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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Qwen/Qwen2.5-Math-1.5B")
pipe(messages)
# Load the model and tokenizer
# model_name = "Qwen/Qwen2-Math-1.5B"
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model = AutoModelForCausalLM.from_pretrained(
# model_name,
# torch_dtype="auto",
# device_map="auto"
# ).to(device)
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# # Define a function for Gradio to handle user input
# def solve_math(prompt):
# messages = [
# {"role": "system", "content": "You are a helpful assistant."},
# {"role": "user", "content": prompt}
# ]
# text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# model_inputs = tokenizer([text], return_tensors="pt").to(device)
# generation_config = GenerationConfig(
# do_sample=False, # For greedy decoding
# max_new_tokens=512
# )
# generated_ids = model.generate(
# **model_inputs,
# generation_config=generation_config
# )
# # Remove the input tokens from the output
# generated_ids = [
# output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
# ]
# # Decode the generated output and return the result
# response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
# return response
# # Create the Gradio interface
# iface = gr.Interface(
# fn=solve_math, # Function to call
# inputs="text", # Text input for the user prompt
# outputs="text", # Text output for the model's response
# title="Math Solver", # App title
# description="Provide a math problem and the model will solve it."
# )
# Launch the app
if __name__ == "__main__":
iface.launch()
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