app.py

import subprocess
import sys
import os

# Install required packages directly in the app
def install(package):
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

print("Installing required packages...")
install("transformers==4.56.0")
install("torch==2.3.0")
install("peft==0.12.0")
install("accelerate==0.28.0")
install("bitsandbytes==0.43.0")
install("sentencepiece")
install("protobuf")
install("gradio==5.27.0")

print("All packages installed. Loading model...")

# Now import after installation
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = "google/gemma-2-2b"
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
    use_cache=False
)

# Load your fine-tuned adapter
model = PeftModel.from_pretrained(model, "KlemGunn0519/Mighty_Mountain_Ski_Resort")

def respond(message, history):
    prompt = f"### Instruction\n{message}\n\n### Response"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Response")[-1].strip()

# Create Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    title="🎿 Mighty Mountain Ski Resort Assistant",
    description="Ask about lift tickets, trail conditions, hours, and more!",
    examples=[
        "What are daily lift ticket prices?",
        "What's the best beginner slope?",
        "Do you offer ski rentals?",
        "When does the resort open for the season?"
    ],
    theme="soft",
    retry_btn=None,
    undo_btn=None,
    clear_btn="Clear"
).queue()

if __name__ == "__main__":
    demo.launch()