| import gradio as gr |
| from datasets import load_dataset |
| from itertools import chain |
| from difflib import get_close_matches |
| import random |
|
|
| |
| 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))) |
|
|
| |
| 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}" |
|
|
| |
| 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() |
|
|
