app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
#import sqlite3
import json
from db_setup import setup_database
#setup database
setup_database()

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

modelpath = "Salesforce/xLAM-1b-fc-r"
model = AutoModelForCausalLM.from_pretrained(modelpath, torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(modelpath)

#=============prompt template and task instructions==============
# Please use our provided instruction prompt for best performance
task_instruction = """
You are an expert in composing functions. You are given a question and a set of possible functions. 
Based on the question, you will need to make one or more function/tool calls to achieve the purpose. 
If none of the functions can be used, point it out and refuse to answer. 
If the given question lacks the parameters required by the function, also point it out.
""".strip()
format_instruction = """
The output MUST strictly adhere to the following JSON format, and NO other text MUST be included.
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 '[]'.
```
{
    "tool_calls": [
    {"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
    ... (more tool calls as required)
    ]
}
```
""".strip()##==output format
#=============APIs and Functions Metadata========================
get_weather_api = {
    "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"]
    }
}

search_api = {
    "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"]
    }
}

search_loanapplication = {
    "name": "searchLA",
    "description": "Search for Loan Application status",
    "parameters": {
        "type": "object",
        "properties": {
            "loan_application_id": {
                "type": "alphanumeric string",
                "description": "The unique identifier for a loan application, eg: LA1234"
            },
            "phone_number": {
                "type": "string",
                "description": "The phone number associated with the loan application"
            }
        },
        "required": ["loan_application_id", "phone_number"]
    }
}

openai_format_tools = [search_api, search_loanapplication, get_weather_api]
# Helper function to convert openai format tools to our more concise xLAM format
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
#=========prompt builder====================================
# Helper function to build the input prompt for our model
def build_prompt(task_instruction: str, format_instruction: str, xlam_format_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(xlam_format_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

def to_model(query):
    xlam_format_tools = convert_to_xlam_tool(openai_format_tools)
    content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query)
    #print(f"content: {content}")
    messages=[
        { 'role': 'user', 'content': content}
    ]
    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
    # tokenizer.eos_token_id is the id of <|EOT|> token
    outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
    return (tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

def to_app(callobj):
    callobject = json.loads(callobj)
    callfunctions = []
    callarguments = []
    for tool_call in callobject['tool_calls']:
        callfunctions.append(tool_call['name'])
        callarguments.append(list(tool_call['arguments'].values()))
    #print(f"fuctions: {callfunctions}")
    #print(f"arguments: {callarguments}")
    return callfunctions, callarguments
#===========sample application===================================
def application(callfunctions, callarguments):
    ##los application functions
    def get_weather(location):
        return (print(f"weather function executed with city {location}"))
    def searchLA(laid, phnumber):
        query = f"""SELECT * from sfdc_la where LAid = '{laid}'"""
        cursor.execute(query)
        result = cursor.fetchall()
        return (print(result))
    losfunctions_list = ['get_weather','searchLA']
    for i, functionname in enumerate(callfunctions):
        if functionname in losfunctions_list:
            function = globals().get(functionname) or locals().get(functionname)
            if function:
                arguments = callarguments[i]
                out = function(*arguments)
    return out
#out = application(callfunctions, callarguments)
def process_input(input_str):
    if not input_str:
        return "No input provided!"
    model_out = to_model(input_str)
    funs, args = to_app(model_out)
    output_obj = application(funs, args)
    return output_obj

def respond(
    message,
    history: list[tuple[str, str]],
    max_tokens,
    temperature,
    top_p,
):
    #messages = []

    #for val in history:
    #    if val[0]:
    #        messages.append({"role": "user", "content": val[0]})
    #    if val[1]:
    #        messages.append({"role": "assistant", "content": val[1]})

    #messages.append({"role": "user", "content": message})

    response = process_input(message)

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


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