Update app.py

app.py

@@ -1,18 +1,17 @@
-import spaces
 import json
 import torch
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-title = """# 🙋🏻‍♂️ Welcome to Tonic's Salesforce/Xlam-7B-r"""
+title = """# 🙋🏻‍♂️ Welcome to Tonic's On 📲🎬🦀Device Function Calling"""
 description = """
-🎬 Large Action Models (LAMs) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.Check our the Salesforce/xLAM models : [🤗 xLAM-1b-fc-r](https://huggingface.co/Salesforce/xLAM-1b-fc-r) | [🤗 xLAM-1b-fc-r-GGUF](https://huggingface.co/Salesforce/xLAM-1b-fc-r-gguf) [🤗 xLAM-7b-fc-r](https://huggingface.co/Salesforce/xLAM-7b-fc-r) | [🤗 xLAM-7b-fc-r-GGUF](https://huggingface.co/Salesforce/xLAM-7b-fc-r-gguf) [🤗 xLAM-7b-r ](https://huggingface.co/Salesforce/xLAM-7b-r) | [🤗 xLAM-8x7b-r](https://huggingface.co/Salesforce/xLAM-8x7b-r) [🤗 xLAM-8x22b-r](https://huggingface.co/Salesforce/xLAM-8x22b-r) |
+📲🎬🦀Salesforce/xLAM-1b-fc-r is on device , meaning you can put it in offline apps and more! 🎬 Large Action Models (LAMs) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications.Check our the Salesforce/xLAM models : [🤗 xLAM-1b-fc-r](https://huggingface.co/Salesforce/xLAM-1b-fc-r) | [🤗 xLAM-1b-fc-r-GGUF](https://huggingface.co/Salesforce/xLAM-1b-fc-r-gguf) [🤗 xLAM-7b-fc-r](https://huggingface.co/Salesforce/xLAM-7b-fc-r) | [🤗 xLAM-7b-fc-r-GGUF](https://huggingface.co/Salesforce/xLAM-7b-fc-r-gguf) [🤗 xLAM-7b-r ](https://huggingface.co/Salesforce/xLAM-7b-r) | [🤗 xLAM-8x7b-r](https://huggingface.co/Salesforce/xLAM-8x7b-r) [🤗 xLAM-8x22b-r](https://huggingface.co/Salesforce/xLAM-8x22b-r) |
 ### Join us : 
 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
 """
 
 # Load model and tokenizer
-model_name = "Salesforce/xLAM-7b-r"
+model_name = "Salesforce/xLAM-1b-fc-r"
 model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -106,7 +105,6 @@ def build_prompt(task_instruction: str, format_instruction: str, tools: list, qu
         prompt += build_conversation_history_prompt(conversation_history)
     return prompt
 
-@spaces.GPU
 def generate_response(tools_input, query):
     try:
         tools = json.loads(tools_input)