image_analyzer.py

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}"