app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer from Hugging Face
model_name = "iqrabatool/finetuned_LLaMA"
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def respond(message, system_message, max_tokens, temperature, top_p):
    # Generate response
    inputs = tokenizer(message, return_tensors="pt", max_length=max_tokens, truncation=True, padding=True)
    outputs = model.generate(**inputs, temperature=temperature, top_p=top_p)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Define interface components
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)"),
]

# Create the ChatInterface
demo = gr.Interface(
    fn=respond,
    inputs=["text", "text", "number", "number", "number"],
    outputs="text",
    title="Health Bot",
    description="A chatbot for health-related inquiries.",
    article="The Health Bot assists users with health-related questions and provides information based on a pre-trained language model.",
    examples=[["What are the symptoms of COVID-19?", "Health Bot: COVID-19 symptoms include..."]],
    additional_inputs=additional_inputs
)

if __name__ == "__main__":
    demo.launch()