app.py

import spaces
from transformers import (
    TextIteratorStreamer,
)
from transformers import (
    AutoProcessor,
    BitsAndBytesConfig,
    LlavaForConditionalGeneration,
)
from PIL import Image
import gradio as gr
from threading import Thread
from dotenv import load_dotenv

# Add these imports
from datetime import datetime
import pytz
from typing import Optional
from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer
import torch
from theme import Seafoam


load_dotenv()

# Add TESTING variable
TESTING = False

# Hugging Face model id
# model_id = "mistral-community/pixtral-12b"
model_id = "blanchon/PixDiet-pixtral-nutrition-v2"

# BitsAndBytesConfig int-4 config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# Modify the model and processor initialization
if TESTING:
    model_id = "vikhyatk/moondream1"
    model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
    processor = Tokenizer.from_pretrained(model_id)
else:
    model = LlavaForConditionalGeneration.from_pretrained(
        model_id,
        device_map="auto",
        torch_dtype=torch.bfloat16,
        quantization_config=bnb_config,
    )
    processor = AutoProcessor.from_pretrained(model_id)

# Set the chat template for the tokenizer
processor.chat_template = """
{%- for message in messages %}
    {%- if message.role == "user" %}
        <s>[INST]
        {%- for item in message.content %}
            {%- if item.type == "text" %}
                {{ item.text }}
            {%- elif item.type == "image" %}
                \n[IMG]
            {%- endif %}
        {%- endfor %}
        [/INST]
    {%- elif message.role == "assistant" %}
        {%- for item in message.content %}
            {%- if item.type == "text" %}
                {{ item.text }}
            {%- endif %}
        {%- endfor %}
        </s>
    {%- endif %}
{%- endfor %}
""".replace("    ", "")

processor.tokenizer.pad_token = processor.tokenizer.eos_token


@spaces.GPU
def bot_streaming(chatbot, image_input, max_new_tokens=250):
    # Preprocess inputs
    messages = []
    images = []
    text_input = chatbot[-1][0]

    # Get current time in Paris timezone
    paris_tz = pytz.timezone("Europe/Paris")
    current_time = datetime.now(paris_tz).strftime("%I:%M%p")

    if text_input != "":
        text_input = f"Current time: {current_time}. You are a nutrition expert. Identify the food/ingredients in this image. Is this a healthy meal? Can you think of how to improve it?"
    else:
        text_input = f"Current time: {current_time}. You are a nutrition expert. Identify the food/ingredients in this image. Is this a healthy meal? Can you think of how to improve it?"

    # Add current message
    if image_input is not None:
        # Check if image_input is already a PIL Image
        if isinstance(image_input, Image.Image):
            image = image_input.convert("RGB")
        else:
            image = Image.fromarray(image_input).convert("RGB")
        images.append(image)
        messages.append(
            {
                "role": "user",
                "content": [{"type": "text", "text": text_input}, {"type": "image"}],
            }
        )
    else:
        messages.append(
            {"role": "user", "content": [{"type": "text", "text": text_input}]}
        )

    # Apply chat template
    texts = processor.apply_chat_template(messages)

    # Process inputs
    if not images:
        inputs = processor(text=texts, return_tensors="pt").to("cuda")
    else:
        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")

    streamer = TextIteratorStreamer(
        processor.tokenizer, skip_special_tokens=True, skip_prompt=True
    )

    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)

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

    response = ""
    for new_text in streamer:
        response += new_text
        chatbot[-1][1] = response
        yield chatbot

    thread.join()

    # Debug output
    print("*" * 60)
    print("*" * 60)
    print("BOT_STREAMING_CONV_START")
    for i, (request, answer) in enumerate(chatbot[:-1], 1):
        print(f"Q{i}:\n {request}")
        print(f"A{i}:\n {answer}")
    print("New_Q:\n", text_input)
    print("New_A:\n", response)
    print("BOT_STREAMING_CONV_END")

    new_history = messages + [
        {"role": "assistant", "content": [{"type": "text", "text": response}]}
    ]


seafoam = Seafoam()

