Update app.py

app.py

@@ -1,55 +1,68 @@
 import gradio as gr
 from transformers import pipeline
+import functools
 
-# 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",  
-    device=-1  # Use CPU explicitly
-)
+# Cache the pipeline so it loads only once
+@functools.lru_cache(maxsize=1)
+def load_pipeline():
+    return pipeline("text-generation", model="EleutherAI/gpt-neo-125M", device=-1)
 
 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.
+    Generates responses focused on dental terminology.
     """
-    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:"
+        "You are a highly knowledgeable and friendly dental expert chatbot. "
+        "Provide detailed and accurate explanations of dental terms, procedures, and treatments. "
+        "If the query is not dental-related, respond helpfully and informatively.\n\n"
+        f"User: {message}\n\nChatbot:"
     )
 
-    # Generate a response using the LLM
-    generated = generation_pipeline(
+    gen_pipe = load_pipeline()
+    generated = gen_pipe(
         prompt,
-        max_length=200,      # Increase max_length for more detailed responses
+        max_length=200,      # Adjust for desired detail vs. speed
         num_return_sequences=1,
         do_sample=True,
-        top_p=0.9,           # Nucleus sampling for diverse responses
+        top_p=0.9,           # Nucleus sampling for diverse outputs
         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}")
+    generated_text = generated[0]["generated_text"]
+    # Safely extract the chatbot's response
+    if "Chatbot:" in generated_text:
+        ai_response = generated_text.split("Chatbot:")[1].strip()
+    else:
+        ai_response = generated_text.strip()
     return ai_response
 
-# Gradio ChatInterface
+# Custom CSS to enhance the interface look
+custom_css = """
+body {
+    background: #f8f9fa;
+    font-family: Arial, sans-serif;
+}
+.gradio-container {
+    background: #ffffff;
+    border-radius: 10px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    margin: 20px;
+    padding: 20px;
+}
+.gradio-title, .gradio-description {
+    text-align: center;
+}
+"""
+
+# Define the Gradio ChatInterface with the custom styling
 demo = gr.ChatInterface(
     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."
-    )
+    ),
+    css=custom_css
 )
 
 if __name__ == "__main__":