import os
import sys
import numpy as np
import tensorflow as tf
import mediapy
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download

# Clone the repository and add the path
os.system("git clone https://github.com/google-research/frame-interpolation")
sys.path.append("frame-interpolation")

# Import after appending the path
from eval import interpolator, util

def load_model(model_name):
    model = interpolator.Interpolator(snapshot_download(repo_id=model_name), None)
    return model

model_names = [
    "akhaliq/frame-interpolation-film-style",
    "NimaBoscarino/frame-interpolation_film_l1",
    "NimaBoscarino/frame_interpolation_film_vgg",
]

models = {model_name: load_model(model_name) for model_name in model_names}

ffmpeg_path = util.get_ffmpeg_path()
mediapy.set_ffmpeg(ffmpeg_path)

def resize(width, img):
    img = Image.fromarray(img)
    wpercent = (width / float(img.size[0]))
    hsize = int((float(img.size[1]) * float(wpercent)))
    img = img.resize((width, hsize), Image.LANCZOS)
    return img

def resize_and_crop(img_path, size, crop_origin="middle"):
    img = Image.open(img_path)
    img = img.resize(size, Image.LANCZOS)
    return img

def resize_img(img1, img2_path):
    img_target_size = Image.open(img1)
    img_to_resize = resize_and_crop(
        img2_path,
        (img_target_size.size[0], img_target_size.size[1]),  # set width and height to match img1
        crop_origin="middle"
    )
    img_to_resize.save('resized_img2.png')

def predict(frame1, frame2, times_to_interpolate, model_name):
    model = models[model_name]

    frame1 = resize(1080, frame1)
    frame2 = resize(1080, frame2)

    frame1.save("test1.png")
    frame2.save("test2.png")

    resize_img("test1.png", "test2.png")
    input_frames = ["test1.png", "resized_img2.png"]

    frames = list(
        util.interpolate_recursively_from_files(
            input_frames, times_to_interpolate, model))

    mediapy.write_video("out.mp4", frames, fps=30)
    return "out.mp4"

title = "Sports model"
description = "Wechat:Liesle1"
article = ""
examples = [
    ['cat3.jpeg', 'cat4.jpeg', 2, model_names[0]],
    ['cat1.jpeg', 'cat2.jpeg', 2, model_names[1]],
]

gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(label="First Frame"),
        gr.Image(label="Second Frame"),
        gr.Number(label="Times to Interpolate", value=2),
        gr.Dropdown(label="Model", choices=model_names),
    ],
    outputs=gr.Video(label="Interpolated Frames"),
    title=title,
    description=description,
    article=article,
    examples=examples,
).launch()