app.py

import gradio as gr
from transformers import pipeline
from transformers_modules.Qwen.Qwen2.5-Math-PRM-72B.configuration_qwen2_rm import Qwen2RMConfig
from transformers_modules.Qwen.Qwen2.5-Math-PRM-72B.modeling_qwen2_rm import Qwen2RMForSequenceClassification

model = Qwen2RMForSequenceClassification.from_pretrained("Qwen/Qwen2.5-Math-PRM-72B")
pipe = pipeline("text-classification", model=model)


# Load the model
pipe = pipeline("text-classification", model="Qwen/Qwen2.5-Math-PRM-72B", trust_remote_code=True)

# Define a function to generate responses
def generate_response(prompt):
    output = pipe(prompt)
    return output[0]['label']

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs="text",
    outputs="text",
    title="Qwen2.5 Math Model",
    description="Ask a math question and get an answer!"
)

# Launch the interface
iface.launch()