app.py

import gradio as gr
from fuzzywuzzy import process
from transformers import pipeline

# 1) 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."
}

# 2) Set up a Transformer-based text generation pipeline
generation_pipeline = pipeline("text-generation", model="gpt2")

def chatbot_response(message, history):
    """
    A hybrid response function:
    - Check if the user query matches a known dental term (direct or fuzzy).
    - If not matched, use a transformer model to generate an open-ended response.
    """
    print(f"User Input: {message}")
    print(f"Chat History: {history}")

    # Lowercase for simpler matching
    input_lower = message.lower()

    # 1) Check for exact match
    if input_lower in dental_terms:
        response = dental_terms[input_lower]
        print(f"Exact Match Response: {response}")
        return response

    # 2) Fuzzy matching for approximate matches
    closest_match, score = process.extractOne(input_lower, dental_terms.keys())
    print(f"Closest Match: {closest_match}, Score: {score}")

    if score >= 80:
        # Suspect the user intended a known term
        return f"Did you mean '{closest_match}'? {dental_terms[closest_match]}"
    else:
        # 3) If no good match, let transformer-based AI handle it
        #    Generate a short text response.
        generated = generation_pipeline(
            message, 
            max_length=100,     # adjust as needed
            num_return_sequences=1,
            do_sample=True,
            top_p=0.9,
            top_k=50
        )
        ai_response = generated[0]["generated_text"]
        print(f"Transformer-based response: {ai_response}")
        return ai_response

# 3) Gradio ChatInterface
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, a transformer-based model will respond :) "
    )
)

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