Update app.py

app.py

@@ -1,64 +1,56 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        response += token
-        yield response
-
+from transformers import pipeline
+
+# Initialize the Hugging Face pipeline with a more advanced model
+# Replace "EleutherAI/gpt-neo-2.7B" with other models like "mosaicml/mpt-7b-chat" or "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
+generation_pipeline = pipeline(
+    "text-generation", 
+    model="EleutherAI/gpt-neo-2.7B",  # Replace this with the desired advanced model
+    device=0  # Use GPU if available
+)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+def dental_chatbot_response(message, history):
+    """
+    Responds to user queries with a focus on dental terminology.
+    - Dynamically generates responses using an advanced LLM.
+    - Designed to address dental-related questions or provide general responses.
+    """
+    print(f"User Input: {message}")
+    print(f"Chat History: {history}")
+
+    # Add a prompt to guide the LLM's focus on dental terminology
+    prompt = (
+        f"You are a highly knowledgeable and friendly dental expert chatbot. "
+        f"Provide detailed and accurate explanations of dental terms, procedures, and treatments. "
+        f"If the query is not dental-related, respond helpfully and informatively.\n\n"
+        f"User: {message}\n\n"
+        f"Chatbot:"
+    )
+
+    # Generate a response using the LLM
+    generated = generation_pipeline(
+        prompt,
+        max_length=200,      # Increase max_length for more detailed responses
+        num_return_sequences=1,
+        do_sample=True,
+        top_p=0.9,           # Nucleus sampling for diverse responses
+        top_k=50             # Top-k sampling for quality control
+    )
+    
+    # Extract the chatbot's response
+    ai_response = generated[0]["generated_text"].split("Chatbot:")[1].strip()
+    print(f"Dental Chatbot Response: {ai_response}")
+    return ai_response
+
+# Gradio ChatInterface
 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=dental_chatbot_response,
+    title="Advanced Dental Terminology Chatbot",
+    description=(
+        "Ask me anything about dental terms, procedures, and treatments! "
+        "This chatbot is powered by an advanced LLM for detailed and accurate answers."
+    )
 )
 
-
 if __name__ == "__main__":
     demo.launch()