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d1479d0 | 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 | import os
from smolagents import Tool, tool
from huggingface_hub import HfApi
from dotenv import load_dotenv
from huggingface_hub import InferenceClient
from utils import upload_file
load_dotenv()
HF_TOKEN = os.environ.get("HF_TOKEN")
api = HfApi()
client = InferenceClient(
provider="hf-inference",
api_key=HF_TOKEN,
)
# --- Constants ---
local_data_path = "../data"
if not os.path.exists(local_data_path):
os.makedirs(local_data_path)
@tool
def image_question_answering(image_path: str, prompt: str) -> str:
"""
This function takes a image path and a prompt, and returns the answer to the question.
Args:
image_path: The path to the image file
prompt: The prompt to the question
Returns:
The answer to the question
"""
file_extension = image_path.split(".")[-1]
if file_extension in [".mp4", ".avi", ".mov", ".wmv", ".mkv", ".webm"]:
return "Media type not supported. Please upload an image."
if image_path.startswith("http"):
media_url = image_path
else:
media_url = upload_file(image_path)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt,
},
{
"type": "image_url",
"image_url": {"url": media_url},
}
],
}
]
completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-11B-Vision-Instruct",
messages=messages,
)
return completion.choices[0].message
@tool
def transcribe_audio(file_local_path: str) -> str:
"""
Transcribe the audio file and return the transcript
Args:
file_local_path: The local path to the audio file
Returns:
The transcript of the audio file
"""
file_url = upload_file(file_local_path)
asr_tool = Tool.from_space(
"hf-audio/whisper-large-v3",
api_name="/predict_1", # from file
name="transcribe_audio",
description="Use this tool to transcribe the audio"
)
transcript = asr_tool(file_url)
return transcript
class GetFileTool(Tool):
name = "get_file"
description = "Download a file from the given file name"
inputs = {
"file_name": {
"type": "string",
"description": "Download the file from the given file name and outputs the local path"
}
}
output_type = "string"
def forward(self, file_name: str) -> str:
import requests
if file_name == "":
return "No file name provided"
task_id = file_name.split(".")[0]
url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
headers = {
"accept": "application/json"
}
req = requests.get(url, headers=headers)
if req.status_code != 200:
return "File not found, please check the file name and try again."
local_file_path = local_data_path + "/" + file_name
with open(local_file_path, "wb") as f:
f.write(req.content)
print(f"File saved to {local_file_path}. You can read this file to process its contents.")
return local_file_path
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