Upload tool

app.py

@@ -1,68 +1,6 @@
+from smolagents import launch_gradio_demo
+from tool import SimpleTool
 
-import gradio as gr
-from smolagents import load_tool
-import torch  # Add explicit torch import
+tool = SimpleTool()
 
-# Ensure CUDA is available
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load the tool
-web_analyzer = load_tool("MHamdan/web-analyzer", trust_remote_code=True)
-
-def create_interface():
-    with gr.Blocks(title="AI Web Analyzer") as iface:
-        gr.Markdown("# 🤖 AI-Powered Web Content Analyzer")
-        gr.Markdown("""
-        ## Features:
-        - 📊 **Analyze**: Complete content analysis with AI summary
-        - 📝 **Summarize**: AI-generated multi-section summary
-        - 😊 **Sentiment**: Section-by-section sentiment analysis
-        - 🎯 **Topics**: AI topic classification
-        """)
-        
-        with gr.Row():
-            with gr.Column():
-                url_input = gr.Textbox(
-                    label="Webpage URL",
-                    placeholder="Enter URL to analyze..."
-                )
-                mode = gr.Dropdown(
-                    choices=["analyze", "summarize", "sentiment", "topics"],
-                    label="Analysis Mode",
-                    value="analyze"
-                )
-                submit_btn = gr.Button("Analyze Content", variant="primary")
-                
-            with gr.Column():
-                output = gr.Textbox(
-                    label="AI Analysis Results",
-                    lines=15
-                )
-
-        # Example data using artificial-intelligence-news.com       
-        examples = [
-            ["https://www.artificialintelligence-news.com/2024/02/14/openai-anthropic-google-white-house-red-teaming/", "analyze"],
-            ["https://www.artificialintelligence-news.com/2024/02/13/ai-21-labs-wordtune-chatgpt-plugin/", "summarize"],
-            ["https://www.artificialintelligence-news.com/2024/02/12/google-responds-gemini-ai-historical-images/", "sentiment"],
-            ["https://www.artificialintelligence-news.com/2024/02/09/anthropic-claude-3-models-preview/", "topics"]
-        ]
-        
-        gr.Examples(
-            examples=examples,
-            inputs=[url_input, mode],
-            outputs=output,
-            label="Try these examples:",
-            cache_examples=True
-        )
-        
-        submit_btn.click(
-            fn=web_analyzer,
-            inputs=[url_input, mode],
-            outputs=output
-        )
-    
-    return iface
-
-# Create and launch the interface
-demo = create_interface()
-demo.launch()
+launch_gradio_demo(tool)

requirements.txt

@@ -1,12 +1,5 @@
-
-gradio>=4.0.0
-beautifulsoup4>=4.9.3
-requests>=2.25.1
-smolagents
+bs4
+requests
 transformers
 torch
-accelerate
-sacremoses
-sentencepiece
-protobuf
-scipy
+smolagents

tool.py

@@ -21,48 +21,53 @@ class SimpleTool(Tool):
         from bs4 import BeautifulSoup
         import re
         from transformers import pipeline
+        import torch
+
+        # Check if GPU is available
+        device = 0 if torch.cuda.is_available() else -1
 
         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')
+
+            # Remove scripts and styles
             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 len(text_content) < 100:
+                return "Error: Not enough content to analyze"
+
             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")
+                try:
+                    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
+                    classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device)
 
-                # 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]
+                    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
+                    return f"""📊 Content Analysis
 
     Title: {title}
-    Length: {len(text_content)} characters
 
     📝 AI Summary:
     {summary}
 
-    😊 Overall Sentiment: {sent_text} ({sent_score}/5)"""
+    😊 Overall Sentiment: {sent_text} ({sent_score}/5)
+
+    Length: {len(text_content)} characters"""
+
+                except Exception as e:
+                    return f"Error with AI analysis: {str(e)}. Please check if PyTorch and transformers are properly installed."
+
 
             elif mode == "summarize":
                 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")