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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):
if len(text) > 0:
speechclassifier = pipeline("text-to-speech", model=model)
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
output = speechclassifier(text, forward_params={"speaker_embeddings": speaker_embedding})
return output
radio1 = gr.Radio(["microsoft/resnet-50", "google/vit-base-patch16-224", "apple/mobilevit-small"], 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"], label="Select an Age Classifier", info="Age Classifier")
tab2 = gr.Interface(
fn=guessanAge,
inputs=[radio2, gr.Image(type="pil")],
outputs=["text"],
)
radio3 = gr.Radio(["microsoft/speecht5_tts"], label="Select an tts", info="Age Classifier")
tab3 = gr.Interface(
fn=text2speech,
inputs=[radio3, "text"],
outputs=[gr.Audio(label="Generated Speech", type="numpy")],
)
demo = gr.TabbedInterface([tab1, tab2, tab3], ["tab1", "tab2", "tab3"])
demo.launch()
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