app_flash.py

import gradio as gr
from transformers import AutoTokenizer
from flashpack.integrations.transformers import FlashPackTransformersModelMixin
from transformers import AutoModelForCausalLM, pipeline as hf_pipeline

# ============================================================
# 1️⃣ Define FlashPack-enabled model class
# ============================================================
class FlashPackGemmaModel(AutoModelForCausalLM, FlashPackTransformersModelMixin):
    """Gemma 3 model wrapped with FlashPackTransformersModelMixin"""
    pass

# ============================================================
# 2️⃣ Load tokenizer
# ============================================================
MODEL_ID = "gokaygokay/prompt-enhancer-gemma-3-270m-it"
FLASHPACK_REPO = "rahul7star/FlashPack"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# ============================================================
# 3️⃣ Load or create FlashPack model
# ============================================================
try:
    print("📂 Loading model from FlashPack repository...")
    model = FlashPackGemmaModel.from_pretrained_flashpack(FLASHPACK_REPO)
except FileNotFoundError:
    print("⚠️ FlashPack model not found. Loading from HF Hub and uploading FlashPack...")
    model = FlashPackGemmaModel.from_pretrained(MODEL_ID)
    model.save_pretrained_flashpack(FLASHPACK_REPO, push_to_hub=True)
    print(f"✅ FlashPack model uploaded to Hugging Face Hub: {FLASHPACK_REPO}")

# ============================================================
# 4️⃣ Build text-generation pipeline
# ============================================================
pipe = hf_pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto"
)

# ============================================================
# 5️⃣ Define prompt enhancement function
# ============================================================
def enhance_prompt(user_prompt, temperature, max_tokens, chat_history):
    chat_history = chat_history or []

    messages = [
        {"role": "system", "content": "Enhance and expand the following prompt with more details and context:"},
        {"role": "user", "content": user_prompt},
    ]

    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    outputs = pipe(
        prompt,
        max_new_tokens=int(max_tokens),
        temperature=float(temperature),
        do_sample=True
    )
    enhanced = outputs[0]["generated_text"].strip()

    chat_history.append({"role": "user", "content": user_prompt})
    chat_history.append({"role": "assistant", "content": enhanced})
    return chat_history

# ============================================================
# 6️⃣ Gradio UI
# ============================================================
with gr.Blocks(title="Prompt Enhancer – Gemma 3 270M", theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # ✨ Prompt Enhancer (Gemma 3 270M)
        Enter a short prompt, and the model will **expand it with details and creative context**  
        using the Gemma chat-template interface.
        """
    )

    with gr.Row():
        chatbot = gr.Chatbot(height=400, label="Enhanced Prompts", type="messages")
        with gr.Column(scale=1):
            user_prompt = gr.Textbox(
                placeholder="Enter a short prompt...",
                label="Your Prompt",
                lines=3,
            )
            temperature = gr.Slider(0.0, 1.0, value=0.7, step=0.05, label="Temperature")
            max_tokens = gr.Slider(32, 256, value=128, step=16, label="Max Tokens")
            send_btn = gr.Button("🚀 Enhance Prompt", variant="primary")
            clear_btn = gr.Button("🧹 Clear Chat")

    # Bind UI actions
    send_btn.click(enhance_prompt, [user_prompt, temperature, max_tokens, chatbot], chatbot)
    user_prompt.submit(enhance_prompt, [user_prompt, temperature, max_tokens, chatbot], chatbot)
    clear_btn.click(lambda: [], None, chatbot)

    gr.Markdown(
        """
        ---
        💡 **Tips:**
        - Works best with short, descriptive prompts (e.g., "a cat sitting on a chair")
        - Increase *Temperature* for more creative output.
        """
    )

# ============================================================
# 7️⃣ Launch
# ============================================================
if __name__ == "__main__":
    demo.launch(show_error=True)