app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "google/flan-t5-base"  # Free LLM from Hugging Face
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def solve_math_problem(problem):
    inputs = tokenizer.encode(problem, return_tensors="pt")
    outputs = model.generate(inputs, max_length=500)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Breaking it down to step-by-step
    steps = "Step-by-Step: " + result
    return steps

# Gradio interface
iface = gr.Interface(
    fn=solve_math_problem,
    inputs="text",
    outputs="text",
    title="Maths Step-by-Step Solver with LLM",
    description="Enter a maths problem and get a step-by-step solution using LLM."
)

iface.launch()