import gradio as gr
from datasets import load_dataset
from itertools import chain
from difflib import get_close_matches
import random

# Load and clean dataset
dataset = load_dataset("asuender/motivational-quotes", "quotes_extended", split="train")

quotes = [item["quote"] for item in dataset if "quote" in item]
authors = [item["author"] if item.get("author") else "Unknown" for item in dataset]
tags_list = [item["tags"].split(", ") if item.get("tags") else [] for item in dataset]
all_tags = sorted(set(chain.from_iterable(tags_list)))

# Quote search logic
def recommend_quote(user_input):
    if not user_input:
        return "Please enter a category."

    matched = get_close_matches(user_input.lower(), all_tags, n=1, cutoff=0.4)
    if not matched:
        return f"😕 No quotes found for a category like '{user_input}'. Try something else."

    selected_tag = matched[0]

    matches = [
        (q, a) for q, a, t in zip(quotes, authors, tags_list)
        if selected_tag in [tag.lower() for tag in t]
    ]

    if not matches:
        return f"😕 No quotes found for the tag '{selected_tag}'."

    quote, author = random.choice(matches)
    return f"“{quote}”\n\n– {author}"

# UI
gr.Interface(
    fn=recommend_quote,
    inputs=gr.Textbox(
        label="Type a category like 'hope', 'courage', 'resilience'...",
        placeholder="e.g. hope"
    ),
    outputs="text",
    title="MoodMatch",
    description="Get inspiring quotes by typing any theme or category — even partial matches work."
).launch()