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from smolagents import Tool
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
from wikipedia_utils import *
from youtube_utils import *
class MathModelQuerer(Tool):
name = "math_model"
description = "Answers advanced math questions using a pretrained math model."
inputs = {
"problem": {
"type": "string",
"description": "Math problem to solve.",
}
}
output_type = "string"
def __init__(self, model_name="deepseek-ai/deepseek-math-7b-base"):
print(f"Loading math model: {model_name}")
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
print("loaded tokenizer")
self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
print("loaded auto model")
self.model.generation_config = GenerationConfig.from_pretrained(model_name)
print("loaded coonfig")
self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
print("loaded pad token")
def forward(self, problem: str) -> str:
try:
print(f"[MathModelTool] Question: {problem}")
inputs = self.tokenizer(problem, return_tensors="pt")
outputs = self.model.generate(**inputs, max_new_tokens=100)
result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
except:
return f"Failed using the tool {self.name}"
class CodeModelQuerer(Tool):
name = "code_querer"
description = "Given a problem description, generates a piece of code used specialized LLM model. Returns output of the model."
inputs = {
"problem": {
"type": "string",
"description": "Description of a code sample to be generated",
}
}
output_type = "string"
def __init__(self, model_name="Qwen/Qwen2.5-Coder-32B-Instruct"):
from smolagents import HfApiModel
print(f"Loading llm for Code tool: {model_name}")
self.model = HfApiModel()
def forward(self, problem: str) -> str:
try:
return self.model.generate(problem, max_new_tokens=512)
except:
return f"Failed using the tool {self.name}"
class WikipediaPageFetcher(Tool):
name = "wiki_page_fetcher"
description = "Searches Wikipedia and provides summary about the queried topic as a string."
inputs = {
"query": {
"type": "string",
"description": "Topic of wikipedia search",
}
}
output_type = "string"
def forward(self, query: str) -> str:
try:
wiki_query = query(query)
wiki_page = fetch_wikipedia_page(wiki_query)
return wiki_page
except:
return f"Failed using the tool {self.name}"
class YoutubeTranscriptFetcher(Tool):
name = "youtube_transcript_fetcher"
description = "Attempts to fetch a youtube transcript in english, if provided with a query \\" \
" that contains a youtube link with video id. Returns a transcript content as a string. Alternatively, if tool is provided with a\\"" \
youtube video id, it can fetch the transcript directly."
inputs = {
"query": {
"type": "string",
"description": "A query that includes youtube id."
},
"video_id" : {
"type" : "string",
"description" : "Optional string with video id from youtube.",
"nullable" : True
}
}
output_type = "string"
def forward(self, query: str, video_id=None) -> str:
try:
if video_id is None:
video_id = get_youtube_video_id(query)
fetched_transcript = fetch_transcript_english(video_id)
return post_process_transcript(fetched_transcript)
except:
return f"Failed using the tool {self.name}"
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