Create app.py

app.py

@@ -0,0 +1,42 @@
+import os
+import numpy as np
+import tensorflow as tf
+import mediapy
+from PIL import Image
+from eval import interpolator, util
+import tensorflow as tf
+import gradio as gr
+
+
+_UINT8_MAX_F = float(np.iinfo(np.uint8).max)
+
+from huggingface_hub import snapshot_download
+model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
+
+interpolator = interpolator.Interpolator(model, None)
+
+batch_dt = np.full(shape=(1,), fill_value=0.5, dtype=np.float32)
+
+def predict(frame1, frame2, times_to_interpolate):
+    img1 = frame1
+    img2 = frame2
+    if not img1.size == img2.size:
+        img1 = img1.crop((0, 0, min(img1.size[0], img2.size[0]), min(img1.size[1], img2.size[1])))
+        img2 = img2.crop((0, 0, min(img1.size[0], img2.size[0]), min(img1.size[1], img2.size[1])))
+        frame1 = 'new_frame1.png'
+        frame2 = 'new_frame2.png'
+        img1.save(frame1)
+        img2.save(frame2)
+
+    input_frames = [str(frame1), str(frame2)]
+
+    frames = list(
+        util.interpolate_recursively_from_files(
+            input_frames, times_to_interpolate, interpolator))
+    ffmpeg_path = util.get_ffmpeg_path()
+    mediapy.set_ffmpeg(ffmpeg_path)
+    out_path =  "out.mp4"
+    mediapy.write_video(str(out_path), frames, fps=30)
+    return out_path
+
+gr.Interface(predict,[gr.inputs.Image(type='pil'),gr.inputs.Image(type='pil'),gr.inputs.Slider(minimum=2,maximum=5,step=1)],"playable_video").launch(enable_queue=True)