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| from diffusers import DiffusionPipeline | |
| from torchvision.transforms.functional import to_tensor | |
| import torch | |
| import matplotlib.pyplot as plt | |
| from torchvision.transforms.functional import to_pil_image | |
| import gradio as gr | |
| from torchvision.utils import save_image | |
| from PIL import Image | |
| import os | |
| from gtts import gTTS | |
| import torch | |
| import gradio as gr | |
| from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize | |
| from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer | |
| import transformers | |
| transformers.utils.move_cache() | |
| example_1 = "ninja turtles fighting against a mosquito, in the sea" | |
| example_2 = "warrior fighting zombies with a sword, in the forest" | |
| example_3 = "western cowboy fighting against a dragon, in the desert" | |
| def load_image(image_path): | |
| images = [] | |
| image = Image.open(image_path) | |
| image_tensor = to_tensor(image) | |
| image_tensor = image_tensor / image_tensor.max() | |
| images.append(image_tensor) | |
| return to_pil_image(images[0]) | |
| def text_to_comics(text): | |
| if text == example_1: | |
| return load_image("e1_comics_0.png") | |
| if text == example_2: | |
| return load_image("e2_comics_0.png") | |
| if text == example_3: | |
| return load_image("e3_comics_0.png") | |
| pipeline = DiffusionPipeline.from_pretrained("ogkalu/Comic-Diffusion") | |
| output = pipeline(text, prompt_len=70, num_images=1, return_tensors=True) | |
| images = [] | |
| for i in range(1): | |
| image = output.images[i] | |
| image_tensor = to_tensor(image) | |
| image_tensor = image_tensor / image_tensor.max() | |
| save_image(image_tensor, f"comics_{i}.png") | |
| images.append(to_pil_image(image_tensor)) | |
| return images[0] | |
| #text_to_comics(example_1) | |
| #text_to_comics(example_2) | |
| #text_to_comics(example_3) | |
| def generate_story(description): | |
| model = GPT2LMHeadModel.from_pretrained("gpt2") | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2") | |
| inputs = tokenizer.encode(description + " a thriller/action story.", return_tensors='pt') | |
| outputs = model.generate(input_ids=inputs, | |
| max_length=200, | |
| num_return_sequences=1, | |
| temperature=0.7, | |
| no_repeat_ngram_size=2) | |
| story = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return story | |
| def convert_to_audio(text): | |
| tts = gTTS(text) | |
| audio_file_path = "audio.mp3" | |
| tts.save(audio_file_path) | |
| return audio_file_path | |
| def audio_to_text(audio_file_path): | |
| pipe = pipeline("automatic-speech-recognition", "openai/whisper-large-v2") | |
| result = pipe("audio.mp3") | |
| print(result) | |
| return result['text'] | |
| def sentiment_analysis(text): | |
| sentiment_analyzer = pipeline("sentiment-analysis") | |
| result = sentiment_analyzer(text) | |
| print(result) | |
| return result | |
| def app(text): | |
| comics = text_to_comics(text) | |
| story = generate_story(text) | |
| audio_file = convert_to_audio(story) | |
| transcribed_text = audio_to_text(audio_file) | |
| sentiment = sentiment_analysis(transcribed_text) | |
| return comics, audio_file,transcribed_text, sentiment | |
| ui = gr.Interface(fn=app, | |
| inputs="text", | |
| outputs=["image", "audio", "text", "text"], | |
| title="GenAI Multi-model LLM comics: Type some text get comics!", | |
| description="This model generates comics based on the text(max 70 chars) you provide." + \ | |
| "<BR/>It does not work on mobile(timeout issue) click on examples if dont want to wait. " + \ | |
| "<BR/>It may take ~10-20min to generate the comics.", | |
| examples=[(example_1),(example_2),(example_3)], | |
| ) | |
| ui.launch() |