# Define the HTML content for the header
html = """
<!-- Foreground content -->
<p align="center" style="font-size: 2.5em; line-height: 1; ">
    <span style="display: inline-block; vertical-align: middle;">🍽️</span>
    <span style="display: inline-block; vertical-align: middle;">PixDiet</span>
</p>
<center>
    <font size=3><b>PixDiet</b> is your AI nutrition expert. Upload an image of your meal and chat with our AI to get personalized advice on your diet, meal composition, and ways to improve your nutrition.</font>
</center>
<!-- Background image positioned behind everything -->
<div style="display: flex; flex-direction: column; justify-content: center; align-items: center; margin-top: 20px; width: 100%;">
    <div style="display: flex; justify-content: center; width: 100%;">
        <img src="https://dropshare.blanchon.xyz/public/dropshare/alan.png" alt="Alan AI Logo" style="height: 50px; margin-right: 20px;">
        <img src="https://dropshare.blanchon.xyz/public/dropshare/mistral-ai-icon-logo-B3319DCA6B-seeklogo.com.png" alt="Mistral AI Logo" style="height: 50px;">
    </div>
</div>
"""

footer_html = """
<!-- Footer content -->
<div style="display: flex; flex-direction: column; justify-content: center; align-items: center; margin-top: 20px; width: 100%;">
    <div style="display: flex; justify-content: center; width: 100%;">
        <img src="https://dropshare.blanchon.xyz/public/dropshare//VariantVariant6-Photoroom.png" alt="Background Image" 
    style="height: 100px; width: 100%; object-fit: scale-down;">
    </div>
    <div>
        Made with ❤️ during the Mistral AI x Alan Hackathon.
    </div>

</div>
"""


# Define LaTeX delimiters
latex_delimiters_set = [
    {"left": "\\(", "right": "\\)", "display": False},
    {"left": "\\begin{equation}", "right": "\\end{equation}", "display": True},
    {"left": "\\begin{align}", "right": "\\end{align}", "display": True},
    {"left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True},
    {"left": "\\begin{gather}", "right": "\\end{gather}", "display": True},
    {"left": "\\begin{CD}", "right": "\\end{CD}", "display": True},
    {"left": "\\[", "right": "\\]", "display": True},
]

# Create the Gradio interface
with gr.Blocks(
    title="PixDiet", theme=seafoam, css="footer{display:none !important}"
) as demo:
    gr.HTML(html)

    with gr.Row():
        with gr.Column(scale=3):
            about_you = gr.Textbox(
                label="About you",
                placeholder="Add information about you here...",
                lines=3,
                interactive=True,
            )
            image_input = gr.Image(
                label="Upload your meal image", height=350, type="pil"
            )
            gr.Examples(
                examples=[
                    [
                        "./examples/mistral_breakfast.jpeg",
                        "John, 45 years old, 80kg, lactose intolerant. Training for his first triathlon.",
                    ],
                    [
                        "./examples/mistral_desert.jpeg",
                        "Emma, 26 years old, 55kg, iron deficiency. Training for her first Ironman competition.",
                    ],
                    [
                        "./examples/mistral_snacks.jpeg",
                        "Paul, 34 years old, 62kg, no known pathologies. Focused on improving strength for weightlifting competitions.",
                    ],
                    [
                        "./examples/mistral_pasta.jpeg",
                        "Carla, 52 years old, 58kg, no known pathologies. Currently training for her first marathon.",
                    ],
                ],
                inputs=[image_input, about_you],
            )
        with gr.Column(scale=7):
            chatbot = gr.Chatbot(
                label="Chat with PixDiet",
                layout="panel",
                height=700,
                show_copy_button=True,
                latex_delimiters=latex_delimiters_set,
                type=None,
            )
            text_input = gr.Textbox(
                label="Ask about your meal",
                placeholder="(Optional) Enter your message here...",
                lines=1,
                container=False,
                interactive=True,
            )
            with gr.Row():
                send_btn = gr.Button("Send", variant="primary", visible=True)
                clear_btn = gr.Button(
                    "Delete my history",
                    variant="stop",
                    visible=True,
                )

    def submit_chat(chatbot, text_input):
        response = ""
        chatbot.append((text_input, response))
        return chatbot, ""

    def clear_chat():
        return [], None, ""


    send_click_event = send_btn.click(
        submit_chat, [chatbot, text_input], [chatbot, text_input]
    ).then(bot_streaming, [chatbot, image_input], chatbot)
    submit_event = text_input.submit(
        submit_chat, [chatbot, text_input], [chatbot, text_input]
    ).then(bot_streaming, [chatbot, image_input], chatbot)
    clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input])

    gr.HTML(footer_html)

if __name__ == "__main__":
    demo.launch(debug=False, share=False, show_api=False)