import gradio as gr
import torch
import time
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import time
import pytz
from datetime import datetime
print("Loading model and tokenizer...")
model_name = "large-traversaal/Phi-4-Hindi"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
print("Model and tokenizer loaded successfully!")
def generate_response(message, temperature, max_new_tokens, top_p):
    print(f"Input: {message}")
    start_time = time.time()
    inputs = tokenizer(message, return_tensors="pt").to(model.device)
    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
    gen_kwargs = {
        "input_ids": inputs["input_ids"],
        "streamer": streamer,
        "temperature": temperature,
        "max_new_tokens": max_new_tokens,
        "top_p": top_p,
        "do_sample": True if temperature > 0 else False,
    }
    thread = Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()
    result = []
    for text in streamer:
        result.append(text)
        current_output = "".join(result)
        if current_output.startswith(message):
            yield current_output[len(message):]
        else:
            yield current_output
    end_time = time.time()
    time_taken = end_time - start_time
    output_text = "".join(result)
    if output_text.startswith(message):
        output_text = output_text[len(message):]
    print(f"Output: {output_text}")
    print(f"Time taken: {time_taken:.2f} seconds")
    pst_timezone = pytz.timezone('America/Los_Angeles')
    current_time_pst = datetime.now(pst_timezone).strftime("%Y-%m-%d %H:%M:%S %Z%z")
    print(f"Current timestamp (PST): {current_time_pst}")
with gr.Blocks() as demo:
    gr.Markdown("# Phi-4-Hindi Demo")
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(
                label="Input",
                placeholder="Enter your text here...",
                lines=5
            )
            with gr.Row():
                with gr.Column():
                    temperature = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.1,
                        step=0.01,
                        label="Temperature"
                    )
                with gr.Column():
                    max_new_tokens = gr.Slider(
                        minimum=50,
                        maximum=1000,
                        value=400,
                        step=10,
                        label="Max New Tokens"
                    )
                with gr.Column():
                    top_p = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.1,
                        step=0.01,
                        label="Top P"
                    )
            with gr.Row():
                clear_btn = gr.Button("Clear")
                send_btn = gr.Button("Send", variant="primary")
        with gr.Column():
            output_text = gr.Textbox(
                label="Output",
                lines=15
            )
    send_btn.click(
        fn=generate_response,
        inputs=[input_text, temperature, max_new_tokens, top_p],
        outputs=output_text
    )
    clear_btn.click(
        fn=lambda: ("", "", "", ""),
        inputs=None,
        outputs=[input_text, output_text]
    )
if __name__ == "__main__":
    demo.queue().launch()