| import gradio as gr |
| import torch |
| import re |
| from transformers import pipeline |
|
|
| |
| |
| |
| MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct" |
|
|
| print(f"Starting model load: {MODEL_NAME}...") |
| try: |
| dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
| device = 0 if torch.cuda.is_available() else -1 |
|
|
| generator = pipeline( |
| "text-generation", |
| model=MODEL_NAME, |
| torch_dtype=dtype, |
| device=device |
| ) |
| MODEL_LOADED = True |
| print("Model loaded successfully!") |
| except Exception as e: |
| print(f"Error loading model: {e}") |
| MODEL_LOADED = False |
|
|
| |
| |
| |
| def process_prompt(user_input, task): |
| """ |
| Evaluates, scores, and rewrites the user's prompt by forcing a strict |
| Persona and Structure using clear few-shot examples for the small model. |
| """ |
| if not user_input or not user_input.strip(): |
| return "N/A", "Please enter a prompt.", "No input provided." |
| |
| if not MODEL_LOADED: |
| return ( |
| "Error", |
| "Error: Model failed to load offline.", |
| "Please check the server logs or try a different lightweight model." |
| ) |
|
|
| |
| system_message = ( |
| "You are an expert Prompt Engineer. Your task is to critically evaluate the user's prompt, " |
| "give it an honest and strict quality score out of 100, and rewrite it into a highly effective prompt.\n\n" |
| "CRITICAL RULES:\n" |
| "1. Assign a Persona/Role: The rewritten prompt MUST start immediately by defining an expert role for the AI " |
| "(e.g., 'Act as an expert [Role]...', 'You are a professional [Role]...').\n" |
| "2. Add Structure & Details: Expand the prompt by adding specific guidelines, constraints, and target output format.\n" |
| "3. Strict Scoring: Be very critical. Vague or single-sentence prompts (e.g., 'explain quantum physics', 'write a story') " |
| "are extremely weak and MUST get a low score between 15 and 45. Only highly structured prompts with roles get 80+.\n\n" |
| "You MUST format your output EXACTLY like this template. Do not add any extra intro or outro text:\n" |
| "[SCORE]\n<numerical score only, e.g., 30>\n" |
| "[IMPROVED]\n<The complete rewritten prompt starting with the Persona/Role>\n" |
| "[EXPLANATION]\n<Short bullet points explaining the improvements>\n\n" |
| "--- EXAMPLE OF EXPECTED BEHAVIOR ---\n" |
| "User Input: write a blog about AI\n" |
| "Output:\n" |
| "[SCORE]\n" |
| "30\n" |
| "[IMPROVED]\n" |
| "Act as an expert technology copywriter. Write a highly engaging 600-word blog post about how generative AI tools are changing daily office productivity. Use an informative yet professional tone. Structure the post with a compelling hook in the introduction, three practical use-cases with bullet points, and a forward-looking conclusion.\n" |
| "[EXPLANATION]\n" |
| "- Assigned the role of an expert tech copywriter to give the AI context.\n" |
| "- Added specific constraints (600 words) and tone instruction (professional).\n" |
| "- Outlined a clear structure (introduction, use-cases, conclusion) to ensure organized output." |
| ) |
| |
| user_message = f"Task: {task}\nOriginal Prompt: {user_input}" |
|
|
| messages = [ |
| {"role": "system", "content": system_message}, |
| {"role": "user", "content": user_message} |
| ] |
|
|
| try: |
| |
| prompt_text = generator.tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| |
| |
| outputs = generator( |
| prompt_text, |
| max_new_tokens=350, |
| temperature=0.4, |
| do_sample=True, |
| return_full_text=False |
| ) |
| |
| response = outputs[0]["generated_text"].strip() |
| |
| |
| parts = re.split(r'\[SCORE\]|\[IMPROVED\]|\[EXPLANATION\]', response, flags=re.IGNORECASE) |
| |
| score = "⭐ N/A" |
| improved_text = "" |
| explanation_text = "" |
|
|
| if len(parts) >= 4: |
| score_num = parts[1].strip() |
| |
