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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Initialize model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("diabolic6045/ELN-Llama-1B-base")
model = AutoModelForCausalLM.from_pretrained("diabolic6045/ELN-Llama-1B-base")
def generate_response(message, history):
# Format the conversation history
print("here")
conversation = ""
for h in history:
conversation += f"User: {h[0]}\nAssistant: {h[1]}\n"
conversation += f"User: {message}\nAssistant:"
# Tokenize input
inputs = tokenizer(conversation, return_tensors="pt", truncation=True, max_length=512)
# Generate response
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
max_length=200,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
num_return_sequences=1,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
response = response.split("Assistant:")[-1].strip()
return response
# Create the Gradio interface
demo = gr.ChatInterface(
fn=generate_response,
type="messages",
title="LLaMA Chatbot",
description="Chat with the ELN-Llama-1B model. Ask questions or have a conversation!",
examples=[
"What is artificial intelligence?",
"Write a short poem about nature.",
"Explain quantum computing in simple terms.",
],
cache_examples=True,
additional_inputs=[
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
gr.Slider(minimum=50, maximum=500, value=200, step=50, label="Max Length"),
],
retry_btn="Regenerate",
undo_btn="Undo Last",
clear_btn="Clear",
)
if __name__ == "__main__":
demo.launch(share=True) |