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"""import gradio as gr
import torch
import time
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
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!")
option_mapping = {
"translation": "### TRANSLATION ###",
"mcq": "### MCQ ###",
"nli": "### NLI ###",
"summarization": "### SUMMARIZATION ###",
"long response": "### LONG RESPONSE ###",
"short response": "### SHORT RESPONSE ###",
"direct response": "### DIRECT RESPONSE ###",
"paraphrase": "### PARAPHRASE ###",
"code": "### CODE ###"
}
def generate_response(message, temperature, max_new_tokens, top_p, task):
append_text = option_mapping.get(task, "")
prompt = f"INPUT : {message} {append_text} RESPONSE : "
print(f"Prompt: {prompt}")
start_time = time.time()
inputs = tokenizer(prompt, 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)
yield "".join(result)
end_time = time.time()
time_taken = end_time - start_time
output_text = "".join(result)
if "RESPONSE : " in output_text:
output_text = output_text.split("RESPONSE : ", 1)[1].strip()
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
)
task_dropdown = gr.Dropdown(
choices=["translation", "mcq", "nli", "summarization", "long response", "short response", "direct response", "paraphrase", "code"],
value="long response",
label="Task"
)
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, task_dropdown],
outputs=output_text
)
clear_btn.click(
fn=lambda: ("", ""),
inputs=None,
outputs=[input_text, output_text]
)
if __name__ == "__main__":
demo.queue().launch()
"""
import gradio as gr
import torch
import time
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
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!")
option_mapping = {"translation": "### TRANSLATION ###", "mcq": "### MCQ ###", "nli": "### NLI ###", "summarization": "### SUMMARIZATION ###",
"long response": "### LONG RESPONSE ###", "direct response": "### DIRECT RESPONSE ###", "paraphrase": "### PARAPHRASE ###", "code": "### CODE ###"}
def generate_response(message, temperature, max_new_tokens, top_p, task):
append_text = option_mapping.get(task, "")
prompt = f"INPUT : {message} {append_text} ### RESPONSE : "
print(f"Prompt: {prompt}")
start_time = time.time()
inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=max_new_tokens, use_cache=True, temperature=temperature, min_p=top_p, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
processed_response = response.split("### RESPONSE :assistant")[-1].strip()
end_time = time.time()
time_taken = end_time - start_time
print(f"Output: {processed_response}")
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}")
return processed_response
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)
task_dropdown = gr.Dropdown(choices=["translation", "mcq", "nli", "summarization", "long response", "direct response", "paraphrase", "code"], value="long response", label="Task")
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=10, maximum=1000, value=10, 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, task_dropdown], outputs=output_text)
clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
if __name__ == "__main__":
demo.queue().launch() |