move llava next notebook on genai (#2549)

notebooks/llava-next-multimodal-chatbot/README.md

@@ -8,12 +8,10 @@ In this tutorial we consider how to convert and optimize LLaVA-NeXT model from T
 The tutorial consists from following steps:
 
 - Install requirements
-- Download PyTorch model
-- Convert model to OpenVINO Intermediate Representation (IR)
-- Compress Language Model weights
-- Quantize Image Encoder
-- Prepare Inference Pipeline
-- Run OpenVINO model inference
+- Convert model to OpenVINO Intermediate Representation (IR) using Optimum CLI
+- Compress Language Model weights using NNCF
+- Prepare OpenVINO GenAI Inference Pipeline
+- Run OpenVINO GenAI model inference
 - Launch Interactive demo
 
 In this demonstration, you'll create interactive chatbot that can answer questions about provided image's content. Image bellow shows a result of model work.

notebooks/llava-next-multimodal-chatbot/gradio_helper.py

@@ -1,7 +1,13 @@
-from PIL import Image
-from typing import Callable
+from pathlib import Path
 import gradio as gr
+
+
+from PIL import Image
+import numpy as np
 import requests
+from threading import Event, Thread
+import inspect
+from queue import Queue
 
 example_image_urls = [
     (
@@ -14,19 +20,112 @@
     ),
 ]
 for url, file_name in example_image_urls:
-    Image.open(requests.get(url, stream=True).raw).save(file_name)
+    if not Path(file_name).exists():
+        Image.open(requests.get(url, stream=True).raw).save(file_name)
+
+
+def make_demo(model):
+    import openvino_genai
+    import openvino as ov
+
+    has_additonal_buttons = "undo_button" in inspect.signature(gr.ChatInterface.__init__).parameters
+
+    def read_image(path: str) -> ov.Tensor:
+        """
+
+        Args:
+            path: The path to the image.
+
+        Returns: the ov.Tensor containing the image.
+
+        """
+        pic = Image.open(path).convert("RGB")
+        image_data = np.array(pic.getdata()).reshape(1, pic.size[1], pic.size[0], 3).astype(np.byte)
+        return ov.Tensor(image_data)
+
+    class TextQueue:
+        def __init__(self) -> None:
+            self.text_queue = Queue()
+            self.stop_signal = None
+            self.stop_tokens = []
+
+        def __call__(self, text):
+            self.text_queue.put(text)
+
+        def __iter__(self):
+            return self
+
+        def __next__(self):
+            value = self.text_queue.get()
+            if value == self.stop_signal or value in self.stop_tokens:
+                raise StopIteration()
+            else:
+                return value
+
+        def reset(self):
+            self.text_queue = Queue()
+
+        def end(self):
+            self.text_queue.put(self.stop_signal)
+
+    def bot_streaming(message, history):
+        print(f"message is - {message}")
+        print(f"history is - {history}")
+
+        if not history:
+            model.start_chat()
+        generation_config = openvino_genai.GenerationConfig()
+        generation_config.max_new_tokens = 128
+        files = message["files"] if isinstance(message, dict) else message.files
+        message_text = message["text"] if isinstance(message, dict) else message.text
+
+        image = None
+        if files:
+            # message["files"][-1] is a Dict or just a string
+            if isinstance(files[-1], dict):
+                image = files[-1]["path"]
+            else:
+                if isinstance(files[-1], (str, Path)):
+                    image = files[-1]
+                else:
+                    image = files[-1] if isinstance(files[-1], (list, tuple)) else files[-1].path
+        if image is not None:
+            image = read_image(image)
+        streamer = TextQueue()
+        stream_complete = Event()
+
+        def generate_and_signal_complete():
+            """
+            generation function for single thread
+            """
+            streamer.reset()
+            generation_kwargs = {"prompt": message_text, "generation_config": generation_config, "streamer": streamer}
+            if image is not None:
+                generation_kwargs["image"] = image
+            model.generate(**generation_kwargs)
+            stream_complete.set()
+            streamer.end()
+
+        t1 = Thread(target=generate_and_signal_complete)
+        t1.start()
 
+        buffer = ""
+        for new_text in streamer:
+            buffer += new_text
+            yield buffer
 
-def make_demo(fn: Callable):
+    additional_buttons = {}
+    if has_additonal_buttons:
+        additional_buttons = {"undo_button": None, "retry_button": None}
     demo = gr.ChatInterface(
-        fn=fn,
-        title="LLaVA NeXT",
-        description="Try [LLaVA NeXT](https://huggingface.co/docs/transformers/main/en/model_doc/llava_next) in this demo using OpenVINO. Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
+        fn=bot_streaming,
+        title="LLaVA Next OpenVINO Chatbot",
         examples=[
             {"text": "What is on the flower?", "files": ["./bee.jpg"]},
             {"text": "How to make this pastry?", "files": ["./baklava.png"]},
         ],
-        stop_btn="Stop Generation",
+        stop_btn=None,
         multimodal=True,
+        **additional_buttons,
     )
     return demo