app.py

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

# Pre-download the model and tokenizer
model_name = "gpt2"
GPT2Tokenizer.from_pretrained(model_name)
GPT2LMHeadModel.from_pretrained(model_name)

# Load the pipeline with additional parameters
generator = pipeline(
    'text-generation',
    model=model_name,
    do_sample=True,  # Enables sampling for more creative output
    temperature=0.7,  # Controls randomness (0.7 is balanced for creativity and coherence)
    top_k=50,  # Limits the vocabulary to the top 50 most likely tokens
    max_length=150  # Increased length for longer responses
)

# Function to generate text
def generate_text(prompt):
    output = generator(prompt, num_return_sequences=1)
    return output[0]['generated_text']

# Build the interface
iface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter a prompt here..."),
    outputs="text",
    title="AI Text Generator",
    description="Generate text with AI!"
)

# Launch it
iface.launch()