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import gradio as gr
from fuzzywuzzy import process
from transformers import pipeline
# Our dictionary of 20 dental terms
dental_terms = {
"cavity": "A cavity is a hole in a tooth caused by decay.",
"gingivitis": "Gingivitis is the inflammation of the gums, often caused by plaque buildup.",
"implant": "A dental implant is a surgical component that interfaces with the jawbone to support a dental prosthesis.",
"orthodontics": "Orthodontics is a branch of dentistry that corrects teeth and jaw alignment issues.",
"plaque": "Plaque is a sticky, colorless film of bacteria that forms on teeth.",
"enamel": "Enamel is the hard, outer surface layer of your teeth that protects against decay.",
"braces": "Braces are orthodontic devices used to straighten teeth and correct bite issues.",
"root canal": "A root canal is a treatment to repair and save a badly damaged or infected tooth.",
"crown": "A crown is a dental cap placed over a tooth to restore its shape, size, and strength.",
"veneers": "Veneers are thin shells placed over the front of teeth to improve appearance.",
"halitosis": "Halitosis is chronic bad breath caused by bacteria or other factors.",
"periodontitis": "Periodontitis is a serious gum infection that damages gums and can destroy the jawbone.",
"denture": "Dentures are removable appliances that replace missing teeth and surrounding tissues.",
"bridge": "A dental bridge is a fixed prosthetic device that replaces missing teeth.",
"tartar": "Tartar is hardened plaque that forms on teeth and can only be removed by a dentist.",
"x-ray": "A dental x-ray is an imaging technique used to view the inside of teeth and surrounding tissues.",
"flossing": "Flossing is the process of cleaning between your teeth with dental floss.",
"sealant": "A sealant is a protective coating applied to teeth to prevent decay.",
"bitewing": "A bitewing is a type of dental x-ray that shows the upper and lower back teeth.",
"occlusion": "Occlusion refers to the alignment and contact between teeth when the jaws close."
}
# Set up a gpt2-large pipeline
generation_pipeline = pipeline(
"text-generation",
model="gpt2-large"
)
def chatbot_response(message, history):
"""
Hybrid response logic:
1) Check if user input matches a known dental term (exactly or via fuzzy matching).
2) If found or close match, return the definition from our dictionary.
3) Otherwise, use GPT-2 to generate an open-ended response.
"""
# Normalize user input to lowercase for simpler matching
user_input_lower = message.lower()
# 1) Exact match check
if user_input_lower in dental_terms:
return dental_terms[user_input_lower]
# 2) Fuzzy match check
closest_match, score = process.extractOne(user_input_lower, dental_terms.keys())
if score >= 80:
return f"Did you mean '{closest_match}'? {dental_terms[closest_match]}"
# 3) If no match or fuzzy match is too low, use GPT-2 for generation
try:
result = generation_pipeline(message, max_length=100, num_return_sequences=1)
generated_text = result[0]["generated_text"]
except Exception as e:
generated_text = f"Error generating response: {str(e)}"
return generated_text
# Gradio chat interface
demo = gr.ChatInterface(
fn=chatbot_response,
title="Hybrid Dental Terminology Chatbot",
description=(
"Enter a dental term to get its definition (20 known terms). "
"If the term isn't recognized, GPT-2 will respond with a generated message."
)
)
if __name__ == "__main__":
demo.launch()
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