import gradio as gr
import os
import time
from moviepy.editor import *
from share_btn import community_icon_html, loading_icon_html, share_js

token = os.environ.get('HF_TOKEN')
caption = gr.Blocks.load(name="spaces/SRDdev/Image-Caption")
audio_gen = gr.Blocks.load(name="spaces/fffiloni/audioldm-text-to-audio-generation-clone", api_key=token)

ph_message="If you're not happy with sound result, you can manually describe the scene depicted in your image :)"
def infer(image_input, manual_caption, duration_in):
    print(duration_in)
    if manual_caption == "":
        cap = caption(image_input, fn_index=0)
        print("gpt2 caption: " + cap)
        ph_update = "GP2 Caption: " + cap
    else:
        cap = manual_caption
        print("manual captiony: " + cap)
        ph_update=""
    sound = audio_gen(cap, duration_in, 2.5, 45, 3, fn_index=0)
    
    return cap, sound[1], gr.Textbox.update(placeholder=f"{ph_update} {ph_message}"), gr.Group.update(visible=True)

title = """
    <div style="text-align: center; max-width: 700px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
            Image to Sound Effect
        </h1>
        </div>
        <p style="margin-bottom: 10px; font-size: 94%">
        Convert an image to a corresponding sound effect generated through GPT2 Image Captioning & AudioLDM
        </p>
    </div>
"""

article = """
    
    <div class="footer">
        <p>
         
        Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗
        </p>
    </div>

    <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;">
        <p>You may also like: </p>
        
        <div id="may-like-content" style="display:flex;flex-wrap: wrap;align-items:center;height:20px;">
            
            <svg height="20" width="208" style="margin-left:4px;margin-bottom: 6px;">       
                 <a href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation" target="_blank">
                    <image href="https://img.shields.io/badge/🤗 Spaces-AudioLDM_Text_to_Audio-blue" src="https://img.shields.io/badge/🤗 Spaces-AudioLDM_Text_to_Audio-blue.png" height="20"/>
                 </a>
            </svg>

            <svg height="20" width="122" style="margin-left:4px;margin-bottom: 6px;">       
                 <a href="https://huggingface.co/spaces/fffiloni/spectrogram-to-music" target="_blank">
                    <image href="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue" src="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue.png" height="20"/>
                 </a>
            </svg>
            
        </div>
    </div>
"""

with gr.Blocks(css="style.css") as demo:
    with gr.Column(elem_id="col-container"):
        
        gr.HTML(title)
    
        input_img = gr.Image(type="filepath", elem_id="input-img")
        manual_cap = gr.Textbox(label="Manual Image description (optional)", lines=2, placeholder=ph_message)
        duration_in = gr.Slider(minimum=5, maximum=30, step=5, value=10, label="Duration")
        caption_output = gr.Textbox(label="Caption", visible=False, elem_id="text-caption")
        sound_output = gr.Audio(label="Result", elem_id="sound-output")
        
        generate = gr.Button("Generate SFX from Image")

        with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
            community_icon = gr.HTML(community_icon_html)
            loading_icon = gr.HTML(loading_icon_html)
            share_button = gr.Button("Share to community", elem_id="share-btn")

        gr.HTML(article)
        
    generate.click(infer, inputs=[input_img, manual_cap, duration_in], outputs=[caption_output, sound_output, manual_cap, share_group], api_name="i2fx")
    share_button.click(None, [], [], _js=share_js)

demo.queue(max_size=32).launch(debug=True)