tools.py

from smolagents import DuckDuckGoSearchTool
from smolagents import Tool
from huggingface_hub import InferenceClient
import soundfile as sf
from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
from qwen_omni_utils import process_mm_info
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset

class Web_research(Tool):
    name="web_research"
    description = "Web search on a specific topic."
    inputs = {
        "topic": {
            "type": "string",
            "description": "The topic on which the user wants the latest news"
        }
    }
    output_type = "string"

    def forward(self, topic: str):
        search_tool = DuckDuckGoSearchTool()
        # Example usage
        results = search_tool(f"{topic}")
        return f"Here is what we can find on the web for {topic} : str({results})"

class Find_wikipedia_URL(Tool):
    name="wiki_url"
    description = "Always use to check a wikipedia ENGLISH URL page before trying to acces the URL. For another langage, you just have to change the beginning of the url (here, it is en for english)"
    inputs = {
        "subject": {
            "type": "string",
            "description": "The name or topic on which you want the Wikipedia URL"
        }
    }
    output_type = "string"

    def forward(self, subject: str):
        words=subject.split()
        url_wiki="https://en.wikipedia.org/wiki/"
        for i in range(len(words)):
            if(i==0):
                url_wiki+=str(words[i])
            if(i!=0):
                url_wiki+='_'+str(words[i])
        return f"Here is what we url to use : str({url_wiki}). If it does not work, change the first letters of {subject} to be upper or lower, but never change anything else"

class translate_everything(Tool):
    name="translator"
    description = "You do not understand a sentence? It does not look like any language you know? Try this tool, maybe the sentence is just reversed!"
    inputs = {
        "sentence": {
            "type": "string",
            "description": "The sentence to translate"
        }
    }
    output_type = "string"

    def forward(self, sentence: str):
        # Input string
        reversed_words = sentence.split() #' '.join(s.split()[::-1])
        right_sentence=[]
        for word in reversed_words:
            right_sentence.append(word[::-1])
        
        translated_sentence = " ".join(right_sentence[::-1])
        return f"The translated sentence is : {translated_sentence}"

class multimodal_interpreter(Tool):
    name="multimodal_tool"
    description = "Allows you to answer any question which relies on image or video input."
    inputs = {
        'image': {"type": "image", "description": "the image or video of interest"},
        'prompt': {"type": "string", "description": "Any specific question you have on the image. For example, the prompt can be : Summarise this image in one sentence."}
    }
    output_type = "string"
    
    def forward(self, prompt, image):
        # default: Load the model on the available device(s)
        model = Qwen2_5OmniForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B", torch_dtype="auto", device_map="auto")
        
        # We recommend enabling flash_attention_2 for better acceleration and memory saving.
        # model = Qwen2_5OmniForConditionalGeneration.from_pretrained(
        #     "Qwen/Qwen2.5-Omni-7B",
        #     torch_dtype="auto",
        #     device_map="auto",
        #     attn_implementation="flash_attention_2",
        # )
        
        processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
        
        conversation = [
            {
                "role": "system",
                "content": [
                    {"type": "text", "text": "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech."}
                ],
            },
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": {image}},
                ],
            },
        ]
        
        # set use audio in video
        USE_AUDIO_IN_VIDEO = True
        
        # Preparation for inference
        text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
        audios, images, videos = process_mm_info(conversation, use_audio_in_video=USE_AUDIO_IN_VIDEO)
        inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=USE_AUDIO_IN_VIDEO)
        inputs = inputs.to(model.device).to(model.dtype)
        
        # Inference: Generation of the output text and audio
        text_ids, audio = model.generate(**inputs, use_audio_in_video=USE_AUDIO_IN_VIDEO)

        text = processor.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        sf.write(
            "output.wav",
            audio.reshape(-1).detach().cpu().numpy(),
            samplerate=24000,
        )

        return text

class audio_or_mp3__interpreter(Tool):
    name="multimodal_tool"
    description = "Allows you to convert audio into text. It uses Whisper, it is a state-of-the-art model for automatic speech recognition (ASR) and speech translation"
    inputs = {
        'audio': {"type": "audio", "description": "the audio of interest"}
    }
    output_type = "string"
    
    def forward(self, prompt, audio):
        device = "cuda:0" if torch.cuda.is_available() else "cpu"
        torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
        
        model_id = "openai/whisper-large-v3"
        
        model = AutoModelForSpeechSeq2Seq.from_pretrained(
            model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
        )
        model.to(device)
        
        processor = AutoProcessor.from_pretrained(model_id)
        
        pipe = pipeline(
            "automatic-speech-recognition",
            model=model,
            tokenizer=processor.tokenizer,
            feature_extractor=processor.feature_extractor,
            torch_dtype=torch_dtype,
            device=device,
        )
        
        sample = {audio}[0]["audio"]
        
        result = pipe(sample)
        return result["text"]

class Wikipedia_reader(Tool):
    name="wikipedia_tool"
    description = "To be used whenever you need to read a Wikipedia page. Will return all the text of the Wikipedia page, to easily read it and find information"
    inputs = {
        "url": {
            "type": "string",
            "description": "The wikippedia url page"
        }
    }
    output_type = "string"

    def forward(self, url: str):
        try:
            page = requests.get(url)
        except Exception as e:
            print('Error downloading page: ',e)
        soup = BeautifulSoup(page.text, 'html.parser')
        return soup.text