import random
import gradio as gr
import ollama
from huggingface_hub import InferenceClient
import os


def chat_short_story(length, genre, theme, tone, writing_style):
    """
    Generates a creative short story using a Hugging Face model.
    Parameters:
        length (int): The approximate word count for the story.
        genre (str): The genre of the story (e.g., fantasy, mystery).
        theme (str): The central theme of the story.
        tone (str): The tone of the story (e.g., humorous, serious).
        writing_style (str): The writing style (e.g., poetic, conversational).
    Returns:
        str: The generated short story, or an error message if unsuccessful.
    """
    # System message to define the assistant's role
    system_message = (
        "You are a highly creative short story writer capable of crafting stories across any genre. "
        "For every story created, ensure you generate a suitable title."
    )

    # Construct the user prompt
    prompt = (
        f"Write a creative short story of approximately {length} words in the {genre} genre. "
        f"Use a {writing_style} writing style with a {tone} tone. "
        f"The story should revolve around the theme of {theme}. "
        f"Ensure the narrative is compelling and includes a suitable title."
    )

    # Retrieve the Hugging Face API key from the environment
    HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
    if not HUGGINGFACE_API_KEY:
        return "Error: Hugging Face API key not found. Please set the HUGGINGFACE_API_KEY environment variable."

    # Initialize the Hugging Face Inference Client
    try:
        client = InferenceClient(api_key=HUGGINGFACE_API_KEY)
    except Exception as e:
        return f"Error: Failed to initialize Hugging Face client. Details: {e}"

    # Interact with the model
    try:
        result = client.chat.completions.create(
            model="meta-llama/Llama-3.2-3B-Instruct",
            messages=[
                {"role": "system", "content": system_message},
                {"role": "user", "content": prompt},
            ]
        )
    except Exception as e:
        return f"Error: Failed to interact with the model. Details: {e}"

    # Extract the story content from the response
    if "choices" in result and len(result["choices"]) > 0:
        return result["choices"][0]["message"]["content"]
    else:
        return "Error: No story generated. Please check your prompt or model configuration."



# Predefined options
Length = [100, 250, 750]
Length = [l for l in Length if l >= 100]
r_length = random.choice(Length)

Genre = [
    "Fiction", "Nonfiction", "Drama", "Poetry", "Fantasy", "Horror", "Mystery",
    "Science Fiction", "Suspense", "Women's fiction", "Supernatural/Paranormal", "Young adult"
]
r_genre = random.choice(Genre)

Themes = [
    "Love", "Redemption", "Forgiveness", "Coming of age", "Revenge", "Good vs evil",
    "Bravery and hardship", "The power of social status", "The destructive nature of love",
    "The fallibility of the human condition"
]
r_themes = random.choice(Themes)

Writing_Styles = ["Expository", "Narrative", "Descriptive", "Persuasive", "Creative"]
r_Style = random.choice(Writing_Styles)

Tones = ["Formal", "Optimistic", "Worried", "Friendly", "Curious", "Assertive", "Encouraging"]
r_tones = random.choice(Tones)


# Gradio Interface setup
iface = gr.Interface(
    fn=chat_short_story,
    inputs=[
        gr.Slider(value=100, label="Story_Length", minimum=100, maximum=2500),
        gr.Dropdown(label="Story_Genre", choices=Genre),
        gr.Dropdown(label="Story_Theme", choices=Themes),
        gr.Dropdown(label="Writing_Styles", choices=Writing_Styles),
        gr.Dropdown(label="Story_Tone", choices=Tones)
    ],
    outputs=gr.Text(),
    title="Welcome to the Patrick's Story Generator"
)

iface.launch()