gradio as gr
import torch
from diffusers import DiffusionPipeline

pipe_ar = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
pipe_en = pipeline("text-generation", model="ismaelfaro/gpt2-poems.en")
pipe_image = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ar-en")

# Initialize text-to-speech models for Arabic and English
# Arabic: text-to-speech
synthesiser_arabic = pipeline("text-to-speech", model="MBZUAI/speecht5_tts_clartts_ar")
embeddings_dataset_arabic = load_dataset("herwoww/arabic_xvector_embeddings", split="validation")
speaker_embedding_arabic = torch.tensor(embeddings_dataset_arabic[105]["speaker_embeddings"]).unsqueeze(0)

# English: text-to-speech
synthesiser_english = pipeline("text-to-speech", model="microsoft/speecht5_tts")
embeddings_dataset_english = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding_english = torch.tensor(embeddings_dataset_english[7306]["xvector"]).unsqueeze(0)

# Generate poem based on language and convert it to audio and image
def generate_poem(selected_language, text):
    if selected_language == "English":
        poem = generate_poem_english(text) #retrun the generated poem from the generate_poem_english function
        sampling_rate, audio_data = text_to_speech_english(poem) #return the audio from the text_to_speech_english function
        image = generate_image_from_poem(text) #return the image from the generate_image_from_poem function
    elif selected_language == "Arabic":
        poem = generate_poem_arabic(text) #retrun the generated poem from the generate_poem_arabic function
        sampling_rate, audio_data = text_to_speech_arabic(poem) #return the audio from the text_to_speech_arabic function
        translated_text = translate_arabic_to_english(text) #return the translated poem from arabic to englsih, using translate_arabic_to_english function
        image = generate_image_from_poem(translated_text) #return the image from the generate_image_from_poem function

    return poem, (sampling_rate, audio_data), image

# Poem generation for Arabic
def generate_poem_arabic(text):
    generated_text = pipe_ar(text, do_sample=True, max_length=96, top_k=50, top_p=1.0, temperature=1.0, num_return_sequences=1,
                              no_repeat_ngram_size = 3, return_full_text=True)[0]["generated_text"]
    clean_text = generated_text.replace("-", "") #To get rid of the dashs generated by the model.
    return clean_text

# Poem generation for English
def generate_poem_english(text):
    generated_text = pipe_en(text, do_sample=True, max_length=50)[0]['generated_text']
    clean_text = generated_text.replace("-", "")  # Remove dashes generated by the model
    clean_text = clean_text.replace("\\n", " ")  # Replace newlines with a space
    return clean_text

# Text-to-speech conversion for Arabic
def text_to_speech_arabic(text):
    speech = synthesiser_arabic(text, forward_params={"speaker_embeddings": speaker_embedding_arabic})
    audio_data = speech["audio"]
    sampling_rate = speech["sampling_rate"]
    return (sampling_rate, audio_data)

# Text-to-speech conversion for English
def text_to_speech_english(text):
    speech = synthesiser_english(text, forward_params={"speaker_embeddings": speaker_embedding_english})
    audio_data = speech["audio"]
    sampling_rate = speech["sampling_rate"]
    return (sampling_rate, audio_data)

#Image Function
def generate_image_from_poem(poem_text):
    image = pipe_image(poem_text).images[0]
    return image

#Translation Function from Arabic to English
def translate_arabic_to_english(text):
    translated_text = pipe_translator(text)[0]['translation_text']
    return translated_text

custom_css = """
body {
    background-color: #f4f4f9;
    color: #333;
}
.gradio-container {
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    background-color: #fff;
}
label {
    color: #4A90E2;
    font-weight: bold;
}

input[type="text"],
textarea {
    border: 1px solid #4A90E2;
}
textarea {
    height: 150px;
}

button {
    background-color: #4A90E2;
    color: #fff;
    border-radius: 5px;
    cursor: pointer;
}
button:hover {
    background-color: #357ABD;
}

.dropdown {
    border: 1px solid #4A90E2;
    border-radius: 4px;
}

"""
#First parameter is for the dropdown menu, and the second parameter is for the starter of the poem
examples = [["English", "The night sky is filled with stars and dreams"]]


my_model = gr.Interface(
    fn=generate_poem,  #The primary function that will recives the inputs (language and the starter of the poem)
    inputs=[
        gr.Dropdown(["English", "Arabic"], label="Select Language"), #Dropdown menu to select the language, either "English" or "Arabic" for the poem
        gr.Textbox(label="Enter a sentence")], #Textbox where the user will input a sentence or phrase to generate the poem (starter of the peom)

    outputs=[
        gr.Textbox(label="Generated Poem", lines=10), # Textbox to display the generated poem
        gr.Audio(label="Generated Audio", type="numpy"), #Audio output for the generated poem
        gr.Image(label="Generated Image")], #Display an image generated from the starter of the peom

    examples=examples,  #Predefined examples to guide the user how to use the interface
    css=custom_css  #Applying CSS Custeom
)
my_model.launch()