app.py

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

# Load the fine-tuned GPT-2 model and tokenizer
model_dir = "Manasa1/finetuned_GPT23"
fine_tuned_model = GPT2LMHeadModel.from_pretrained(model_dir)
fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(model_dir)

# Create a text-generation pipeline
generator = pipeline('text-generation', model=fine_tuned_model, tokenizer=fine_tuned_tokenizer)

def generate_tweet(input_question):
    # Format the prompt
    prompt = f"Question: {input_question} Answer:"
    # Generate the output
    output = generator(prompt, max_length=100, num_return_sequences=1, temperature=0.9, top_p=0.9)
    # Extract and return the generated text
    return output[0]['generated_text']

# Create the Gradio interface
interface = gr.Interface(
    fn=generate_tweet,
    inputs=gr.Textbox(label="Enter a prompt/question", placeholder="Write a tweet about startup."),
    outputs=gr.Textbox(label="Generated Tweet"),
    title="Tweet Generator",
    description="Generate tweets based on prompts using a fine-tuned GPT-2 model."
)

# Launch the interface
interface.launch()