Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from fuzzywuzzy import process
 from transformers import pipeline
 
-# 1) Our 20 dental terms:
+# 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.",
@@ -27,7 +27,6 @@ dental_terms = {
 }
 
 # 2) Set up a Transformer-based text generation pipeline
-#    (You can choose any model on Hugging Face; "gpt2" is just an example.)
 generation_pipeline = pipeline("text-generation", model="gpt2")
 
 def chatbot_response(message, history):
@@ -53,11 +52,11 @@ def chatbot_response(message, history):
     print(f"Closest Match: {closest_match}, Score: {score}")
 
     if score >= 80:
-        # We suspect the user intended a known term
+        # 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
-        #    We'll generate a short text response.
+        #    Generate a short text response.
         generated = generation_pipeline(
             message, 
             max_length=100,     # adjust as needed
@@ -67,18 +66,16 @@ def chatbot_response(message, history):
             top_k=50
         )
         ai_response = generated[0]["generated_text"]
-        # Optionally, you might want to trim the prompt out of the generated text,
-        # but here we'll just return it as is.
         print(f"Transformer-based response: {ai_response}")
         return ai_response
 
-# 3) Gradio ChatInterface (or you can use gr.Interface)
+# 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 the term isn't recognized, a transformer-based model will respond :) "
     )
 )