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from smolagents import Tool
from typing import Any, Optional
class SimpleTool(Tool):
name = "web_content_analyzer"
description = "Analyzes web content using AI models."
inputs = {"url":{"type":"string","description":"The webpage URL to analyze."}}
output_type = "string"
def forward(self, url: str) -> str:
"""Analyzes web content using AI models.
Args:
url: The webpage URL to analyze.
Returns:
str: Analysis results in JSON format.
"""
import requests
from bs4 import BeautifulSoup
import re
from transformers import pipeline
import json
try:
# Fetch content
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}
response = requests.get(url, headers=headers, timeout=10)
# Parse HTML
soup = BeautifulSoup(response.text, 'html.parser')
for tag in soup(['script', 'style', 'meta']):
tag.decompose()
# Extract basic info
title = soup.title.string if soup.title else "No title found"
text = re.sub(r'\s+', ' ', soup.get_text()).strip()
if len(text) < 100:
return json.dumps({
"error": "Not enough content to analyze"
})
# Get summary
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(text[:1024], max_length=100, min_length=30)[0]['summary_text']
# Get sentiment
classifier = pipeline("text-classification",
model="nlptown/bert-base-multilingual-uncased-sentiment")
sentiment = classifier(text[:512])[0]
score = int(sentiment['label'][0])
mood = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"][score-1]
# Format results
result = {
"title": title,
"summary": summary,
"sentiment": f"{mood} ({score}/5)",
"stats": {
"words": len(text.split()),
"chars": len(text)
}
}
return json.dumps(result)
except Exception as e:
return json.dumps({
"error": str(e)
}) |