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import torch
import os
import random
import gradio as gr
from TTS.api import TTS
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, pipeline
import base64
from datasets import load_dataset
from diffusers import DiffusionPipeline
from huggingface_hub import login
import numpy as np
import spaces
@spaces.GPU
def guessanImage(model, image):
imgclassifier = pipeline("image-classification", model=model)
if image is not None:
description = imgclassifier(image)
return description
@spaces.GPU
def guessanAge(model, image):
imgclassifier = pipeline("image-classification", model=model)
if image is not None:
description = imgclassifier(image)
return description
@spaces.GPU(duration=120)
def text2speech(text, sample):
print(TTS().list_models())
print(voice)
if len(text) > 0:
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
wav = tts.tts(text="Hello world!", speaker_wav=sample, language="en")
return wav
@spaces.GPU
def ImageGenFromText(text, model):
api_key = os.getenv("fluxauth")
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!")
tab3 = gr.Interface(
fn=text2speech,
inputs=[textbox, "microphone"],
outputs=["audio"],
)
radio4 = 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", radio4],
outputs=["image"],
)
demo = gr.TabbedInterface([tab1, tab2, tab3, tab4], ["Describe", "Estimage Age", "Speak", "Generate Image"])
demo.launch()
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