File size: 4,540 Bytes
21132c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
518f90d
9eda7a5
 
21132c7
 
 
518f90d
9eda7a5
 
21132c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11a3890
21132c7
 
11a3890
 
 
 
 
 
 
9b927a8
11a3890
 
 
 
 
9b927a8
 
11a3890
 
 
 
 
 
 
21132c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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

# a multi agent proposal to solve HF agent course final assignment
import os
import dotenv
from smolagents import CodeAgent
from smolagents import OpenAIServerModel
from tools.fetch import fetch_webpage, search_web
from smolagents import PythonInterpreterTool
from tools.yttranscript import get_youtube_transcript, get_youtube_title_description
from tools.stt import get_text_transcript_from_audio_file
from tools.image import analyze_image
from common.mylogger import mylog
import myprompts

dotenv.load_dotenv()

gemini_model = OpenAIServerModel(
    model_id="gemini-2.0-flash",
    api_key=os.environ["GEMINI_API_KEY"],
    # Google Gemini OpenAI-compatible API base URL
    api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
)

vllm_model = OpenAIServerModel(
    model_id="Qwen/Qwen2.5-1.5B-Instruct",
    api_base="http://192.168.1.39:18000/v1",  
    api_key="token-abc123",  
)

openai_41nano_model = OpenAIServerModel(
    model_id="llama3-70b-8192",
    api_base="https://api.groq.com/openai/v1",
    api_key=os.environ["GROQ_API_KEY"],
)

openai_41mini_model = OpenAIServerModel(
    model_id="llama3-70b-8192",
    api_base="https://api.groq.com/openai/v1",
    api_key=os.environ["GROQ_API_KEY"],
)


def check_final_answer(final_answer, agent_memory)  -> bool:
    """
    Check if the final answer is correct.
    basic check on the length of the answer.
    """
    mylog("check_final_answer", final_answer)
    # if return answer is more than 200 characters, we will assume it is not correct    
    if len(str(final_answer)) > 200:
        return False
    else:
        return True


web_agent = CodeAgent(
    model=openai_41nano_model,
    tools=[
        search_web,
        fetch_webpage,                
    ],
    name="web_agent",
    description="Use search engine to find webpages related to a subject and get the page content",
    additional_authorized_imports=["pandas", "numpy","bs4"],
    verbosity_level=1,    
    max_steps=7,
)

audiovideo_agent = CodeAgent(
    model=openai_41nano_model,
    tools=[
        get_youtube_transcript,
        get_youtube_title_description,
        get_text_transcript_from_audio_file,
        analyze_image
    ],
    name="audiovideo_agent",
    description="Extracts information from image, video or audio files from the web",
    additional_authorized_imports=["pandas", "numpy","bs4", "requests"],
    verbosity_level=1,
    max_steps=7,
)



manager_agent = CodeAgent(
    model=openai_41mini_model,
    tools=[ PythonInterpreterTool()],
    managed_agents=[web_agent, audiovideo_agent],    
    additional_authorized_imports=["pandas", "numpy","bs4"],
    planning_interval=5,
    verbosity_level=2,
    final_answer_checks=[check_final_answer],
    max_steps=15,
    name="manager_agent",
    description="A manager agent that coordinates the work of other agents to answer questions.",
)

class MultiAgent:
    def __init__(self):
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        mylog(self.__class__.__name__, question)

        try:
            prefix = (
                "You are the top agent of a multi-agent system that can answer questions "
                "by coordinating the work of other agents.\n"
                "- Use `web_agent` to search or fetch webpage content.\n"
                "- Use `audiovideo_agent` to process YouTube videos, images, or audio files.\n"
                "- You can also use your own reasoning.\n\n"
                "Find the correct answer step by step, then format your output properly."
            )

            # ✅ Ensure everything is plain string
            full_prompt = f"{prefix.strip()}\n\nTHE QUESTION:\n{question.strip()}\n\n{myprompts.output_format.strip()}"

            # ✅ Hard enforcement of string format
            if not isinstance(full_prompt, str):
                full_prompt = str(full_prompt)

            # ✅ Safe model call
            result = manager_agent.run(full_prompt)

            # Optionally truncate very long answers to avoid submission rejection
            return result if isinstance(result, str) else str(result)

        except Exception as e:
            error = f"An error occurred while processing the question: {e}"
            print(error)
            return error


if __name__ == "__main__":
    # Example usage
   
    question = """
What was the actual enrollment of the Malko competition in 2023?
"""
    agent = MultiAgent()
    answer = agent(question)
    print(f"Answer: {answer}")