app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=True)

# Define the response function
def respond(query):
    prompt = f"[INST] {query} [/INST]"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        **inputs,
        max_new_tokens=50,  # Adjust based on resource constraints
        pad_token_id=tokenizer.eos_token_id
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Deepseek Coder Chatbot")
    query_input = gr.Textbox(label="Ask me anything...")
    output = gr.Textbox(label="Response")
    submit_button = gr.Button("Submit")
    submit_button.click(respond, inputs=query_input, outputs=output)

demo.launch()