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| import torch | |
| import os | |
| import random | |
| import gradio as gr | |
| from transformers import pipeline | |
| import base64 | |
| from datasets import load_dataset | |
| from diffusers import DiffusionPipeline | |
| from huggingface_hub import login | |
| import numpy as np | |
| def guessanImage(model, image): | |
| imgclassifier = pipeline("image-classification", model=model) | |
| if image is not None: | |
| description = imgclassifier(image) | |
| return description | |
| def guessanAge(model, image): | |
| imgclassifier = pipeline("image-classification", model=model) | |
| if image is not None: | |
| description = imgclassifier(image) | |
| return description | |
| def text2speech(model, text, voice): | |
| print(voice) | |
| if len(text) > 0: | |
| synthesiser = pipeline("text-to-speech", model=model) | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embedding = torch.tensor(embeddings_dataset[voice]["xvector"]).unsqueeze(0) | |
| speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding}) | |
| audio_data = np.frombuffer(speech["audio"], dtype=np.float32) | |
| audio_data_16bit = (audio_data * 32767).astype(np.int16) | |
| return speech["sampling_rate"], audio_data_16bit | |
| def ImageGenFromText(text, model): | |
| api_key = os.getenv("fluxauthtoken") | |
| login(token=api_key) | |
| if len(text) > 0: | |
| dtype = torch.bfloat16 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| seed = random.randint(0, MAX_SEED) | |
| pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=dtype).to(device) | |
| generator = torch.Generator().manual_seed(seed) | |
| image = pipe( | |
| prompt = text, | |
| width = 512, | |
| height = 512, | |
| num_inference_steps = 4, | |
| generator = generator, | |
| guidance_scale=0.0 | |
| ).images[0] | |
| print(image) | |
| return image | |
| radio1 = gr.Radio(["microsoft/resnet-50", "google/vit-base-patch16-224", "apple/mobilevit-small"], value="microsoft/resnet-50", label="Select a Classifier", info="Image Classifier") | |
| tab1 = gr.Interface( | |
| fn=guessanImage, | |
| inputs=[radio1, gr.Image(type="pil")], | |
| outputs=["text"], | |
| ) | |
| radio2 = gr.Radio(["nateraw/vit-age-classifier"], value="nateraw/vit-age-classifier", label="Select an Age Classifier", info="Age Classifier") | |
| tab2 = gr.Interface( | |
| fn=guessanAge, | |
| inputs=[radio2, gr.Image(type="pil")], | |
| outputs=["text"], | |
| ) | |
| textbox = gr.Textbox(value="good morning pineapple! looking very good very nice!") | |
| radio3 = gr.Radio(["microsoft/speecht5_tts"], value="microsoft/speecht5_tts", label="Select an tts", info="Age Classifier") | |
| radio3_1 = gr.Radio([("Scottish male (awb)", 0), ("US male (bdl)", 1138), ("US female (clb)", 2271), ("Canadian male (jmk)",3403), ("Indian male (ksp)", 4535), ("US male (rms)", 5667), ("US female (slt)", 6799)], value=4535) | |
| tab3 = gr.Interface( | |
| fn=text2speech, | |
| inputs=[radio3, textbox, radio3_1], | |
| outputs=["audio"], | |
| ) | |
| radio3 = gr.Radio(["black-forest-labs/FLUX.1-schnell"], value="black-forest-labs/FLUX.1-schnell", label="Select", info="text to image") | |
| tab4 = gr.Interface( | |
| fn=ImageGenFromText, | |
| inputs=["text", "model"], | |
| outputs=["image"], | |
| ) | |
| demo = gr.TabbedInterface([tab1, tab2, tab3, tab4], ["tab1", "tab2", "tab3", "tab4"]) | |
| demo.launch() | |