File size: 6,367 Bytes
434b328
 
 
 
 
 
 
 
 
8875451
434b328
 
 
 
 
 
980918c
434b328
 
 
 
 
 
 
 
 
 
 
 
 
 
980918c
 
 
 
 
 
 
 
 
434b328
 
 
 
980918c
 
434b328
980918c
 
434b328
 
 
 
 
 
 
 
 
980918c
 
434b328
 
 
 
 
980918c
 
434b328
980918c
 
434b328
 
980918c
 
434b328
 
980918c
 
 
434b328
 
 
 
 
 
980918c
434b328
 
 
 
 
980918c
 
434b328
 
 
980918c
 
434b328
 
980918c
434b328
 
 
 
 
980918c
 
 
434b328
 
 
 
 
 
 
980918c
 
434b328
 
8875451
 
434b328
980918c
 
434b328
 
980918c
 
 
434b328
 
 
 
 
 
 
 
8875451
980918c
434b328
 
 
 
980918c
434b328
 
980918c
434b328
 
 
 
980918c
8875451
980918c
8875451
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
from google import genai
from google.genai import types
from google.genai.types import *
import os
from dotenv import load_dotenv
import sys
from src.tool_loader import ToolLoader
from src.utils.suppress_outputs import suppress_output
import logging
import gradio as gr

logger = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
# handler.setLevel(logging.DEBUG)
logger.addHandler(handler)


class GeminiManager:
    def __init__(self, toolsLoader: ToolLoader, system_prompt_file="./models/system3.prompt", gemini_model="gemini-2.5-pro-exp-03-25"):
        load_dotenv()
        self.API_KEY = os.getenv("GEMINI_KEY")
        self.client = genai.Client(api_key=self.API_KEY)
        self.toolsLoader: ToolLoader = toolsLoader
        self.toolsLoader.load_tools()
        self.model_name = gemini_model
        with open(system_prompt_file, 'r', encoding="utf8") as f:
            self.system_prompt = f.read()
        self.messages = []

    def generate_response(self, messages):
        return self.client.models.generate_content(
            model=self.model_name,
            contents=messages,
            config=types.GenerateContentConfig(
                system_instruction=self.system_prompt,
                temperature=0.2,
                tools=self.toolsLoader.getTools(),
            ),
        )

    def handle_tool_calls(self, response):
        parts = []
        for function_call in response.function_calls:
            toolResponse = None
            logger.info(
                f"Function Name: {function_call.name}, Arguments: {function_call.args}")
            try:
                toolResponse = self.toolsLoader.runTool(
                    function_call.name, function_call.args)
            except Exception as e:
                logger.warning(f"Error running tool: {e}")
                toolResponse = {
                    "status": "error",
                    "message": f"Tool {function_call.name} failed to run.",
                    "output": str(e),
                }
            logger.debug(f"Tool Response: {toolResponse}")
            tool_content = types.Part.from_function_response(
                name=function_call.name,
                response={"result": toolResponse})
            try:
                self.toolsLoader.load_tools()
            except Exception as e:
                logger.info(f"Error loading tools: {e}. Deleting the tool.")
                # delete the created tool
                self.toolsLoader.delete_tool(
                    toolResponse['output']['tool_name'], toolResponse['output']['tool_file_path'])
                tool_content = types.Part.from_function_response(
                    name=function_call.name,
                    response={"result": f"{function_call.name} with {function_call.args} doesn't follow the required format, please read the other tool implementations for reference." + str(e)})
            parts.append(tool_content)
        return {
            "role": "tool",
            "content": repr(types.Content(
                    role='model' if self.model_name == "gemini-2.5-pro-exp-03-25" else 'tool',
                    parts=parts
            ))
        }

    def format_chat_history(self, messages=[]):
        formatted_history = []
        for message in messages:
            # Skip thinking messages (messages with metadata)
            if not (message.get("role") == "assistant" and "metadata" in message):
                role = "model"
                parts = [types.Part.from_text(text=message.get("content", ""))]
                match message.get("role"):
                    case "user":
                        role = "user"
                    case "tool":
                        role = "tool"
                        formatted_history.append(
                            eval(message.get("content", "")))
                        continue
                    case "function_call":
                        role = "model"
                        formatted_history.append(
                            eval(message.get("content", "")))
                        continue
                    case _:
                        role = "model"
                formatted_history.append(types.Content(
                    role=role,
                    parts=parts
                ))
        return formatted_history

    def run(self, messages):
        print("Messages: ", messages)
        chat_history = self.format_chat_history(messages)
        logger.debug(f"Chat history: {chat_history}")
        try:
            response = suppress_output(self.generate_response)(chat_history)
        except Exception as e:
            logger.debug(f"Error generating response: {e}")
            messages.append({
                "role": "assistant",
                "content": f"Error generating response: {e}"
            })
            logger.error(f"Error generating response: {e}")
            yield messages, gr.update(interactive=True)
            return
        logger.debug(f"Response: {response}")
        print("Response: ", response)

        if (not response.text and not response.function_calls):
            messages.append({
                "role": "assistant",
                "content": "No response from the model.",
                "metadata": {"title": "No response from the model."}
            })

        # Attach the llm response to the messages
        if response.text is not None and response.text != "":
            messages.append({
                "role": "assistant",
                "content": response.text
            })
            yield messages, gr.update(interactive=False,)

        # Attach the function call response to the messages
        if response.candidates[0].content and response.candidates[0].content.parts:
            # messages.append(response.candidates[0].content)
            messages.append({
                "role": "function_call",
                "content": repr(response.candidates[0].content),
            })

        # Invoke the function calls if any and attach the response to the messages
        if response.function_calls:
            calls = self.handle_tool_calls(response)
            messages.append(calls)
            yield from self.run(messages)
            return
        print("Final messages: ", messages)
        yield messages, gr.update(interactive=True)