app.py

import spaces
import random
import torch
import gradio as gr
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration

model_path = "ibm-granite/granite-vision-3.1-2b-preview"
processor = LlavaNextProcessor.from_pretrained(model_path, use_fast=True)
model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto", device_map="auto")

def get_text_from_content(content):
    texts = []
    for item in content:
        if item["type"] == "text":
            texts.append(item["text"])
        elif item["type"] == "image":
            texts.append("[Image]")
    return " ".join(texts)

@spaces.GPU
def chat_inference(image, text, temperature, top_p, top_k, max_tokens, conversation):
    if conversation is None:
        conversation = []
        
    user_content = []
    if image is not None:
        user_content.append({"type": "image", "image": image})
    if text and text.strip():
        user_content.append({"type": "text", "text": text.strip()})
    if not user_content:
        return conversation_display(conversation), conversation

    conversation.append({
        "role": "user",
        "content": user_content
    })

    inputs = processor.apply_chat_template(
        conversation,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt"
    ).to("cpu")

    torch.manual_seed(random.randint(0, 10000))

    generation_kwargs = {
        "max_new_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "do_sample": True,
    }

    output = model.generate(**inputs, **generation_kwargs)
    assistant_response = processor.decode(output[0], skip_special_tokens=True)

    conversation.append({
        "role": "assistant",
        "content": [{"type": "text", "text": assistant_response.strip()}]
    })
    
    return conversation_display(conversation), conversation

def conversation_display(conversation):
    chat_history = []
    for msg in conversation:
        if msg["role"] == "user":
            user_text = get_text_from_content(msg["content"])
        elif msg["role"] == "assistant":
            assistant_text = msg["content"][0]["text"].split("<|assistant|>")[-1].strip()
            chat_history.append({"role": "user", "content": user_text})
            chat_history.append({"role": "assistant", "content": assistant_text})
    return chat_history

def clear_chat():
    return [], [], "", None
    
with gr.Blocks(title="Granite Vision 3.1 2B", css="h1 { overflow: hidden; }") as demo:
    gr.Markdown("# Granite Vision 3.1 2B")
    
    with gr.Row():
        with gr.Column(scale=2):
            image_input = gr.Image(type="pil", label="Upload Image (optional)")
            with gr.Column():
                temperature_input = gr.Slider(minimum=0.0, maximum=2.0, value=0.2, step=0.01, label="Temperature")
                top_p_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label="Top p")
                top_k_input = gr.Slider(minimum=0, maximum=100, value=50, step=1, label="Top k")
                max_tokens_input = gr.Slider(minimum=10, maximum=300, value=128, step=1, label="Max Tokens")
            
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot", type='messages')
            text_input = gr.Textbox(lines=2, placeholder="Enter your message here", label="Message")
            with gr.Row():
                send_button = gr.Button("Chat")
                clear_button = gr.Button("Clear Chat")
    

    state = gr.State([])

    send_button.click(
        chat_inference,
        inputs=[image_input, text_input, temperature_input, top_p_input, top_k_input, max_tokens_input, state],
        outputs=[chatbot, state]
    )

    clear_button.click(
        clear_chat,
        inputs=None,
        outputs=[chatbot, state, text_input, image_input]
    )

    gr.Examples(
        examples=[
            ["https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "What is this?"]
        ],
        inputs=[image_input, text_input]
    )

if __name__ == "__main__":
    demo.launch()