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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}"
description = """"""
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)
# Task and format instructions
task_instruction = """
Based on the previous context and API request history, generate an API request or a response as an AI assistant.""".strip()
format_instruction = """
The output should be of the JSON format, which specifies a list of generated function calls. The example format is as follows, please make sure the parameter type is correct. If no function call is needed, please make
tool_calls an empty list "[]".
```
{"thought": "the thought process, or an empty string", "tool_calls": [{"name": "api_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}]}
```
""".strip()
# Example tools and query
example_tools = json.dumps([
{
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, New York"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature to return"
}
},
"required": ["location"]
}
},
{
"name": "search",
"description": "Search for information on the internet",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query, e.g. 'latest news on AI'"
}
},
"required": ["query"]
}
}
], indent=2)
example_query = "What's the weather like in New York in fahrenheit?"
def convert_to_xlam_tool(tools):
if isinstance(tools, dict):
return {
"name": tools["name"],
"description": tools["description"],
"parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
}
elif isinstance(tools, list):
return [convert_to_xlam_tool(tool) for tool in tools]
else:
return tools
def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str):
prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(tools)}\n[END OF AVAILABLE TOOLS]\n\n"
prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
return prompt
@spaces.GPU
def generate_response(tools_input, query):
try:
tools = json.loads(tools_input)
except json.JSONDecodeError:
return "Error: Invalid JSON format for tools input."
xlam_format_tools = convert_to_xlam_tool(tools)
content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query)
messages = [
{'role': 'user', 'content': content}
]
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)
agent_action = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
return agent_action
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
tools_input = gr.Code(
label="Available Tools (JSON format)",
lines=20,
value=example_tools,
language='json'
)
query_input = gr.Textbox(
label="User Query",
lines=2,
value=example_query
)
submit_button = gr.Button("Generate Response")
with gr.Column():
output = gr.Code(label="🎬 xLam :", lines=10, language="json")
submit_button.click(generate_response, inputs=[tools_input, query_input], outputs=output)
if __name__ == "__main__":
demo.launch() |