requirements.txt

gradio
huggingface_hub

app.py

@@ -1,55 +1,36 @@
 import gradio as gr
-import cv2
-import numpy as np
 import os
+from huggingface_hub import InferenceClient
 
-os.makedirs("outputs", exist_ok=True)
-
-def motion_transfer(image, video):
-
-    if image is None or video is None:
-        return None
+client = InferenceClient("akhaliq/animate-anyone")
 
-    img = cv2.imread(image)
-    cap = cv2.VideoCapture(video)
-
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    fps = cap.get(cv2.CAP_PROP_FPS)
+os.makedirs("outputs", exist_ok=True)
 
-    img = cv2.resize(img, (width, height))
+def animate(image, video):
 
     output_path = "outputs/result.mp4"
 
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-
-    while True:
-
-        ret, frame = cap.read()
-
-        if not ret:
-            break
-
-        blended = cv2.addWeighted(img, 0.7, frame, 0.3, 0)
-
-        out.write(blended)
+    result = client.text_to_video(
+        prompt="person moving naturally",
+        image=image,
+        video=video
+    )
 
-    cap.release()
-    out.release()
+    with open(output_path, "wb") as f:
+        f.write(result)
 
     return output_path
 
 
 app = gr.Interface(
-    fn=motion_transfer,
+    fn=animate,
     inputs=[
         gr.Image(type="filepath", label="Upload Image"),
-        gr.Video(label="Upload Motion Video")
+        gr.Video(label="Upload Motion Reference Video")
     ],
-    outputs=gr.Video(label="Generated Video"),
-    title="Motion Control AI FREE",
-    description="Upload image and motion video"
+    outputs=gr.Video(label="Generated Motion Video"),
+    title="Motion Control AI",
+    description="Animate image using motion reference video"
 )
 
 app.launch()