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README.md

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 ---
 title: Chunking Visualizer
-emoji: 🐢
-colorFrom: blue
+emoji: ✂️
+colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 5.9.1
 app_file: app.py
 pinned: false
+license: mit
+short_description: See how chunking strategies split your documents
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Chunking Visualizer
+
+Visualize how different chunking strategies split your documents. Spot problems before they hit production.
+
+Part of the **AI for Product Managers** course by Data Trainers LLC.

app.py

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+import gradio as gr
+import re
+
+SAMPLE_DOCS = {
+    "FAQ Document": """Q: What is your return policy?
+A: You can return most items within 30 days of purchase for a full refund. Items must be in original condition with tags attached.
+
+Q: How long does shipping take?
+A: Standard shipping takes 5-7 business days. Express shipping takes 2-3 business days.
+
+Q: Do you offer international shipping?
+A: Yes, we ship to over 50 countries. International shipping typically takes 10-14 business days.
+
+Q: How do I track my order?
+A: Once your order ships, you'll receive an email with tracking information. You can also check order status in your account.
+
+Q: What payment methods do you accept?
+A: We accept Visa, Mastercard, American Express, PayPal, and Apple Pay.""",
+
+    "Product Documentation": """Smart Thermostat Pro - User Guide
+
+Installation:
+Turn off power at the circuit breaker before beginning installation. Remove your old thermostat and take a photo of the wiring. The Smart Thermostat Pro is compatible with most 24V heating and cooling systems.
+
+Setup:
+Download the SmartHome app and create an account. The thermostat will automatically enter pairing mode when powered on. Follow the in-app instructions to connect to your WiFi network.
+
+Daily Use:
+The touchscreen displays current temperature and humidity. Swipe left or right to adjust target temperature. Tap the calendar icon to view and edit your schedule.
+
+Energy Saving Features:
+Auto-Away detects when you leave and adjusts temperature to save energy. The monthly energy report shows your usage patterns and savings. Eco mode reduces heating/cooling by 2 degrees to save up to 15% on energy bills.
+
+Troubleshooting:
+If the display is blank, check that power is connected at the circuit breaker. If WiFi won't connect, ensure your network is 2.4GHz (5GHz is not supported). For heating/cooling issues, verify the system wires match the terminal labels.""",
+
+    "Policy Document": """Employee Remote Work Policy
+
+1. Eligibility
+All full-time employees who have completed their probationary period are eligible for remote work. Certain roles requiring physical presence are exempt from this policy.
+
+2. Core Hours
+Remote employees must be available from 10am to 3pm in their local timezone. This ensures overlap for team collaboration and meetings.
+
+3. Equipment
+The company provides a laptop and external monitor. Employees are responsible for maintaining reliable internet connectivity with minimum 25 Mbps speed.
+
+4. Communication
+Employees must respond to messages within 2 hours during core hours. All meetings should be attended with camera on unless otherwise specified.
+
+5. Performance
+Remote work privileges are contingent on meeting performance expectations. Managers will review remote work arrangements quarterly.
+
+6. Expenses
+Home office setup stipend: $500 one-time. Monthly internet reimbursement: up to $50. Coworking space usage requires pre-approval."""
+}
+
+
+def chunk_fixed_size(text, size, overlap_pct):
+    """Fixed-size chunking."""
+    overlap = int(size * overlap_pct / 100)
+    chunks = []
+    start = 0
+    while start < len(text):
+        end = min(start + size, len(text))
+        chunks.append(text[start:end])
+        start = end - overlap if overlap > 0 else end
+    return chunks
+
+
+def chunk_sentence(text, sentences_per_chunk):
+    """Sentence-based chunking."""
+    sentences = re.split(r'(?<=[.!?])\s+', text)
+    chunks = []
+    for i in range(0, len(sentences), sentences_per_chunk):
+        chunk = ' '.join(sentences[i:i + sentences_per_chunk])
+        if chunk.strip():
+            chunks.append(chunk.strip())
+    return chunks
+
+
