Create app.py

app.py

@@ -0,0 +1,62 @@
+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
+
+
+model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
+
+interpolator = interpolator.Interpolator(model, None)
+
+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]))
+  hsize = int((float(img.size[1])*float(wpercent)))
+  img = img.resize((basewidth,hsize), Image.ANTIALIAS)
+  return img
+  
+
+def resize_img(img1,img2):
+ img_target_size = Image.open(img1)
+ img_to_resize = resize_and_crop(
+     img2, 
+     (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):
+   
+    frame1 = resize(780,frame1)
+    frame2 = resize(780,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, interpolator))
+
+    mediapy.write_video("out.mp4", frames, fps=30)
+    return "out.mp4"
+
+title="frame-interpolation"
+examples=[['cat3.jpeg','cat4.jpeg',2]]
+gr.Interface(predict,[gr.inputs.Image(type='filepath'),gr.inputs.Image(type='filepath'),gr.inputs.Slider(minimum=2,maximum=8,step=1)],"playable_video",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)