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# Solving Tomorrow AI Model Implementation
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

class SolvingTomorrowAI:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained("solving-tomorrow/ai-assistant")
        self.model = AutoModelForCausalLM.from_pretrained("solving-tomorrow/ai-assistant")
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        
    def generate_solution(self, problem_statement, max_length=512):
        """Generate an AI-powered solution for a business problem"""
        inputs = self.tokenizer(f"Problem: {problem_statement}\nSolution:", 
                               return_tensors="pt").to(self.device)
        
        # Generate innovative solution with our custom model
        outputs = self.model.generate(
            inputs["input_ids"],
            max_length=max_length,
            temperature=0.7,
            top_p=0.85,
            do_sample=True
        )
        
        solution = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return solution.split("Solution:")[1].strip()

# Implementation example
ai = SolvingTomorrowAI()
solution = ai.generate_solution("Optimize customer support with AI")