app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Load the model and tokenizer using Hugging Face
#model_name = "microsoft/Phi-3-mini-4k-instruct"


# Explicitly load the tokenizer and model
#tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
#model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# Create the pipeline
chatbot = pipeline("text-generation", model="KingNish/Qwen2.5-0.5b-Test-ft", trust_remote_code=True)
#chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer, framework="pt")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Combine system message and conversation history
    prompt = system_message + "\n"
    prompt += f"User: {message}\n\nBot:"

    # Generate the response using the model
    response = chatbot(prompt, max_length=max_tokens, temperature=temperature, top_p=top_p)[0]['generated_text']
    return response

# Define the Gradio interface with additional inputs
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()