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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load your model and tokenizer locally
model_name = "william590y/AshishGPT" # Replace with your Hugging Face model name
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
print("Model loaded successfully!")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Prepare the input context from history
input_text = system_message + "\n"
for user_input, bot_response in history:
input_text += f"User: {user_input}\nAssistant: {bot_response}\n"
input_text += f"User: {message}\nAssistant:"
# Tokenize input and generate response
inputs = tokenizer(input_text, return_tensors="pt", truncation=True).to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the assistant's response
response = response[len(input_text):].strip()
return response
# Set up the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
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