# -*- coding: utf-8 -*-
"""L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B.ipynb

# Local Inference on GPU

Model page: https://huggingface.co/DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B

"""

# Install transformers library
# This command should be run in a Colab cell, not directly in a Python script.
# !pip install -U transformers

from transformers import pipeline

# Load the model
pipeline = pipeline("text-generation", model="DavidAU/L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B")

def chat_with_model(prompt):
    messages = [
        {"role": "user", "content": prompt},
    ]
    response = pipeline(messages)
    # The response structure might vary, so we'll try to extract the content safely.
    # This assumes the last generated text is the bot's response.
    if response and isinstance(response, list) and len(response) > 0:
        generated_text = response[0].get("generated_text")
        if generated_text and isinstance(generated_text, list) and len(generated_text) > 0:
            last_message = generated_text[-1]
            if isinstance(last_message, dict) and "content" in last_message:
                return last_message["content"]
    return "Sorry, I couldn't generate a response."

# Example usage in a Colab cell:
# print(chat_with_model("Hello, how are you?"))
# print(chat_with_model("Tell me a story."))

# To make it interactive in a Colab environment, you would typically use
# input() within a loop in a separate cell, or use Colab's form features.
# For a simple demonstration within the notebook:

user_query_1 = "Hello, how are you?"
bot_response_1 = chat_with_model(user_query_1)
print(f"User: {user_query_1}")
print(f"Bot: {bot_response_1}")

user_query_2 = "What can you do?"
bot_response_2 = chat_with_model(user_query_2)
print(f"User: {user_query_2}")
print(f"Bot: {bot_response_2}")