app.py

import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the language model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Define the coding mode function
def coding_mode(text):
    # Prepend the text with a coding prompt
    prompt = ">>> "
    input_text = prompt + text
    # Generate a response from the model
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output_ids = model.generate(input_ids, max_length=1000, do_sample=True)
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    # Remove the coding prompt from the response
    response = response[len(prompt):]
    return response

# Define the normal mode function
def normal_mode(text):
    # Generate a response from the model
    input_ids = tokenizer.encode(text, return_tensors="pt")
    output_ids = model.generate(input_ids, max_length=1000, do_sample=True)
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return response

# Create the Gradio interface
iface = gr.Interface(
    fn=normal_mode,
    inputs=gr.inputs.Chat(
        placeholder="Enter your message here...",
        prompt="Me: ",
        allow_audio_input=False,
        allow_video_input=False,
    ),
    outputs=gr.outputs.Textbox(placeholder="Output text will appear here..."),
    title="Chatbot",
    description="Enter text and the chatbot will respond!",
    theme="compact",
    layout="vertical",
    allow_flagging=False,
    allow_screenshot=False,
    allow_sharing=False,
    examples=[
        ["Hi there!"],
        ["What's your name?"],
        ["How old are you?"],
        ["What do you like to do for fun?"],
    ],
)

# Add the coding mode toggle to the interface
iface.add_mode("Coding Mode", coding_mode)

# Launch the interface
iface.launch(share=True)