app.py

import os
import asyncio
import pandas as pd
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr

# Paths
PROMPTS_CSV = "prompts.csv"
MODEL_NAME = "LiquidAI/LFM2-2.6B"

# Check for dataset, download if missing
if not os.path.exists(PROMPTS_CSV):
    print("prompts.csv not found. Downloading dataset from Hugging Face...")
    dataset = load_dataset("fka/awesome-chatgpt-prompts", split="train")
    df = pd.DataFrame(dataset)
    df.to_csv(PROMPTS_CSV, index=False)
    print("Dataset saved to prompts.csv")
else:
    df = pd.read_csv(PROMPTS_CSV)

all_prompts = df['prompt'].tolist()
print(f"Total prompts available: {len(all_prompts)}")

# Load first 20 prompts for fast startup
fast_prompts = all_prompts[:20]
remaining_prompts = all_prompts[20:]

# Load tokenizer and model
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
print(f"Model loaded on {device}")

# Async function to load remaining prompts
async def load_remaining_prompts():
    global fast_prompts
    print("Loading remaining prompts asynchronously...")
    await asyncio.sleep(1)  # simulate async loading
    fast_prompts.extend(remaining_prompts)
    print("All prompts loaded.")

# Function to generate response
def generate_response(prompt, max_tokens=100):
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    outputs = model.generate(**inputs, max_new_tokens=max_tokens)
    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
    return response

# Gradio interface
def chat_with_prompt(prompt_idx):
    prompt = fast_prompts[prompt_idx]
    response = generate_response(prompt)
    return f"Prompt:\n{prompt}\n\nResponse:\n{response}"

with gr.Blocks() as demo:
    gr.Markdown("## ChatGPT Prompt Tester")
    prompt_dropdown = gr.Dropdown(choices=[str(i) for i in range(len(fast_prompts))], label="Select Prompt Index")
    output_text = gr.Textbox(label="Model Response", lines=15)
    prompt_dropdown.change(chat_with_prompt, inputs=prompt_dropdown, outputs=output_text)

# Run async loading in the background
asyncio.create_task(load_remaining_prompts())

# Launch Gradio
demo.launch(server_name="0.0.0.0", server_port=7860)