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import gradio as gr
from huggingface_hub import InferenceClient
import os
import re
#from transformers import Qwen2_5OmniForConditionalGeneration
#qwenModel = Qwen2_5OmniForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2.5-Omni-7B",
# device_map="auto",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
#)
# Load API key from environment variables
HF_API_TOKEN = os.getenv("HUG_TOKEN_READ2")
#HF_INF_PROVIDERS_TOKEN
#HF_INF_KEY = os.getenv("INF_NEBIUS")
# Hugging Face Inference API Client
#client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.1", token=HF_API_TOKEN)
#client = InferenceClient(model="openGPT-X/Teuken-7B-instruct-commercial-v0.4", token=HF_API_TOKEN)
#client = InferenceClient(model=qwenModel, token=HF_API_TOKEN)
#client = InferenceClient(model="microsoft/Phi-4-mini-instruct", token=HF_API_TOKEN)
#client = InferenceClient(model="openai-community/gpt2", token=HF_API_TOKEN)
client = InferenceClient(
provider="nebius",
#api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
api_key=HF_API_TOKEN
)
# Function to translate text into emojis
def text_to_emoji(text):
# remove special characters
text_cleaned = re.sub(r"[.,!?;:]", "", text)
prompt = f"Convert this sentence into an emoji-sequence which conveys a similar meaning and return only the emojis, no explanation:\n\n\"{text_cleaned}\""
#response = client.text_generation(prompt, max_new_tokens=50)
#return response
completion = client.chat.completions.create(
model="mistralai/Mistral-Small-3.1-24B-Instruct-2503",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
}
]
}
],
max_tokens=25
)
return completion.choices[0].message
# Gradio UI
iface = gr.Interface(
fn=text_to_emoji,
inputs=gr.Textbox(lines=2, placeholder="Enter a sentence..."),
outputs="text",
title="AI-Powered Emoji Translator",
description="Enter a sentence, and the AI will transform it into an emoji-version 🥳"
)
iface.launch() |