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import gradio as gr
import os
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load your model and tokenizer
model_name = "rshaikh22/coachcarellm" # Replace with your actual model repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Move model to appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def respond(message, history):
input_text = message
inputs = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=1000, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
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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