Update app.py

app.py

@@ -1,10 +1,9 @@
 import streamlit as st
-from transformers import pipeline, AutoTokenizer
+from transformers import pipeline
 from langchain_core.output_parsers import StrOutputParser
 
-# Initialize the tokenizer and text generation pipeline
+# Initialize the text generation pipeline
 model_name = "Qwen/Qwen2.5-0.5B-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
 pipe = pipeline("text-generation", model=model_name, device=-1)
 parser = StrOutputParser()
 
@@ -20,11 +19,8 @@ if st.button("Generate"):
     user_messages = user_input.splitlines()
     messages = [message.strip() for message in user_messages if message.strip()]
 
-    # Tokenize messages
-    tokenized_inputs = [tokenizer(message, return_tensors='pt') for message in messages]
-
-    # Process tokenized messages in a batch
-    outputs = [pipe(input_ids=tokenized_input['input_ids'], max_new_tokens=50)[0] for tokenized_input in tokenized_inputs]
+    # Process messages in a batch
+    outputs = pipe(messages, max_new_tokens=50)  # Adjust max_new_tokens as needed
 
     # Display the generated text for each input message
     st.write("Generated Responses:")