app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import spaces
import torch
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

DESCRIPTION = """\
Shakti is a 2.5 billion parameter language model specifically optimized for resource-constrained environments such as edge devices, including smartphones, wearables, and IoT systems. With support for vernacular languages and domain-specific tasks, Shakti excels in industries such as healthcare, finance, and customer service.
For more details, please check [here](https://arxiv.org/pdf/2410.11331v1).
"""

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "2048"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Model configurations
model_options = {
    "Shakti-100M": "SandLogicTechnologies/Shakti-100M",
    "Shakti-250M": "SandLogicTechnologies/Shakti-250M",
    "Shakti-2.5B": "SandLogicTechnologies/Shakti-2.5B"
}

# Initialize tokenizer and model variables
tokenizer = None
model = None
current_model = "Shakti-2.5B"  # Keep track of current model


def load_model(selected_model: str):
    global tokenizer, model, current_model
    model_id = model_options[selected_model]
    tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.getenv("SHAKTI"))
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",
        torch_dtype=torch.bfloat16,
        token=os.getenv("SHAKTI")
    )
    model.eval()
    current_model = selected_model  # Update the current model


# Initial model load (default to 2.5B)
load_model("Shakti-2.5B")


@spaces.GPU(duration=90)
def generate(
        message: str,
        chat_history: list[tuple[str, str]],
        max_new_tokens: int = 1024,
        temperature: float = 0.6,
        top_p: float = 0.9,
        top_k: int = 50,
        repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []

    # Conditional logic for adding prompt based on model
    if current_model == "Shakti-2.5B":
        for user, assistant in chat_history:
            conversation.extend(
                [
                    json.loads(os.getenv("PROMPT")),
                    {"role": "user", "content": user},
                    {"role": "assistant", "content": assistant},
                ]
            )
    else:
        for user, assistant in chat_history:
            conversation.extend(
                [
                    {"role": "user", "content": user},
                    {"role": "assistant", "content": assistant},
                ]
            )

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

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


def update_examples(selected_model):
    if selected_model == "Shakti-100M":
        return [["Tell me a story"],
                ["Write a short poem on Rose"],
                ["What are computers"]]
    elif selected_model == "Shakti-250M":
        return [["Can you explain the pathophysiology of hypertension and its impact on the cardiovascular system?"],
                ["What are the potential side effects of beta-blockers in the treatment of arrhythmias?"],
                ["What foods are good for boosting the immune system?"],
                ["What is the difference between a stock and a bond?"],
                ["How can I start saving for retirement?"],
                ["What are some low-risk investment options?"],
                ["What is a power of attorney and when is it used?"],
                ["What are the key differences between a will and a trust?"],
                ["How do I legally protect my business name?"]]
    else:
        return [["Tell me a story"], ["write a short poem which is hard to sing"],
                ['मुझे भारतीय इतिहास के बारे में बताएं']]


def on_model_select(selected_model):
    load_model(selected_model)  # Load the selected model
    examples = update_examples(selected_model)  # Update examples
    return gr.update(examples=examples), gr.update(value=[])  # Clear the chat space and update examples


chat_history = gr.Chatbot()

with gr.Blocks(css="style.css", fill_height=True) as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")

    # Dropdown for model selection
    model_dropdown = gr.Dropdown(
        label="Select Model",
        choices=["Shakti-100M", "Shakti-250M", "Shakti-2.5B"],
        value="Shakti-2.5B",
        interactive=True,
    )

    # Create the interface with dynamic inputs and chat history
    max_tokens_slider = gr.Slider(
        label="Max new tokens",
        minimum=1,
        maximum=MAX_MAX_NEW_TOKENS,
        step=1,
        value=DEFAULT_MAX_NEW_TOKENS,
    )

    temperature_slider = gr.Slider(
        label="Temperature",
        minimum=0.1,
        maximum=4.0,
        step=0.1,
        value=0.6,
    )

    chat_interface = gr.Interface(
        fn=generate,
        inputs=[gr.Textbox(lines=2, placeholder="Enter your message here"), chat_history, max_tokens_slider,
                temperature_slider],
        outputs=chat_history,
        live=True,
    )

    # Function to handle model change and update examples dynamically
    model_dropdown.change(on_model_select, inputs=model_dropdown, outputs=[chat_interface, chat_history])

    demo.queue(max_size=20).launch()