app.py

# -*- 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