import re
import nltk
import gradio as gr
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

nltk.download("stopwords", quiet=True)

faq_data = {
    "What is Artificial Intelligence?":"Artificial Intelligence (AI) is the simulation of human intelligence by machines.",
    "What is Machine Learning?":"Machine Learning is a subset of AI that enables computers to learn from data.",
    "What is Deep Learning?":"Deep Learning is a branch of Machine Learning based on neural networks.",
    "What is NLP?":"Natural Language Processing enables computers to understand human language.",
    "What is Python?":"Python is a programming language widely used for AI and web development.",
    "Who developed Python?":"Python was created by Guido van Rossum.",
    "What is Hugging Face?":"Hugging Face is a platform for machine learning models and datasets.",
    "What is Google Colab?":"Google Colab is a free cloud notebook for Python.",
    "What is Gradio?":"Gradio lets you build web interfaces for Python models.",
    "What is cosine similarity?":"Cosine similarity measures similarity between text vectors."
}

stop_words=set(stopwords.words("english"))

def preprocess(text):
    text=text.lower()
    text=re.sub(r"[^a-z0-9 ]"," ",text)
    return " ".join([w for w in text.split() if w not in stop_words])

questions=list(faq_data.keys())
answers=list(faq_data.values())

vectorizer=TfidfVectorizer()
X=vectorizer.fit_transform([preprocess(q) for q in questions])

def chatbot(msg,history):
    if not msg.strip():
        return "",history
    vec=vectorizer.transform([preprocess(msg)])
    sims=cosine_similarity(vec,X)[0]
    idx=sims.argmax()
    if sims[idx] < 0.25:
        ans="Sorry, I couldn't find a relevant answer."
    else:
        ans=answers[idx]
    history=history or []
    history.append({"role":"user","content":msg})
    history.append({"role":"assistant","content":ans})
    return "",history

css="""
.gradio-container{max-width:1000px!important}
footer{display:none}
"""

with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 FAQ Chatbot\nAsk a question from the FAQ database.")
    chat=gr.Chatbot(type="messages",height=450)
    with gr.Row():
        txt=gr.Textbox(placeholder="Ask your question...",scale=8)
        btn=gr.Button("Send")
    clr=gr.Button("Clear")
    gr.Examples(
        examples=[["What is AI?"],["What is Python?"],["What is Hugging Face?"]],
        inputs=txt
    )
    btn.click(chatbot,[txt,chat],[txt,chat])
    txt.submit(chatbot,[txt,chat],[txt,chat])
    clr.click(lambda:("",[]),outputs=[txt,chat])

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