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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer from Hugging Face
model_name = "meta-llama/Llama-3.2-1b-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
# Chat history
chat_history = []
def generate_response(message, history):
# Combine message with history
prompt = ""
for user, bot in history:
prompt += f"<|user|>{user}<|end|><|assistant|>{bot}<|end|>"
prompt += f"<|user|>{message}<|end|><|assistant|>"
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=200,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's latest message
response = result.split("<|assistant|>")[-1].strip()
history.append((message, response))
return response, history
# Gradio UI
chatbot = gr.ChatInterface(fn=generate_response,
title="Llama 3.2 Chatbot",
chatbot=gr.Chatbot(),
textbox=gr.Textbox(placeholder="Ask me anything...", lines=2),
clear_btn="Clear")
chatbot.launch()
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