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from huggingface_hub import InferenceClient |
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from config import BASE_MODEL, MY_MODEL, HF_TOKEN |
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class SchoolChatbot: |
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""" |
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This class is extra scaffolding around a model. Modify this class to specify how the model recieves prompts and generates responses. |
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Example usage: |
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chatbot = SchoolChatbot() |
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response = chatbot.get_response("What schools offer Spanish programs?") |
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""" |
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def __init__(self): |
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""" |
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Initialize the chatbot with a HF model ID |
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""" |
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model_id = MY_MODEL if MY_MODEL else BASE_MODEL |
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self.client = InferenceClient(model=model_id, token=HF_TOKEN) |
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def format_prompt(self, user_input): |
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""" |
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TODO: Implement this method to format the user's input into a proper prompt. |
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This method should: |
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1. Add any necessary system context or instructions |
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2. Format the user's input appropriately |
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3. Add any special tokens or formatting the model expects |
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Args: |
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user_input (str): The user's question about Boston schools |
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Returns: |
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str: A formatted prompt ready for the model |
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Example prompt format: |
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"You are a helpful assistant that specializes in Boston schools... |
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User: {user_input} |
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Assistant:" |
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""" |
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return ( |
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"<|system|>You are a helpful assistant that specializes in Boston public school enrollment.<|end|>\n" |
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f"<|user|>{user_input}<|end|>\n" |
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"<|assistant|>" |
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) |
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def get_response(self, user_input): |
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""" |
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TODO: Implement this method to generate responses to user questions. |
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This method should: |
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1. Use format_prompt() to prepare the input |
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2. Generate a response using the model |
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3. Clean up and return the response |
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Args: |
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user_input (str): The user's question about Boston schools |
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Returns: |
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str: The chatbot's response |
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Implementation tips: |
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- Use self.format_prompt() to format the user's input |
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- Use self.client to generate responses |
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""" |
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prompt = self.format_prompt(user_input) |
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response = self.client.text_generation( |
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prompt, |
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max_new_tokens=512, |
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temperature=0.7, |
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top_p=0.9, |
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stop_sequences=["<|end|>"] |
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) |
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return response.strip() |
