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# -*- coding: utf-8 -*-
"""Untitled6.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1F6f_vJbssO7C2FM6FILWljFYacDmbVBY
"""
# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_name = "distilgpt2" # A lightweight, CPU-friendly model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Define the function to generate a response
def generate_response(prompt):
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt")
# Generate a response
outputs = model.generate(
inputs.input_ids,
max_length=50,
do_sample=True, # Enable sampling
temperature=0.7, # Controls randomness
top_p=0.9, # Nucleus sampling
pad_token_id=tokenizer.eos_token_id
)
# Decode the output and set clean_up_tokenization_spaces to True to avoid warnings
response = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
return response
# Example usage
prompt = "I went to Safeway and I bought a"
response = generate_response(prompt)
print(response)
def persona_response(prompt, persona="I am a helpful assistant"):
full_prompt = f"{persona}. {prompt}"
return generate_response(full_prompt)
# Import Gradio
import gradio as gr
# Define Gradio interface function
def chat_interface(user_input, persona="I am a helpful assistant"):
return persona_response(user_input, persona)
# Set up Gradio interface
interface = gr.Interface(
fn=chat_interface,
inputs=["text", "text"], # Allows input for both prompt and persona
outputs="text",
title="Simple Chatbot",
description="Type something to chat with the bot! Add a persona to change its style, like 'I am a shopping assistant.'"
)
# Launch the Gradio interface in Colab
interface.launch(share=True) # share=True creates a public link
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