import os import gradio as gr #import whisper import requests import tempfile #from neon_tts_plugin_coqui import CoquiTTS from datasets import load_dataset import random dataset = load_dataset("ysharma/short_jokes", split="train") filtered_dataset = dataset.filter( lambda x: (True not in [nsfw in x["Joke"].lower() for nsfw in ["warning","porn", "blow", "fuck", "dead", "nsfw","69", "sex", "prostitute","prostitutes", "pedophiles", "pedophile"]]) ) # Model 2: Sentence Transformer API_URL = "https://api-inference.huggingface.co/models/sentence-transformers/msmarco-distilbert-base-tas-b" HF_TOKEN = os.environ["HF_TOKEN"] headers = {"Authorization": f"Bearer {HF_TOKEN}"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() # Driver function def driver_fun(text) : print("*********** Inside Driver ************") random_val = random.randrange(0,231657) if random_val < 226657: lower_limit = random_val upper_limit = random_val + 4000 else: lower_limit = random_val - 4000 upper_limit = random_val print(f"lower_limit : upper_limit = {lower_limit} : {upper_limit}") dataset_subset = filtered_dataset['Joke'][lower_limit : upper_limit] data = query({"inputs": {"source_sentence": text ,"sentences": dataset_subset} } ) #"That is a happy person" if 'error' in data: print(f"Error is : {data}") return 'Error in model inference - Run Again Please', 'Error in model inference - Run Again Please', None print(f"type(data) : {type(data)}") #print(f"data : {data} ") max_match_score = max(data) indx_score = data.index(max_match_score) joke = dataset_subset[indx_score] print(f"Joke is : {joke}") return joke demo = gr.Blocks() with demo: gr.Markdown("