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2a66c77 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | 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()
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