app.py

import cv2
import numpy as np
import torch
import subprocess

text = input("Enter text to convert to video: ")

# Load pre-trained GPT-2 model
model = torch.hub.load('huggingface/transformers', 'gpt2', tokenizer='gpt2-medium')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

# Generate text tokens from the input text
input_ids = torch.tensor(model.tokenizer.encode(text)).unsqueeze(0).to(device)

# Generate text sequences from the model
with torch.no_grad():
    output_sequences = model.generate(input_ids=input_ids, max_length=1024, temperature=1.0)

# Convert text sequences to video frames
frames = []
for sequence in output_sequences:
    sequence = sequence.cpu().numpy().tolist()
    frame = np.zeros((1080, 1920, 3), dtype=np.uint8)
    for i in range(len(sequence)):
        color = (255, 255, 255)
        if sequence[i] == 0:
            break
        if sequence[i] == 50256: # <eos> token
            continue
        cv2.putText(frame, model.tokenizer.decode(sequence[i]), (50, (i+1)*70), cv2.FONT_HERSHEY_SIMPLEX, 2, color, 3)
    frames.append(frame)

# Save frames as video
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_writer = cv2.VideoWriter("output.mp4", fourcc, 25.0, (1920, 1080))
for frame in frames:
    video_writer.write(frame)
video_writer.release()

# Use FFmpeg to add audio to the video
subprocess.call(['ffmpeg', '-i', 'output.mp4', '-i', 'audio.mp3', '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental', '-map', '0:v:0', '-map', '1:a:0', '-shortest', 'final_output.mp4'])