app.py

import torch
import os
import random
import gradio as gr
from TTS.api import TTS
from transformers import pipeline
import base64
from datasets import load_dataset
from diffusers import DiffusionPipeline
from huggingface_hub import login
import numpy as np
import spaces
import time

@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, no0, sample):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    os.environ["COQUI_TOS_AGREED"] = "1"
    if sample is None:
        sample = "sampleaudio/abraham.wav"
    if len(text) > 0:
        epoch_time = str(int(time.time()))
        tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
        wav = tts.tts_to_file(text=text, file_path="output-"+epoch_time+".wav", 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

@spaces.GPU
def RunLegalModel(text, model):
    pettyfogger  = pipeline("text-generation", model=model)
    if text is not None:  
        shoddyadvice = pettyfogger(text)
    print(shoddyadvice)
    return shoddyadvice[0]['generated_text']
    
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!", label="Type text to convert to your voice:")
sampletext = gr.HTML("""
<h3>If you do not sample your voice my voice will be used as input:<h3>
 <audio controls>
    <source src="https://huggingface.co/spaces/Abrahamau/gradiotest/resolve/main/sampleaudio/abraham.wav" type="audio/wav">
    Your browser does not support the audio element.
</audio>
""")

micinput = gr.Audio(sources=['microphone'], type="filepath", format="wav", label="Please Provide a Sample Voice for the Model to Mimic")
outaudio = gr.Audio(show_download_button=True, show_share_button=True)
tab3 = gr.Interface(
    fn=text2speech,
    inputs=[textbox, sampletext, micinput],
    outputs=[outaudio],
)

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"],
)


classifiertypes = ["umarbutler/open-australian-legal-llm"]
radio5 = gr.Radio(classifiertypes, value="umarbutler/open-australian-legal-llm", label="Select", info="Legal Model")
textinput5 = gr.Textbox(value="Under the purposes of Part 6 Division 2 of the Act, regulations may confer power on an applicant for")


tab5 = gr.Interface(
    fn=RunLegalModel,
    inputs=[textinput5, radio5],
    outputs=["text"],
)

demo = gr.TabbedInterface([tab1, tab2, tab3, tab4, tab5], ["Describe", "Estimage Age", "Speak", "Generate Image", "Aus. Legal"])
demo.launch()