app.py

import gradio as gr
from transformers import pipeline

# Load a language model for analyzing text quality
nlp_model = pipeline("text-classification", model="roberta-large-mnli")

# Function to analyze prompt

def analyze_prompt(prompt):
    # Simple analysis by length and keyword presence
    score = len(prompt.split()) / 10
    feedback = "Try to use more descriptive words and specific details."
    
    # Generate alternative prompts
    improved_prompts = [
        prompt + " with cinematic lighting",
        prompt + " ultra-realistic style",
        prompt + " high detail, sharp focus"
    ]
    
    # Limit score to a 1-10 scale
    score = min(max(score, 1), 10)
    return score, feedback, improved_prompts

# Gradio Interface
def main_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Interactive Prompt Learning Tool")
        prompt_input = gr.Textbox(label="Enter your prompt for AI image generation")
        submit_button = gr.Button("Analyze Prompt")

        with gr.Row():
            score_output = gr.Number(label="Prompt Quality Score (1-10)")
            feedback_output = gr.Textbox(label="Feedback for Improvement")

        improved_output = gr.Textbox(label="Improved Prompt Suggestions", lines=3)

        submit_button.click(
            analyze_prompt,
            inputs=prompt_input,
            outputs=[score_output, feedback_output, improved_output]
        )

    demo.launch()

# Launch the app
if __name__ == "__main__":
    main_interface()