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import os
import base64
import requests
from smolagents import Tool
class ImageAnalysisTool(Tool):
name = "image_analysis"
description = "Analyze the content of an image and answer a specific question about it using HF Inference API."
inputs = {
"image_path": {
"type": "string",
"description": "Path to the image file (jpg, png, etc.)"
},
"question": {
"type": "string",
"description": "A question about the image content"
}
}
output_type = "string"
def __init__(self):
super().__init__()
# You can replace this with any vision model capable of VQA or image captioning
self.api_url = "https://api-inference.huggingface.co/models/microsoft/git-base-captioning"
self.headers = {
"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"
}
def forward(self, image_path: str, question: str) -> str:
try:
with open(image_path, "rb") as img_file:
image_bytes = img_file.read()
# Prepare the payload depending on the model API.
# Some models accept just the image bytes and return captions,
# some support multimodal input with text question + image.
# For this example, we'll assume a captioning model and append question manually.
response = requests.post(
self.api_url,
headers=self.headers,
data=image_bytes,
timeout=60
)
if response.status_code == 200:
result = response.json()
caption = None
# The format depends on the model; check keys like 'generated_text' or 'caption'
if isinstance(result, dict):
caption = result.get("generated_text") or result.get("caption")
elif isinstance(result, list) and len(result) > 0:
caption = result[0].get("generated_text") if "generated_text" in result[0] else None
if not caption:
return "Error: No caption found in model response."
# Simple approach: combine caption + question to produce answer prompt
# If you want a deeper answer, you could chain a chat model here.
answer = f"Caption: {caption}\nAnswer to question '{question}': {caption}"
return answer.strip()
else:
return f"Error analyzing image: {response.status_code} {response.text}"
except Exception as e:
return f"Error analyzing image: {e}"
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