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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("umar141/Gemma_1B_Baro_v2_vllm")
model = AutoModelForCausalLM.from_pretrained("umar141/Gemma_1B_Baro_v2_vllm")
# Streamlit page configuration
st.set_page_config(page_title="Gemma-based Chatbot", page_icon=":robot:")
# Introduction text
st.title("Gemma-based Chatbot")
st.write("This is a chatbot powered by a fine-tuned Gemma model.")
# User input
user_input = st.text_input("Ask me anything:")
# Generate response when the user inputs a query
if user_input:
# Tokenize input and generate model response
inputs = tokenizer.encode(user_input, return_tensors="pt")
outputs = model.generate(inputs, max_length=150, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)
# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the response
st.write("AI Response:")
st.write(response)
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