app.py

#uploud neccessary libaries
import gradio as gr
from transformers import pipeline # to load a pre-trained model.

# Load the text generation pipeline
text_generator = pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")

# function that takes in a prompt and returns generated text
def generate_text(prompt,temperature):
  result = text_generator(prompt,
                          max_length=100,
                          num_return_sequences=1,
                          temperature=temperature,
                          top_k=5,
                          top_p=0.9
                          )
  return result[0]['generated_text']

# Set up the Gradio interface
iface = gr.Interface(
    fn=generate_text, # Function to process input
    inputs=[
        gr.Textbox(label="Provide a query or idea that will serve as the basis for generating text"),
        gr.Slider(minimum=0.1,maximum=1.0,step=0.1,value=0.7,label="Temperature:Lower = More Focused, Higher = More Creative")
    ], # Input: Textbox for user to enter prompt
    outputs=gr.Textbox(label="Generated Text (Based on Your Input and Temperature Setting)"),   # Output: Generated text
    title="Creative Text Generation",
    description="Adjust the parameters to achieve your desired text output",
)

# Launch the interface
iface.launch()