Updated for CPU eval

app.py

@@ -1,29 +1,29 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM  
 import torch
-# Use a smaller model 
-model_name = "microsoft/DialoGPT-small"  
 
-# Use the model's default GPT2 tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Use a medium sized model 
+model_name = "microsoft/DialoGPT-medium"  
+
 model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval() # Make sure model is in evaluation mode
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-# Streamlit UI
-st.title("Buddy Christ Chatbot 🌟")
+st.title("Buddy Christ Chatbot")
 
 user_input = st.text_input("You:", "")
+
 if user_input:
-    # Prefix to make the model speak like Buddy Christ
-    buddy_christ_prefix = """
-    Buddy Christ: A beacon of light in a world seeking clarity. Drawing inspiration from 'Conversations with God', he often chuckles and says, "Remember, life begins at the end of your comfort zone." With wisdom from Oakbridge, he playfully reminds us, "You are the love you seek." Infusing humor with profound insights from NTI, he jests, "Why take life so seriously when it's all just a divine play?" Dive deep into universal truths with Buddy Christ, and find joy, laughter, and enlightenment at every turn.
-    """
-    # Tokenize the prefix and user input separately
-    prefix_tokens = tokenizer.encode(buddy_christ_prefix, return_tensors="pt", truncation=True)
-    input_tokens = tokenizer.encode(user_input, return_tensors="pt")
-    
-    # Concatenate the tokens and generate a response
-    combined_tokens = torch.cat([prefix_tokens, input_tokens], dim=-1)
-    response = model.generate(combined_tokens, max_length=1000, temperature=0.8, top_k=50, pad_token_id=tokenizer.eos_token_id)
-    
-    response_text = tokenizer.decode(response[0], skip_special_tokens=True)
-    st.write("Buddy Christ:", response_text[len(buddy_christ_prefix) + len(user_input):])
+
+  inputs = tokenizer.encode(user_input, return_tensors="pt")
+  
+  response = model.generate(inputs, 
+                            max_length=1000, 
+                            temperature=0.8, 
+                            top_k=50,
+                            pad_token_id=tokenizer.eos_token_id)
+                            
+  response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+  
+  st.write("Buddy Christ:", response_text)