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from smolagents import CodeAgent, HfApiModel, load_tool, tool, Tool |
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import datetime |
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import os |
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import io |
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import pytz |
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import yaml |
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import torch |
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import soundfile as sf |
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from scipy import signal |
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from huggingface_hub import InferenceClient |
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from tools.final_answer import FinalAnswerTool |
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from tools.visit_webpage import VisitWebpageTool |
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from tools.web_search import DuckDuckGoSearchTool |
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from Gradio_UI import GradioUI |
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class TextToSpeechTool(Tool): |
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description = "This tool creates an audio tensor from the input, which is text. For audio tasks, the output from this tool is the final answer." |
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name = "text_to_speech" |
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inputs = {"text": {"type": "string", "description": "This is the text that will be converted into speech"}} |
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output_type = "audio" |
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client = InferenceClient( |
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provider="auto", |
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api_key=os.environ["HF_TOKEN"], |
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) |
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def forward(self, text: str) -> torch.Tensor: |
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output = self.client.text_to_speech( |
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text, |
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model="ResembleAI/chatterbox", |
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) |
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audio, samplerate = sf.read(io.BytesIO(output)) |
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new_samplerate = 16_000 |
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num_samples = int(len(audio) * new_samplerate / samplerate) |
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resampled_audio = signal.resample(audio, num_samples) |
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return torch.from_numpy(resampled_audio) |
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@tool |
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def get_current_time_in_timezone(timezone: str) -> str: |
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"""A tool that fetches the current local time in a specified timezone. |
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Args: |
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timezone: A string representing a valid timezone (e.g., 'America/New_York'). |
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""" |
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try: |
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tz = pytz.timezone(timezone) |
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local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S") |
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return f"The current local time in {timezone} is: {local_time}" |
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except Exception as e: |
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return f"Error fetching time for timezone '{timezone}': {str(e)}" |
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final_answer = FinalAnswerTool() |
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web_search = DuckDuckGoSearchTool() |
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visit_page = VisitWebpageTool() |
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read_out_loud = TextToSpeechTool() |
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model = HfApiModel( |
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max_tokens=2096, |
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temperature=0.5, |
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model_id='Qwen/Qwen2.5-Coder-32B-Instruct', |
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custom_role_conversions=None |
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) |
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image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) |
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with open("prompts.yaml", 'r') as stream: |
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prompt_templates = yaml.safe_load(stream) |
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demo = CodeAgent( |
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model=model, |
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tools=[ |
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get_current_time_in_timezone, |
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read_out_loud, |
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image_generation_tool, |
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web_search, |
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visit_page, |
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final_answer |
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], |
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additional_authorized_imports=['torch.Tensor'], |
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max_steps=6, |
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verbosity_level=1, |
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grammar=None, |
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planning_interval=None, |
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name=None, |
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description=None, |
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prompt_templates=prompt_templates |
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) |
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GradioUI(demo).launch() |