app.py

```python
import json
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "Salesforce/xLAM-7b-r"
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()

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_conversation_history_prompt(conversation_history: str):
    parsed_history = []
    for step_data in conversation_history:
        parsed_history.append({
            "step_id": step_data["step_id"],
            "thought": step_data["thought"],
            "tool_calls": step_data["tool_calls"],
            "next_observation": step_data["next_observation"],
            "user_input": step_data['user_input']
        })
    
    history_string = json.dumps(parsed_history)
    return f"\n[BEGIN OF HISTORY STEPS]\n{history_string}\n[END OF HISTORY STEPS]\n"

def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str, conversation_history: list):
    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"
    
    if len(conversation_history) > 0:
        prompt += build_conversation_history_prompt(conversation_history)
    return prompt

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)
    conversation_history = []
    content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query, conversation_history)

    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
iface = gr.Interface(
    fn=generate_response,
    inputs=[
        gr.Textbox(
            label="Available Tools (JSON format)",
            lines=10,
            value=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)
        ),
        gr.Textbox(label="User Query", lines=2, value="What's the weather like in New York in fahrenheit?")
    ],
    outputs=gr.Textbox(label="Generated Response", lines=10),
    title="xLAM-7b-r API Request Generator",
    description="Enter available tools in JSON format and a user query to generate an API request or response.",
)

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