import gradio as gr
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
import uuid

# Model Yükleme
model_id = "vdo/zeroscope_v2_576w"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cpu")

def generate_video(prompt):
    try:
        # Kaliteyi koruyalım (Patronun istediği gibi net olsun)
        # num_inference_steps=20 (Dengeli kalite)
        frames = pipe(
            prompt, 
            num_inference_steps=20, 
            height=320, 
            width=576, 
            num_frames=16
        ).frames
        
        output_filename = f"viral_{uuid.uuid4()}.mp4"
        export_to_video(frames[0], output_filename, fps=8)
        
        # SADECE dosya adını döndür, Gradio bunu otomatik URL'ye çevirir
        return output_filename
    except Exception as e:
        print(f"HATA: {str(e)}")
        return None

# API İsmi: predict
demo = gr.Interface(fn=generate_video, inputs="text", outputs="video", api_name="predict")
demo.launch()