import torch
from transformers import pipeline
import gradio as gr

# Init pipeline
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", 
                torch_dtype=torch.bfloat16, device_map="auto")

def predict(input_text):
    # Formatting messages for the chatbot
    messages = [
    {
        "role": "system",
        "content": "You are a conversational text generation chatbot.",
    },
    {
        "role": "user",
        "content": "Hello! I am a chatbot designed to generate conversational text. How can I assist you today?",
    }
]

    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    
    # Create answer
    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
    
    # Return geberate text
    return outputs[0]["generated_text"]

# Gradio Config
title = "Conversation style"
description = "Talk to a chatbot that responds like a conversational chat."
examples = [["¿How are you"]]

iface = gr.Interface(
    fn=predict,
    title=title,
    description=description,
    examples=examples,
    inputs=gr.Textbox(label="Your message"),
    outputs=gr.Textbox(label="Answer Chatbot"),
).launch()