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import torch
from diffusers import DiffusionPipeline
import gradio as gr
from PIL import Image as PILImage
import numpy as np
from diffusers.utils import export_to_gif
# Replace this with your new Hugging Face repository ID
huggingface_repo_id = "Nehal721/my-smart-kitchen-video-model"
# Model ko load karein (ek baar jab app shuru ho)
try:
pipe = DiffusionPipeline.from_pretrained(huggingface_repo_id, torch_dtype=torch.float16)
if torch.cuda.is_available():
pipe.to("cuda")
print("Model loaded on GPU.")
else:
pipe.to("cpu")
print("Model loaded on CPU.")
# xformers memory optimization (optional)
try:
if torch.cuda.is_available():
pipe.enable_xformers_memory_efficient_attention()
print("xFormers memory efficient attention enabled.")
except Exception as e:
print(f"xFormers not enabled: {e}")
except Exception as e:
print(f"Error loading model: {e}")
pipe = None
def generate_video_from_prompt(prompt):
if pipe is None:
return "Error: Model could not be loaded."
try:
# Video frames generate karein
video_frames = pipe(prompt, num_inference_steps=40, height=256, width=448, num_frames=24).frames
# Frames ko GIF mein convert karein
gif_path = "output.gif"
frames_for_gif = []
if isinstance(video_frames, np.ndarray) and video_frames.ndim == 5:
video_frames = video_frames.squeeze(0)
if isinstance(video_frames, np.ndarray):
for frame_np in video_frames:
if frame_np.dtype != np.uint8:
frame_np = (frame_np * 255).astype(np.uint8)
frames_for_gif.append(PILImage.fromarray(frame_np, 'RGB'))
if not frames_for_gif:
raise ValueError("No valid frames for GIF conversion.")
export_to_gif(frames_for_gif, gif_path)
return gif_path # GIF file ka path return karein
except Exception as e:
return f"An error occurred during video generation: {e}"
# Gradio app ka interface banayein
iface = gr.Interface(
fn=generate_video_from_prompt,
inputs="text",
outputs="image",
title="Smart Kitchen Video Generator",
description="Text prompts se cooking videos generate karein."
)
# App ko run karein
iface.launch(share=False)
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