import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()


class Humanities2025:
    def __init__(self):
        self.instructor = "Mr. B"
        self.class_pet = "Cosmo 🐶"
        self.email_limit = 5
        self.allow_late_work = False
        self.canvas_notifications_required = True
        self.ai_use_policy = {
            "emails": True,
            "exams": False,
            "assignments": "≤5% AI content allowed"
        }
        self.exam_policy = {
            "attempts": 1,
            "makeups": False,
            "cheating_consequence": "RD - Report Delayed"
        }
        self.assignment_submission = "Turnitin Only"
        self.app = "Classroom Companion (Cosmo)"
    
    def email_guidelines(self):
        print("Emails must be grammatically correct, detailed, and under 5 per semester.")
    
    def cheating_policy(self):
        print("Cheating = Academic Misconduct Report + Potential RD Grade.")
    
    def class_tools(self):
        return ["Canvas App", "Classroom Companion App", "Turnitin"]