app.py

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

# Load the model and tokenizer from Hugging Face's model hub with trust_remote_code=True
model = AutoModelForCausalLM.from_pretrained(
    "EleutherAI/gpt-neo-1.3B", 
    trust_remote_code=True,  # Allow custom code execution
    attn_implementation='eager'
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

def chatbot_response(user_input):
    messages = [{"role": "user", "content": user_input}]  # Create a fresh conversation context
    output = pipe(messages, max_new_tokens=500, return_full_text=False, temperature=0.0, do_sample=False)
    return output[0]['generated_text']

iface = gr.Interface(
    fn=chatbot_response,
    inputs=gr.components.Textbox(lines=2, placeholder="Type your question here..."),
    outputs=gr.components.Text(label="Response"),
    title="Smart Chatbot",
    description="This is a smart chatbot that can answer your questions. Just type your question below and get an instant response.",
    theme="huggingface"
)

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