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()