Upload tool

app.py

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+from smolagents import launch_gradio_demo
+from tool import SimpleTool
+
+tool = SimpleTool()
+
+launch_gradio_demo(tool)

requirements.txt

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+bs4
+requests
+transformers
+smolagents

tool.py

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+from smolagents import Tool
+from typing import Any, Optional
+
+class SimpleTool(Tool):
+    name = "web_analyzer"
+    description = "Advanced web content analyzer with AI-powered analysis."
+    inputs = {"url":{"type":"string","description":"The webpage URL to analyze."},"mode":{"type":"string","nullable":True,"description":"Analysis mode ('analyze', 'summarize', 'sentiment', 'topics')."}}
+    output_type = "string"
+
+    def forward(self, url: str, mode: str = "analyze") -> str:
+        """Advanced web content analyzer with AI-powered analysis.
+
+        Args:
+            url: The webpage URL to analyze.
+            mode: Analysis mode ('analyze', 'summarize', 'sentiment', 'topics'). 
+
+        Returns:
+            str: AI-enhanced analysis of web content.
+        """
+        import requests
+        from bs4 import BeautifulSoup
+        import re
+        from transformers import pipeline
+
+        try:
+            # Setup headers
+            headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)'}
+
+            # Fetch content
+            response = requests.get(url, headers=headers, timeout=10)
+            response.raise_for_status()
+
+            # Parse content
+            soup = BeautifulSoup(response.text, 'html.parser')
+            for tag in soup(['script', 'style', 'meta']):
+                tag.decompose()
+
+            # Get cleaned text
+            title = soup.title.string if soup.title else "No title found"
+            title = re.sub(r'\s+', ' ', title).strip()
+            text_content = re.sub(r'\s+', ' ', soup.get_text()).strip()
+
+            # Initialize ML models based on mode
+            if mode == "analyze":
+                # Basic analysis with summary
+                summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+                classifier = pipeline("text-classification", 
+                                    model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+                # Get summary and sentiment
+                summary = summarizer(text_content[:1024], max_length=100, min_length=30)[0]['summary_text']
+                sentiment = classifier(text_content[:512])[0]
+                sent_score = int(sentiment['label'][0])
+                sent_text = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"][sent_score-1]
+
+                # Format output
+                return f"""📊 Content Analysis
+
+    Title: {title}
+    Length: {len(text_content)} characters
+
+    📝 AI Summary:
+    {summary}
+
+    😊 Overall Sentiment: {sent_text} ({sent_score}/5)"""
+
+            elif mode == "summarize":
+                summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+                # Process in chunks
+                chunk_size = 1024
+                summaries = []
+
+                for i in range(0, min(len(text_content), 3072), chunk_size):
+                    chunk = text_content[i:i+chunk_size]
+                    if len(chunk) > 100:
+                        summary = summarizer(chunk, max_length=100, min_length=30)[0]['summary_text']
+                        summaries.append(summary)
+
+                return f"""📝 Multi-Section Summary
+
+    Title: {title}
+
+    {' '.join(summaries)}"""
+
+            elif mode == "sentiment":
+                classifier = pipeline("text-classification", 
+                                   model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+                # Analyze paragraphs
+                paragraphs = soup.find_all('p')
+                sentiments = ""
+                count = 0
+
+                for p in paragraphs:
+                    text = p.text.strip()
+                    if len(text) > 50:
+                        result = classifier(text[:512])[0]
+                        score = int(result['label'][0])
+                        mood = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"][score-1]
+                        sentiments += f"\nSection {count + 1}: {mood} ({score}/5 stars)"
+                        count += 1
+                        if count >= 5:
+                            break
+
+                return f"""😊 Sentiment Analysis
+
+    Title: {title}
+    {sentiments}"""
+
+            elif mode == "topics":
+                classifier = pipeline("zero-shot-classification",
+                                   model="facebook/bart-large-mnli")
+
+                topics = [
+                    "Technology", "AI/ML", "Business", "Science",
+                    "Innovation", "Research", "Industry News"
+                ]
+
+                results = classifier(text_content[:512], topics)
+
+                topic_analysis = "Detected Topics:\n"
+                for topic, score in zip(results['labels'], results['scores']):
+                    if score > 0.1:
+                        topic_analysis += f"- {topic}: {score*100:.1f}% confidence\n"
+
+                return f"""🎯 Topic Classification
+
+    Title: {title}
+
+    {topic_analysis}"""
+
+            else:
+                return f"Error: Unknown mode '{mode}'"
+
+        except Exception as e:
+            return f"Error processing webpage: {str(e)}"