Create app.py

app.py

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+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()