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import gradio as gr
import torch
from transformers import pipeline
from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
from diffusers.utils import export_to_gif
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from gtts import gTTS
from moviepy.editor import VideoFileClip, AudioFileClip, concatenate_videoclips
import os
# Load the text generation model
generator = pipeline('text-generation', model='distilgpt2')
def generate_text(prompt):
response = generator(prompt, max_length=150, num_return_sequences=1)
return response[0]['generated_text']
def text_to_speech(text, filename='output_audio.mp3'):
tts = gTTS(text)
tts.save(filename)
return filename
def create_animation(prompt, output_file='animation.gif'):
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32
step = 4
repo = "ByteDance/AnimateDiff-Lightning"
ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
base = "emilianJR/epiCRealism"
adapter = MotionAdapter().to(device, dtype)
adapter.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step)
export_to_gif(output.frames[0], output_file)
return output_file
def create_video(animation_file, audio_file, output_file='output_video.mp4'):
clip = VideoFileClip(animation_file)
audio = AudioFileClip(audio_file)
clip = clip.set_audio(audio)
clip.write_videofile(output_file, fps=24)
return output_file
def generate_educational_video(prompt):
generated_text = generate_text(prompt)
audio_file = text_to_speech(generated_text)
animation_file = create_animation(prompt)
video_file = create_video(animation_file, audio_file)
return video_file
# Define Gradio Interface
def gradio_interface(prompt):
video_path = generate_educational_video(prompt)
return video_path
interface = gr.Interface(
fn=gradio_interface,
inputs="text",
outputs="video",
title="Educational Video Generator",
description="Enter a prompt to generate a video."
)
interface.launch()
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