app.py

import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained model and tokenizer from Hugging Face
model_name = "gpt2"  # You can use other models like "gpt-neo-2.7B" if you need a more powerful bot

tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def chat_with_bot(input_text):
    # Tokenize the input and encode it as tensor
    inputs = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
    
    # Generate a response from the model
    with torch.no_grad():
        outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
    
    # Decode the generated tokens back to text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return response

# Define the Gradio interface
iface = gr.Interface(fn=chat_with_bot, 
                     inputs="text", 
                     outputs="text", 
                     title="Chatbot", 
                     description="A simple chatbot using GPT-2")

iface.launch()