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app.py
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from smolagents import launch_gradio_demo
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from tool import SimpleTool
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import gradio as gr
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import json
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from smolagents import load_tool
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import time
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from datetime import datetime
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import plotly.graph_objects as go
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from fpdf import FPDF
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import tempfile
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import os
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# Load the analyzer with caching
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analyzer = load_tool("MHamdan/smart-web-analyzer-plus", trust_remote_code=True)
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analysis_cache = {}
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def create_sentiment_chart(sentiment_data):
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"""Creates an interactive bar chart for sentiment analysis."""
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sections = []
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scores = []
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for item in sentiment_data['sections']:
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sections.append(f"Section {item['section']}")
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scores.append(item['score'])
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fig = go.Figure(data=[
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go.Bar(
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x=sections,
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y=scores,
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marker_color='rgb(55, 83, 109)',
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text=scores,
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textposition='auto'
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)
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])
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fig.update_layout(
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title='Sentiment Analysis by Section',
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xaxis_title='Content Sections',
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yaxis_title='Sentiment Score (1-5)',
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yaxis_range=[0, 5]
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)
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return fig
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def generate_pdf_report(analysis_result):
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"""Generates a PDF report from analysis results."""
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pdf = FPDF()
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pdf.add_page()
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# Header
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pdf.set_font('Arial', 'B', 16)
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pdf.cell(0, 10, 'Content Analysis Report', 0, 1, 'C')
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pdf.line(10, 30, 200, 30)
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# Date
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pdf.set_font('Arial', '', 10)
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pdf.cell(0, 10, f'Generated on: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}', 0, 1)
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# Content
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pdf.set_font('Arial', 'B', 12)
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pdf.cell(0, 10, 'Basic Statistics:', 0, 1)
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pdf.set_font('Arial', '', 10)
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stats = analysis_result.get('stats', {})
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for key, value in stats.items():
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pdf.cell(0, 10, f'{key.title()}: {value}', 0, 1)
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if 'summary' in analysis_result:
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pdf.set_font('Arial', 'B', 12)
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pdf.cell(0, 10, 'Summary:', 0, 1)
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pdf.set_font('Arial', '', 10)
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pdf.multi_cell(0, 10, analysis_result['summary'])
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# Save to temporary file
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temp_dir = tempfile.gettempdir()
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pdf_path = os.path.join(temp_dir, 'analysis_report.pdf')
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pdf.output(pdf_path)
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return pdf_path
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def process_content(input_text, mode, theme, progress=gr.Progress()):
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"""Main processing function with progress updates."""
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try:
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# Check cache
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cache_key = f"{input_text}_{mode}"
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if cache_key in analysis_cache:
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return (
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analysis_cache[cache_key],
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"Content preview unavailable for cached results",
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"Using cached results",
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None
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)
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# Process in steps
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progress(0, desc="Initializing analysis...")
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time.sleep(0.5) # Simulate processing
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progress(0.3, desc="Fetching content...")
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result = analyzer(input_text, mode)
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analysis_result = json.loads(result)
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progress(0.6, desc="Analyzing content...")
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# Create visualization if sentiment mode
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chart = None
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if mode == "sentiment" and analysis_result.get('status') == 'success':
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progress(0.8, desc="Generating visualizations...")
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chart = create_sentiment_chart(analysis_result['sentiment_analysis'])
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# Cache results
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analysis_cache[cache_key] = analysis_result
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# Generate preview text
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preview = analysis_result.get('stats', {}).get('title', '')
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if 'summary' in analysis_result:
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preview += f"\n\nSummary:\n{analysis_result['summary']}"
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progress(1.0, desc="Complete!")
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return analysis_result, preview, "Analysis complete!", chart
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except Exception as e:
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return (
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{"status": "error", "message": str(e)},
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"Error occurred",
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f"Error: {str(e)}",
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None
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)
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def create_interface():
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with gr.Blocks(title="Smart Web Analyzer Plus", theme=gr.themes.Base()) as iface:
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# Header
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gr.Markdown("# π Smart Web Analyzer Plus")
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gr.Markdown("""
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Advanced content analysis with AI-powered insights:
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* π Comprehensive Analysis
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* π Detailed Sentiment Analysis
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* π Smart Summarization
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* π― Topic Detection
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""")
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# Theme toggle
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with gr.Row():
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theme = gr.Radio(
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choices=["light", "dark"],
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value="light",
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label="Theme",
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interactive=True
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)
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# Main content
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with gr.Tabs():
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# Analysis Tab
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with gr.Tab("Analysis"):
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(
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label="URL or Text to Analyze",
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placeholder="Enter URL or paste text",
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lines=5
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)
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mode = gr.Radio(
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choices=["analyze", "summarize", "sentiment", "topics"],
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value="analyze",
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label="Analysis Mode"
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)
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analyze_btn = gr.Button("π Analyze", variant="primary")
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status = gr.Markdown("Status: Ready")
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with gr.Column():
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results = gr.JSON(label="Analysis Results")
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chart = gr.Plot(label="Visualization", visible=False)
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# Show/hide chart based on mode
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mode.change(
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lambda m: gr.update(visible=(m == "sentiment")),
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inputs=[mode],
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outputs=[chart]
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)
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# Preview Tab
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with gr.Tab("Preview"):
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preview = gr.Textbox(
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label="Content Preview",
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lines=10,
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interactive=False
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)
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# Report Tab
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with gr.Tab("Report"):
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download_btn = gr.Button("π₯ Download PDF Report")
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pdf_output = gr.File(label="Generated Report")
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# Examples
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gr.Examples(
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examples=[
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["https://www.artificialintelligence-news.com/2024/02/14/openai-anthropic-google-white-house-red-teaming/", "analyze", "light"],
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["https://www.artificialintelligence-news.com/2024/02/13/ai-21-labs-wordtune-chatgpt-plugin/", "sentiment", "light"]
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],
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inputs=[input_text, mode, theme],
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outputs=[results, preview, status, chart],
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fn=process_content,
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cache_examples=True
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)
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# Handle theme changes
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theme.change(
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lambda t: gr.update(theme=gr.themes.Base() if t == "light" else gr.themes.Soft()),
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inputs=[theme],
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outputs=[iface]
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)
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# Wire up the analysis button
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analyze_btn.click(
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fn=process_content,
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inputs=[input_text, mode, theme],
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outputs=[results, preview, status, chart]
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)
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# Wire up PDF download
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download_btn.click(
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fn=lambda: generate_pdf_report(json.loads(results.value)),
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inputs=[],
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outputs=[pdf_output]
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)
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return iface
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demo = create_interface()
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demo.launch()
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