app.py

import torch
import gradio as gr
from diffusers import DiffusionPipeline

# 🔍 Automatically detect GPU or CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32
print(f"✅ Running on device: {device}")

# ⚙️ Try to load model safely
model_id = "akhaliq/veo3.1-fast"  # replace with your own model if needed

try:
    with gr.Progress(track_tqdm=True):
        pipe = DiffusionPipeline.from_pretrained(
            model_id,
            torch_dtype=dtype,
        ).to(device)
    print("✅ Model loaded successfully!")

except Exception as e:
    print("❌ Error loading model:", e)
    pipe = None

# 🎬 Define your main function
def generate_video(image, prompt):
    if pipe is None:
        return "⚠️ Model could not be loaded. Please try again later."

    if image is None or prompt.strip() == "":
        return "⚠️ Please upload an image and enter a description."

    try:
        with gr.Progress(track_tqdm=True):
            result = pipe(image=image, prompt=prompt)
        video = result.get("video", None)
        if video:
            return video
        else:
            return "⚠️ No video was generated. Try a different prompt."
    except Exception as e:
        return f"❌ An error occurred while generating video: {str(e)}"

# 🖥️ Gradio interface
demo = gr.Interface(
    fn=generate_video,
    inputs=[
        gr.Image(type="pil", label="📸 Upload Image"),
        gr.Textbox(label="📝 Prompt", placeholder="Describe the animation you want..."),
    ],
    outputs=gr.Video(label="🎥 Generated Video"),
    title="✨ AI Image to Video Generator",
    description="Upload an image and let AI create an animated video for you. Works on CPU and GPU!",
    theme="gradio/soft",
    allow_flagging="never",
)

# 🚀 Launch safely
if __name__ == "__main__":
    demo.launch()