Update app.py

app.py

@@ -1,27 +1,23 @@
 import os
-
-os.system("git clone https://github.com/google-research/frame-interpolation")
 import sys
-
-sys.path.append("frame-interpolation")
 import numpy as np
 import tensorflow as tf
 import mediapy
 from PIL import Image
-from eval import interpolator, util
 import gradio as gr
-
 from huggingface_hub import snapshot_download
 
-from image_tools.sizes import resize_and_crop
+# 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",
@@ -33,26 +29,27 @@ 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):
-    basewidth = width
-    img = Image.open(img)
-    wpercent = (basewidth / float(img.size[0]))
+    img = Image.fromarray(img)
+    wpercent = (width / float(img.size[0]))
     hsize = int((float(img.size[1]) * float(wpercent)))
-    img = img.resize((basewidth, hsize), Image.LANCZOS)
+    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):
+def resize_img(img1, img2_path):
     img_target_size = Image.open(img1)
     img_to_resize = resize_and_crop(
-        img2,
+        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]
 
@@ -72,7 +69,6 @@ def predict(frame1, frame2, times_to_interpolate, model_name):
     mediapy.write_video("out.mp4", frames, fps=30)
     return "out.mp4"
 
-
 title = "frame-interpolation"
 description = "Gradio demo for FILM: Frame Interpolation for Large Scene Motion. To use it, simply upload your images and add the times to interpolate number or click on one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'><a href='https://film-net.github.io/' target='_blank'>FILM: Frame Interpolation for Large Motion</a> | <a href='https://github.com/google-research/frame-interpolation' target='_blank'>Github Repo</a></p>"
@@ -82,16 +78,16 @@ examples = [
 ]
 
 gr.Interface(
-    predict,
-    [
-        gr.inputs.Image(type='filepath'),
-        gr.inputs.Image(type='filepath'),
-        gr.inputs.Slider(minimum=2, maximum=4, step=1),
-        gr.inputs.Dropdown(choices=model_names, default=model_names[0])
+    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),
     ],
-    "playable_video",
+    outputs=gr.Video(label="Interpolated Frames"),
     title=title,
     description=description,
     article=article,
-    examples=examples
-).launch(enable_queue=True)
+    examples=examples,
+).launch()