| score_clean = re.search(r'\d+', score_num) |
| score = f"⭐ {score_clean.group(0) if score_clean else score_num} / 100" |
| |
| improved_text = parts[2].strip() |
| explanation_text = parts[3].strip() |
| else: |
| |
| score_match = re.search(r'(?:SCORE:?)\s*(\d+)', response, re.IGNORECASE) |
| score = f"⭐ {score_match.group(1)} / 100" if score_match else "⭐ 35 / 100" |
| |
| if "[IMPROVED]" in response.upper(): |
| temp = re.split(r'\[IMPROVED\]', response, flags=re.IGNORECASE)[1] |
| if "[EXPLANATION]" in temp.upper(): |
| improved_text = re.split(r'\[EXPLANATION\]', temp, flags=re.IGNORECASE)[0].strip() |
| explanation_text = re.split(r'\[EXPLANATION\]', temp, flags=re.IGNORECASE)[1].strip() |
| else: |
| improved_text = temp.strip() |
| explanation_text = "Prompt restructured with role and context." |
| else: |
| improved_text = response |
| explanation_text = "Prompt successfully optimized." |
| |
| return score, improved_text, explanation_text |
|
|
| except Exception as e: |
| return "Error", f"Generation Error: {str(e)}", "An error occurred during inference." |
|
|
|
|
| |
| |
| |
| custom_theme = gr.themes.Soft(primary_hue="blue") |
|
|
| with gr.Blocks(title="Prompt Assistant") as app: |
| |
| |
| gr.Markdown( |
| """ |
| # 🚀 Prompt Assistant |
| ### Your personal AI Prompt Engineer |
| Welcome to the **Prompt Assistant**! This tool helps students, developers, and researchers write better, clearer, and more structured prompts for Large Language Models. |
| Select a task, enter your raw idea, and let the assistant craft a professional prompt for you. |
| """ |
| ) |
| |
| with gr.Row(): |
| with gr.Column(scale=1): |
| |
| task_selector = gr.Dropdown( |
| choices=[ |
| "Improve Prompt", |
| "Make More Detailed", |
| "Summarize Prompt", |
| "Translate Prompt", |
| "Generate Better Version", |
| "Explain Improvements" |
| ], |
| value="Improve Prompt", |
| label="What would you like to do?", |
| interactive=True |
| ) |
| user_prompt_input = gr.Textbox( |
| lines=6, |
| label="Your Original Prompt", |
| placeholder="Type your basic prompt or idea here... (e.g., 'write an email to my boss about a vacation')" |
| ) |
| |
| with gr.Row(): |
| clear_btn = gr.Button("Clear", variant="secondary") |
| generate_btn = gr.Button("Generate", variant="primary") |
| |
| with gr.Column(scale=1): |
| |
| score_output = gr.Textbox( |
| label="Original Prompt Quality Score", |
| value="N/A", |
| interactive=False |
| ) |
| improved_prompt_output = gr.Textbox( |
| lines=5, |
| label="Ready-to-Use Prompt (With Persona & Context)", |
| buttons=["copy"], |
| interactive=False |
| ) |
| explanation_output = gr.Textbox( |
| lines=3, |
| label="Improvements & Explanation", |
| interactive=False |
| ) |
|
|
| |
| gr.Markdown("### 💡 Ready-to-use Examples") |
| gr.Examples( |
| examples=[ |
| ["write a blog about AI", "Make More Detailed"], |
| ["explain quantum physics", "Improve Prompt"], |
| ["give me python code for a calculator", "Generate Better Version"], |
| ], |
| inputs=[user_prompt_input, task_selector], |
| outputs=[score_output, improved_prompt_output, explanation_output], |
| fn=process_prompt, |
| cache_examples=False |
| ) |
| |
| |
| gr.Markdown( |
| """ |
| --- |
| **Prompt Assistant** | Built for Educational AI Bootcamps | *Powered by open-source models on Hugging Face* |
| """ |
| ) |
| |
| |
| generate_btn.click( |
| fn=process_prompt, |
| inputs=[user_prompt_input, task_selector], |
| outputs=[score_output, improved_prompt_output, explanation_output] |
| ) |
| |
| clear_btn.click( |
| fn=lambda: ("", "N/A", "", "", "Improve Prompt"), |
| inputs=None, |
| outputs=[user_prompt_input, score_output, improved_prompt_output, explanation_output, task_selector] |
| ) |
|
|
| |
| |
| |
| if __name__ == "__main__": |
| app.launch(theme=custom_theme) |
