Create app.py

app.py

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+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()