Update app.py

app.py

@@ -1,48 +1,174 @@
+# app.py
+
+!pip install gradio transformers
+
 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")
+# ---------------------------------------------------------
+# 1. Setup: Load a text2text-generation model from Hugging Face
+#    (You can choose another model if you prefer.)
+# ---------------------------------------------------------
+prompt_analyzer = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# ---------------------------------------------------------
+# 2. Define a function to analyze the user’s prompt
+#    - Returns a score (1–10)
+#    - Provides feedback on clarity/creativity/completeness
+#    - Suggests 3 improved versions of the prompt
+# ---------------------------------------------------------
+def analyze_prompt(user_prompt: str):
+    """
+    Uses a text-to-text model to rate the prompt, provide feedback, 
+    and generate three improved prompt suggestions.
+    
+    Parameters:
+    user_prompt (str): The user's original image-generation prompt.
+    
+    Returns:
+    (str, str, str): A tuple containing:
+       1) Score (as text, 1–10),
+       2) Feedback (as a longer text),
+       3) Three improved prompt suggestions (as a single multiline string).
+    """
+    
+    if not user_prompt.strip():
+        return "N/A", "Please enter a valid prompt.", "No suggestions available."
+    
+    # Prepare an instruction to guide the model
+    # The model output will contain rating, feedback, and 3 improvements.
+    instruction = f"""
+You are an expert prompt engineer. Analyze the following prompt for an AI image generation model.
+
+Prompt: {user_prompt}
+
+1. Rate the prompt on a scale of 1 to 10 based on clarity, creativity, and completeness.
+2. Provide a short explanation (feedback) for your rating.
+3. Give three improved versions of the prompt to make it more descriptive or more creative.
 
-# Function to analyze prompt
+Format your response in the following structure:
 
-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."
+Rating: X
+Feedback: <your feedback here>
+Improvements:
+1. <improvement 1>
+2. <improvement 2>
+3. <improvement 3>
+    """
+    
+    # Generate model output
+    model_response = prompt_analyzer(instruction, max_length=200)[0]['generated_text']
+    
+    # Parse the output (very basic parsing logic for demonstration)
+    # We'll look for specific keywords: "Rating:", "Feedback:", "Improvements:"
+    rating = "N/A"
+    feedback = ""
+    suggestions = ""
     
-    # Generate alternative prompts
-    improved_prompts = [
-        prompt + " with cinematic lighting",
-        prompt + " ultra-realistic style",
-        prompt + " high detail, sharp focus"
-    ]
+    # Basic approach to splitting the text
+    for line in model_response.split("\n"):
+        line = line.strip()
+        if line.lower().startswith("rating:"):
+            rating = line.split(":", 1)[1].strip()
+        elif line.lower().startswith("feedback:"):
+            feedback = line.split(":", 1)[1].strip()
+        elif line.lower().startswith("1.") or line.lower().startswith("- 1."):
+            # Start collecting improvements
+            suggestions += line + "\n"
+        elif line.lower().startswith("2.") or line.lower().startswith("- 2."):
+            suggestions += line + "\n"
+        elif line.lower().startswith("3.") or line.lower().startswith("- 3."):
+            suggestions += line + "\n"
+        elif line.lower().startswith("improvements:"):
+            # If the model has a separate "Improvements:" heading
+            suggestions += "\n"
     
-    # Limit score to a 1-10 scale
-    score = min(max(score, 1), 10)
-    return score, feedback, improved_prompts
+    # If the model didn't output lines with numbering, just set a fallback
+    if not suggestions.strip():
+        # We might handle the entire text as suggestions or produce a fallback
+        suggestions = "Could not parse suggestions properly.\n" + model_response
 
-# 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")
+    return rating, feedback, suggestions
 
-        with gr.Row():
-            score_output = gr.Number(label="Prompt Quality Score (1-10)")
-            feedback_output = gr.Textbox(label="Feedback for Improvement")
+# ---------------------------------------------------------
+# 3. Define some example prompts for reference
+# ---------------------------------------------------------
+example_prompts = [
+    "A majestic dragon soaring above a medieval castle, fantasy art style, highly detailed",
+    "A peaceful countryside landscape with rolling hills and a small cottage at sunset",
+    "A cyberpunk city scene with neon lights, flying cars, and towering skyscrapers",
+]
 
-        improved_output = gr.Textbox(label="Improved Prompt Suggestions", lines=3)
+# ---------------------------------------------------------
+# 4. Build the Gradio interface
+# ---------------------------------------------------------
+def set_example_prompt(example):
+    """
+    Utility function to load an example prompt into the text input box.
+    """
+    return example
 
-        submit_button.click(
-            analyze_prompt,
-            inputs=prompt_input,
-            outputs=[score_output, feedback_output, improved_output]
-        )
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Interactive Prompt Engineering App
+        **Learn how to craft better prompts for AI image generation.**
 
-    demo.launch()
+        1. Enter your prompt below.  
+        2. Click "Evaluate Prompt" to get a **score**, **feedback**, and **3 improved prompts**.  
+        3. Use the dropdown to load example prompts for inspiration.  
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            # Dropdown to select an example
+            example_dropdown = gr.Dropdown(
+                label="Choose an example prompt to load",
+                choices=example_prompts,
+                value=None,
+                interactive=True
+            )
+            
+            # Textbox for user prompt input
+            user_prompt_input = gr.Textbox(
+                label="Enter your prompt here:",
+                lines=4,
+                placeholder="E.g. 'A futuristic cityscape with neon lights at night, highly detailed...'"
+            )
+            
+            # Button to set example prompt in the textbox
+            load_example_btn = gr.Button("Load Example Prompt")
+            
+            # Button to analyze the user's prompt
+            analyze_btn = gr.Button("Evaluate Prompt")
+            
+        with gr.Column():
+            score_output = gr.Textbox(
+                label="Prompt Quality Score (1-10)",
+                interactive=False
+            )
+            feedback_output = gr.Textbox(
+                label="Feedback",
+                lines=3,
+                interactive=False
+            )
+            suggestions_output = gr.Textbox(
+                label="Improved Prompt Suggestions",
+                lines=6,
+                interactive=False
+            )
+    
+    # Define the interactions
+    load_example_btn.click(fn=set_example_prompt, 
+                           inputs=[example_dropdown], 
+                           outputs=[user_prompt_input])
+    
+    analyze_btn.click(fn=analyze_prompt, 
+                      inputs=[user_prompt_input], 
+                      outputs=[score_output, feedback_output, suggestions_output])
 
-# Launch the app
-if __name__ == "__main__":
-    main_interface()
+# ---------------------------------------------------------
+# 5. Launch the Gradio app
+# ---------------------------------------------------------
+demo.launch()