app.py

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

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")
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), (6799, "US female (slt)")], value=2)
tab3 = gr.Interface(
    fn=text2speech,
    inputs=[radio3, "text", radio3_1],
    outputs=["audio"],
)

demo = gr.TabbedInterface([tab1, tab2, tab3], ["tab1", "tab2", "tab3"])
demo.launch()