import gradio as gr from PIL import Image from transformers import AutoModelForCausalLM from transformers import AutoProcessor from transformers import TextIteratorStreamer import time from threading import Thread import torch import spaces model_id = "microsoft/Phi-3-vision-128k-instruct" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto") processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) model.to("cuda:0") PLACEHOLDER = """

Microsoft's Phi3-Vision-128k-Context

Phi-3-Vision is a 4.2B parameter multimodal model that brings together language and vision capabilities.

""" @spaces.GPU def bot_streaming(message, history): print(f'message is - {message}') print(f'history is - {history}') if message["files"]: # message["files"][-1] is a Dict or just a string if type(message["files"][-1]) == dict: image = message["files"][-1]["path"] else: image = message["files"][-1] else: # if there's no image uploaded for this turn, look for images in the past turns # kept inside tuples, take the last one for hist in history: if type(hist[0]) == tuple: image = hist[0][0] try: if image is None: # Handle the case where image is None raise gr.Error("You need to upload an image for FalconVLM to work. Close the error and try again with an Image.") except NameError: # Handle the case where 'image' is not defined at all raise gr.Error("You need to upload an image for FalconVLM to work. Close the error and try again with an Image.") conversation = [] flag=False for user, assistant in history: if assistant is None: #pass flag=True conversation.extend([{"role": "user", "content":""}]) continue if flag==True: conversation[0]['content'] = f"<|image_1|>\n{user}" conversation.extend([{"role": "assistant", "content": assistant}]) flag=False continue #conversation += f"""User:\n{user} Falcon:{assistant} """ conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) if len(history) == 0: conversation.append({"role": "user", "content": f"<|image_1|>\n{message['text']}"}) else: conversation.append({"role": "user", "content": message['text']}) print(f"prompt is -\n{conversation}") #prompt = f"""User:\n{message['text']} Falcon:""" prompt = processor.tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) image = Image.open(image) inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") #inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16) streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) # "eos_token_id":processor.tokenizer.eos_token_id}) generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False, temperature=0.0, eos_token_id=processor.tokenizer.eos_token_id,) thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() buffer = "" for new_text in streamer: # find <|eot_id|> and remove it from the new_text #if "<|eot_id|>" in new_text: # new_text = new_text.split("<|eot_id|>")[0] buffer += new_text yield buffer chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER) chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False) with gr.Blocks(fill_height=True, ) as demo: gr.ChatInterface( fn=bot_streaming, title="FalconVLM", examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]}, {"text": "How to make this pastry?", "files": ["./baklava.png"]}], description="Try [tiiuae/falcon-11B-VLM](https://huggingface.co/tiiuae/falcon-11B-vlm). 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.", stop_btn="Stop Generation", multimodal=True, textbox=chat_input, chatbot=chatbot, cache_examples=False, ) demo.queue() demo.launch(debug=True, quiet=True)