from langchain import LLM

class DesignModel(LangchainModel):

    def __init__(self):
        super().__init__()
        self.lm = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")

    def forward(self, input_text):
        # Search the web for the given design idea.
        design_ideas = []
        for result in search(input_text):
            design_ideas.append(result.url)

        # Come up with steps to follow to build the design model.
        steps = []
        for design_idea in design_ideas:
            steps.append(self._get_steps_to_build_model(design_idea))

        # Use these steps to build the model's scripting language.
        model_script = self._build_model_script(steps)

        return model_script

    def _get_steps_to_build_model(self, design_idea):
        # Extract the design idea from the URL.
        design_idea = design_idea.split("/")[-1]

        # Search the web for the steps to build the design model.
        steps = []
        for result in search(f"how to build a {design_idea} model"):
            steps.append(result.url)

        # Return the steps to build the design model.
        return steps

    def _build_model_script(self, steps):
        # Create a new file to store the model script.
        with open("model_script.scad", "w") as f:
            for step in steps:
                f.write(step)

        # Return the path to the model script.
        return "model_script.scad" Provide your response in exactly 6 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
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