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app.py

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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()