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import os
from smolagents import CodeAgent, ToolCallingAgent
from smolagents import OpenAIServerModel
from tools.fetch import fetch_webpage
from tools.yttranscript import get_youtube_transcript, get_youtube_title_description
import myprompts
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
# --- Basic Agent Definition ---
class BasicAgent:
def __init__(self):
print("BasicAgent initialized.")
def __call__(self, question: str) -> str:
print(f"Agent received question (first 50 chars): {question[:50]}...")
try:
# Use the reviewer agent to determine if the question can be answered by a model or requires code
print("Calling reviewer agent...")
reviewer_answer = reviewer_agent.run(myprompts.review_prompt + "\nThe question is:\n" + question)
print(f"Reviewer agent answer: {reviewer_answer}")
question = question + '\n' + myprompts.output_format
fixed_answer = ""
if reviewer_answer == "code":
fixed_answer = gaia_agent.run(question)
print(f"Code agent answer: {fixed_answer}")
elif reviewer_answer == "model":
# If the reviewer agent suggests using the model, we can proceed with the model agent
print("Using model agent to answer the question.")
fixed_answer = model_agent.run(myprompts.model_prompt + "\nThe question is:\n" + question)
print(f"Model agent answer: {fixed_answer}")
return fixed_answer
except Exception as e:
error = f"An error occurred while processing the question: {e}"
print(error)
return error
# Model configuration
model_id = "bartowski/Llama-3.2-3B-Instruct-GGUF"
filename = "Llama-3.2-3B-Instruct-Q4_K_M.gguf"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model_init = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename, torch_dtype=torch_dtype)
# Create a wrapper class that matches the expected interface
class LocalLlamaModel:
def __init__(self, model, tokenizer):
self.model = model
self.tokenizer = tokenizer
self.device = model.device if hasattr(model, 'device') else 'cpu'
def generate(self, prompt: str, max_new_tokens=512*10, **kwargs):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
output_ids = model.generate(input_ids, max_new_tokens=max_new_tokens)
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return output
def __call__(self, prompt: str, max_new_tokens=512, **kwargs):
"""Make the model callable like a function"""
return self.generate(prompt, max_new_tokens, **kwargs)
# Create the model instance
model = LocalLlamaModel(model_init, tokenizer)
# Now create your agents - these should work with the wrapped model
reviewer_agent = ToolCallingAgent(model=model, tools=[])
model_agent = ToolCallingAgent(model=model, tools=[fetch_webpage])
gaia_agent = CodeAgent(
tools=[fetch_webpage, get_youtube_title_description, get_youtube_transcript],
model=model
)
if __name__ == "__main__":
# Example usage
question = "What was the actual enrollment of the Malko competition in 2023?"
agent = BasicAgent()
answer = agent(question)
print(f"Answer: {answer}") |