Update app.py

app.py

@@ -1,64 +1,86 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from fuzzywuzzy import process
+from transformers import pipeline
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# 1) Our 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
+#    (You can choose any model on Hugging Face; "gpt2" is just an example.)
+generation_pipeline = pipeline("text-generation", model="gpt2")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+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}")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Lowercase for simpler matching
+    input_lower = message.lower()
 
-    messages.append({"role": "user", "content": message})
+    # 1) Check for exact match
+    if input_lower in dental_terms:
+        response = dental_terms[input_lower]
+        print(f"Exact Match Response: {response}")
+        return response
 
-    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}")
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    if score >= 80:
+        # We 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.
+        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"]
+        # 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
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# 3) Gradio ChatInterface (or you can use gr.Interface)
 demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    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()