import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load pre-trained model (or fine-tuned model)
model_name = "Manasa1/GPT_Finetuned_tweets"  # Replace with the fine-tuned model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Function to generate tweets
def generate_tweet(input_text):
    prompt = ("You are a tech-savvy, forward-thinking individual with a deep understanding of technology, innovation, and cultural trends. "
              "Craft a tweet that reflects insightful commentary, wit, or actionable advice based on the following idea: \"{}\". "
              "Ensure the response is concise, engaging, and suitable for a diverse audience on social media. "
              "Incorporate elements of thought leadership, futuristic perspectives, and practical wisdom where appropriate.").format(input_text)

    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
    outputs = model.generate(inputs['input_ids'], max_length=280, num_return_sequences=1, top_p=0.95, top_k=50)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Extract the tweet text (exclude prompt if included)
    return generated_text.replace(prompt, "").strip()

# Gradio interface
def main():
    with gr.Blocks() as interface:
        gr.Markdown("""
        # Tweet Generator
        Enter a topic or idea, and the AI will craft a tweet inspired by innovative, philosophical, and tech-savvy thought leadership.
        """)

        with gr.Row():
            input_text = gr.Textbox(label="Enter your idea or topic:")
            output_tweet = gr.Textbox(label="Generated Tweet:", interactive=False)

        generate_button = gr.Button("Generate Tweet")

        generate_button.click(generate_tweet, inputs=[input_text], outputs=[output_tweet])

    return interface

# Run Gradio app
if __name__ == "__main__":
    app = main()
    app.launch()