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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
model_id = "google/gemma-7b" # Replace with "google/gemma-7b-it" for instruction tuning
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Function to generate responses based on user messages
def generate_response(messages):
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids, max_new_tokens=100)
generated_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_response
# Streamlit app
st.title("Gemma Chatbot")
messages = []
user_input = st.text_input("You:", "")
if st.button("Send"):
if user_input:
messages.append({"role": "user", "content": user_input})
bot_response = generate_response(messages)
messages.append({"role": "assistant", "content": bot_response})
else:
st.warning("Please enter a message.")
# Display conversation
for message in messages:
if message["role"] == "user":
st.text_input("You:", value=message["content"], disabled=True)
elif message["role"] == "assistant":
st.text_area("Gemma:", value=message["content"], disabled=True)
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