Update app.py

app.py

@@ -1,11 +1,12 @@
 import torch
 from diffusers import StableVideoDiffusionPipeline
 from PIL import Image
+import gradio as gr
 import os
 
-HF_TOKEN = os.getenv("HF_TOKEN")  # ← SAFER (use HF Space secrets)
+HF_TOKEN = None  # Uses your Space token automatically
 
-# 1. Load pipeline
+# Load pipeline once at startup
 pipe = StableVideoDiffusionPipeline.from_pretrained(
     "stabilityai/stable-video-diffusion-img2vid",
     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
@@ -16,20 +17,45 @@ pipe = StableVideoDiffusionPipeline.from_pretrained(
 device = "cuda" if torch.cuda.is_available() else "cpu"
 pipe = pipe.to(device)
 
-# === 2. Load local input image ===
-input_path = "input.jpg"   # ← Put your image in the same folder as script
-img = Image.open(input_path).convert("RGB")
-img = img.resize((576, 320))
 
-# 3. Generate video frames
-frames = pipe(img, num_frames=5).frames[0]
+def generate_video(inp_img, num_frames):
+    if inp_img is None:
+        return "No image uploaded!", None
 
-# 4. Save frames
-os.makedirs("frames", exist_ok=True)
-for i, f in enumerate(frames):
-    f.save(f"frames/frame_{i:03d}.png")
+    # Resize image to SVD expected size
+    img = inp_img.convert("RGB").resize((576, 320))
 
-# 5. Build MP4 video
-os.system("ffmpeg -y -framerate 10 -i frames/frame_%03d.png output.mp4")
+    # Generate frames
+    frames = pipe(img, num_frames=num_frames).frames[0]
 
-print("Video saved as output.mp4")
+    # Save frames to video
+    os.makedirs("frames", exist_ok=True)
+    for i, f in enumerate(frames):
+        f.save(f"frames/frame_{i:03d}.png")
+
+    # Output video filename
+    out_path = "output.mp4"
+
+    # Build MP4 video
+    os.system(f"ffmpeg -y -framerate 10 -i frames/frame_%03d.png {out_path}")
+
+    return out_path
+
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🐱 AI Image → Video Generator (SVD)")
+    gr.Markdown("Upload an image and generate a short AI video using **Stable Video Diffusion img2vid**.")
+
+    with gr.Row():
+        inp_img = gr.Image(type="pil", label="Upload an input image")
+        num_frames = gr.Slider(4, 24, value=8, step=1, label="Number of Frames")
+
+    btn = gr.Button("Generate Video")
+
+    out_vid = gr.Video(label="Generated Video")
+
+    btn.click(generate_video, inputs=[inp_img, num_frames], outputs=out_vid)
+
+
+demo.launch()