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 Hugging Face 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__()
        api_token = os.getenv("HF_API_TOKEN")
        if not api_token:
            raise EnvironmentError("HF_API_TOKEN not found in environment variables.")
        self.api_url = "https://api-inference.huggingface.co/models/microsoft/git-base-captioning"
        self.headers = {
            "Authorization": f"Bearer {api_token}",
            "Content-Type": "application/json"
        }

    def forward(self, image_path: str, question: str) -> str:
        try:
            with open(image_path, "rb") as img_file:
                image_bytes = img_file.read()

            # Encode image to base64 string
            img_b64 = base64.b64encode(image_bytes).decode("utf-8")

            # Prepare JSON payload - the exact structure depends on the model capabilities
            # Here we send just the image for captioning
            payload = {
                "inputs": img_b64
            }

            response = requests.post(
                self.api_url,
                headers=self.headers,
                json=payload,
                timeout=60
            )

            if response.status_code == 200:
                result = response.json()

                caption = None
                # Try common keys for caption output
                if isinstance(result, dict):
                    caption = result.get("generated_text") or result.get("caption") or result.get("text")
                elif isinstance(result, list) and len(result) > 0 and isinstance(result[0], dict):
                    caption = result[0].get("generated_text") or result[0].get("caption") or result[0].get("text")

                if not caption:
                    return "Error: No caption found in model response."

                # Combine caption with the question to form a simple answer
                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}"