app.py

import spaces
import json
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "Salesforce/xLAM-1b-fc-r"

title = f"# 🚀 Eval Model: {model_name}"

model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set random seed for reproducibility
torch.random.manual_seed(0)

@spaces.GPU
def generate_response(query):
    messages = [
        {'role': 'user', 'content': query}
    ]

    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
    outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return result

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown(title)
    
    with gr.Row():
        with gr.Column():
            query_input = gr.Code(
                label="User Content",
                lines=20,
                language='json'
            )
            submit_button = gr.Button("Generate Response")
        
        with gr.Column():
            output = gr.Code(label="Response :", lines=20, language="json")
    
    submit_button.click(generate_response, inputs=[query_input], outputs=output)

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