app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Specify the path to your fine-tuned model and tokenizer
model_path = "./"  # Assuming the model is in the same directory as your notebook
model_name = "pytorch_model-00001-of-00002.bin"  # Replace with your model name

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Define the function for text generation
def generate_text(input_text):
    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Create the Gradio interface
text_generation_interface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(label="Input Text"),
    ],
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title="GPT-4 Text Generation",
)

# Launch the Gradio interface
text_generation_interface.launch()