src/streamlit_app.py

import os
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import streamlit as st
import torch

st.title("Tokenizer Test Space")
# model_id = "unsloth/gemma-3-1b-it" # Test with the official model first
model_id = "Rahul-8799/project_manager_gemma3" # If the official model works, try yours

# Define a writable directory for the cache. /tmp is usually writable in Spaces.
cache_directory = "/tmp/hf_cache"

# Ensure the cache directory exists (good practice, though hf_hub might handle it)
os.makedirs(cache_directory, exist_ok=True)

try:
    st.write(f"Attempting to load tokenizer for {model_id}...")
    # Explicitly pass the cache_dir
    tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_directory)
    st.success("Tokenizer loaded successfully!")
    st.write("Tokenizer details:", tokenizer)
except Exception as e:
    st.error(f"Error loading tokenizer: {e}")
    st.exception(e) # Show full traceback

try:
    st.write(f"Attempting to load model for {model_id}...")
    # Assuming you want 4-bit quantization for Gemma
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=False,
    )
    # Explicitly pass the cache_dir
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        quantization_config=quantization_config,
        low_cpu_mem_usage=True,
        torch_dtype=torch.bfloat16,
        trust_remote_code=True,
        cache_dir=cache_directory # Add this line
    )
    st.success("Model loaded successfully!")
    st.write("Model details:", model)
except Exception as e:
    st.error(f"Error loading model: {e}")
    st.exception(e)