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import gradio as gr
import torch
import requests
from PIL import Image
from io import BytesIO
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
model_id = 'Arabic250/gemma-4-E4B-it'
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True
)
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
quantization_config=quantization_config,
device_map='auto'
)
def predict_chat(message, history):
try:
parts = message.split(' ', 1)
has_url = parts[0].startswith('http')
if has_url:
image_url = parts[0]
user_prompt = parts[1] if len(parts) > 1 else 'صف هذه الصورة'
response = requests.get(image_url, stream=True)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.new('RGB', (224, 224), color = (255, 255, 255))
user_prompt = message
messages = [{'role': 'user', 'content': [{'type': 'image'}, {'type': 'text', 'text': user_prompt}]}]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=image, return_tensors='pt').to(model.device)
with torch.inference_mode():
output = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
return processor.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip()
except Exception as e: return f'Error: {str(e)}'
chat_interface = gr.ChatInterface(fn=predict_chat, title='Gemma-4-E4B-it Arabic Space')
chat_interface.launch()
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