app.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import gradio as gr

# Load model and tokenizer
base_model_name = "unsloth/gemma-3-12b-it-unsloth-bnb-4bit"
adapter_name = "adarsh3601/my_gemma3_pt"
device = "cuda" if torch.cuda.is_available() else "cpu"

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_4bit=True
)

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(base_model, adapter_name)
model.to(device)

# Chat function with debug/error handling
def chat(message):
    if not message or not message.strip():
        return "Please enter a message."

    try:
        # Tokenize
        inputs = tokenizer(message, return_tensors="pt")
        inputs = {k: v.to(device) for k, v in inputs.items()}

        # Cast to float16 only if model is on CUDA
        if device == "cuda":
            inputs = {k: v.half() for k, v in inputs.items()}

        # Generate
        outputs = model.generate(
            **inputs,
            max_new_tokens=150,
            do_sample=True,
            temperature=0.7,
            top_k=50,
            top_p=0.95
        )

        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response

    except RuntimeError as e:
        if "CUDA error" in str(e):
            return "⚠️ CUDA error during generation. Try restarting or changing your input."
        return f"Unexpected error: {e}"
    except Exception as e:
        return f"Error: {e}"

# Gradio UI
iface = gr.Interface(fn=chat, inputs="text", outputs="text", title="Gemma Chatbot")
iface.launch()