pip install --pre -U --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/pre-release openvino_tokenizers openvino

pip install git+https://github.com/huggingface/optimum-intel.git

from PIL import Image 
import requests 
from optimum.intel.openvino import OVModelForVisualCausalLM
from transformers import AutoTokenizer, TextStreamer

model_id = "OpenVINO/InternVL2-1B-int4-ov"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

ov_model = OVModelForVisualCausalLM.from_pretrained(model_id, trust_remote_code=True)
prompt = "What is unusual on this picture?"

url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
image = Image.open(requests.get(url, stream=True).raw)

inputs = ov_model.preprocess_inputs(text=prompt, image=image, tokenizer=tokenizer, config=ov_model.config)

generation_args = { 
    "max_new_tokens": 100, 
    "streamer": TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
} 

generate_ids = ov_model.generate(**inputs, **generation_args)

generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]

pip install --pre -U --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/pre-release openvino openvino-tokenizers openvino-genai

pip install huggingface_hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/InternVL2-1B-int4-ov"
model_path = "InternVL2-1B-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

import openvino_genai as ov_genai
import requests
from PIL import Image
from io import BytesIO
import numpy as np
import openvino as ov

device = "CPU"
pipe = ov_genai.VLMPipeline(model_path, device)

def load_image(image_file):
    if isinstance(image_file, str) and (image_file.startswith("http") or image_file.startswith("https")):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.byte)
    return ov.Tensor(image_data)

prompt = "What is unusual on this picture?"

url = "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/d5fbbd1a-d484-415c-88cb-9986625b7b11"
image_tensor = load_image(url)

def streamer(subword: str) -> bool:
    print(subword, end="", flush=True)
    return False

pipe.start_chat()
output = pipe.generate(prompt, image=image_tensor, max_new_tokens=100, streamer=streamer)
pipe.finish_chat()