app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load pre-trained GPT-3.5 model and tokenizer (you can replace this with your model)
model_name = "EleutherAI/gpt-neo-2.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(input_text, max_length=50):
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_text,  # Your text generation function
    inputs=gr.Textbox(text="Enter text here..."),  # Text input field
    outputs=gr.Textbox(),  # Display generated text
    live=True  # Real-time updates
)

# Launch the interface
iface.launch()