app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# Use a more compatible DeepSeek model
model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Fix quantization issue by using 4-bit
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,  # Use 4-bit instead of 8-bit
    bnb_4bit_compute_dtype=torch.float16,  # Use FP16 for better compatibility
    bnb_4bit_use_double_quant=True,  # Enable double quantization for efficiency
)

# Load model with optimized quantization
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    quantization_config=quantization_config,
    trust_remote_code=True
)

# Define text generation function
def generate_response(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        output = model.generate(**inputs, max_length=150)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Set up Gradio UI
interface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your prompt"),
    outputs=gr.Textbox(label="AI Response"),
    title="DeepSeek-R1 Distill LLaMA Chatbot",
    description="Enter a prompt and receive a response from DeepSeek-R1-Distill-Llama-8B."
)

# Launch the app
interface.launch()