"""
File: app.py
Description: Chat with the vision language model Gemma3.
Author: Didier Guillevic
Date: 2025-03-16
"""

import gradio as gr
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from transformers import TextIteratorStreamer
from threading import Thread
import torch

device = 'cuda'
model_id = "google/gemma-3-4b-it"
processor = AutoProcessor.from_pretrained(model_id, use_fast=True, padding_side="left")
model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16
).to(device).eval()

def process(message, history):
    """Generate the model response in streaming mode given message and history
    """
    print(f"{history=}")
    # Get the user's text and list of images
    user_text = message.get("text", "")
    user_images = message.get("files", [])  # List of images

    # Build the message list including history
    messages = []
    combined_user_input = [] # Combine images and text if found in same turn.
    for user_turn, bot_turn in history:
        if isinstance(user_turn, tuple):  # Image input
            image_content = [{"type": "image", "url": image_url} for image_url in user_turn]
            combined_user_input.extend(image_content)
        elif isinstance(user_turn, str): # Text input
            combined_user_input.append({"type":"text", "text": user_turn})
        if combined_user_input and bot_turn:
            messages.append({'role': 'user', 'content': combined_user_input})
            messages.append({'role': 'assistant', 'content': [{"type": "text", "text": bot_turn}]})
            combined_user_input = [] # reset the combined user input.
    
    # Build the user message's content from the provided message
    user_content = []
    if user_text:
        user_content.append({"type": "text", "text": user_text})
    for image in user_images:
        user_content.append({"type": "image", "url": image})
    
    messages.append({'role': 'user', 'content': user_content})
    
    # Generate model's response
    inputs = processor.apply_chat_template(
        messages, add_generation_prompt=True, tokenize=True,
        return_dict=True, return_tensors="pt"
    ).to(model.device, dtype=torch.bfloat16)

    streamer = TextIteratorStreamer(
        processor, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = dict(
        inputs,
        streamer=streamer,
        max_new_tokens=1_024,
        do_sample=False
    )

    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    partial_message = ""
    for new_text in streamer:
        partial_message += new_text
        yield partial_message


#
# User interface
#
with gr.Blocks() as demo:
    chat_interface = gr.ChatInterface(
        fn=process,
        title="Multimedia Chat",
        description="Chat with text or text+image.",
        multimodal=True,
        examples=[
            "How can we rationalize quantum entanglement?",
            {'files': ['./sample_ID.jpeg',], 'text': 'Describe this image in a few words.'},
            "Peux-tu expliquer le 'quantum spin'?"
        ]
    )

demo.launch()