+def chunk_paragraph(text):
+    """Paragraph-based chunking."""
+    paragraphs = text.split('\n\n')
+    return [p.strip() for p in paragraphs if p.strip()]
+
+
+def chunk_qa_pairs(text):
+    """Q&A pair chunking (for FAQ documents)."""
+    pattern = r'(Q:.*?A:.*?)(?=Q:|$)'
+    matches = re.findall(pattern, text, re.DOTALL)
+    return [m.strip() for m in matches if m.strip()]
+
+
+def visualize_chunks(text, strategy, chunk_size, overlap_pct, sentences_per_chunk):
+    """Generate chunk visualization."""
+    if not text.strip():
+        return "Please provide text to chunk.", "", ""
+
+    # Apply chunking strategy
+    if strategy == "Fixed Size":
+        chunks = chunk_fixed_size(text, chunk_size, overlap_pct)
+    elif strategy == "Sentence-Based":
+        chunks = chunk_sentence(text, sentences_per_chunk)
+    elif strategy == "Paragraph-Based":
+        chunks = chunk_paragraph(text)
+    elif strategy == "Q&A Pairs":
+        chunks = chunk_qa_pairs(text)
+    else:
+        chunks = [text]
+
+    if not chunks:
+        return "No chunks generated. Try a different strategy.", "", ""
+
+    # Calculate stats
+    total_chars = sum(len(c) for c in chunks)
+    avg_size = total_chars / len(chunks)
+    min_size = min(len(c) for c in chunks)
+    max_size = max(len(c) for c in chunks)
+
+    # Check for problems
+    problems = []
+    split_sentences = 0
+    for i, chunk in enumerate(chunks):
+        if not chunk.rstrip().endswith(('.', '!', '?', '"')) and i < len(chunks) - 1:
+            split_sentences += 1
+
+    if split_sentences > 0:
+        problems.append(f"⚠️ {split_sentences} chunks end mid-sentence")
+    if min_size < 50:
+        problems.append(f"⚠️ Some chunks are very small ({min_size} chars)")
+    if max_size > 2000:
+        problems.append(f"⚠️ Some chunks are very large ({max_size} chars)")
+
+    # Stats display
+    stats = f"""### Chunking Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Chunks | {len(chunks)} |
+| Average Size | {avg_size:.0f} characters |
+| Min Size | {min_size} characters |
+| Max Size | {max_size} characters |
+| Total Characters | {total_chars} |
+
+"""
+    if problems:
+        stats += "### ⚠️ Potential Issues\n" + "\n".join(problems)
+    else:
+        stats += "### ✅ No obvious issues detected"
+
+    # Chunk display with color coding
+    colors = ['#E6F7F5', '#D4F0EC', '#C2E9E3', '#B0E2DA', '#9EDBD1', '#8CD4C8', '#7ACDBF']
+    chunk_display = "### Chunk Preview\n\n"
+
+    for i, chunk in enumerate(chunks[:10]):  # Show first 10
+        color = colors[i % len(colors)]
+        ends_mid_sentence = not chunk.rstrip().endswith(('.', '!', '?', '"')) and i < len(chunks) - 1
+        border = "2px solid #dc2626" if ends_mid_sentence else "1px solid #40B8A6"
+        warning = " ⚠️ *ends mid-sentence*" if ends_mid_sentence else ""
+
+        preview = chunk[:200] + "..." if len(chunk) > 200 else chunk
+        chunk_display += f"**Chunk {i+1}** ({len(chunk)} chars){warning}\n```\n{preview}\n```\n\n"
+
+    if len(chunks) > 10:
+        chunk_display += f"*... and {len(chunks) - 10} more chunks*"
+
+    return stats, chunk_display, f"Strategy: {strategy} | Chunks: {len(chunks)}"
+
+
+def load_sample(sample_name):
+    return SAMPLE_DOCS.get(sample_name, "")
+
+
+# Build interface
+with gr.Blocks(title="Chunking Visualizer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        "# Chunking Visualizer\n\n"
+        "**PM Decision:** Your engineering team says they'll 'chunk the documents.' "
+        "This tool shows you exactly what that means and helps you spot potential problems "
+        "before they affect retrieval quality.\n\n"
+        "Try different strategies and see how they split your documents."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            sample_dropdown = gr.Dropdown(
+                choices=list(SAMPLE_DOCS.keys()),
+                label="Load Sample Document",
+                value="FAQ Document"
+            )
+            text_input = gr.Textbox(
+                label="Document Text",
+                placeholder="Paste your document here...",
+                lines=12,
+                value=SAMPLE_DOCS["FAQ Document"]
+            )
+
+            strategy = gr.Radio(
+                choices=["Fixed Size", "Sentence-Based", "Paragraph-Based", "Q&A Pairs"],
+                label="Chunking Strategy",
+                value="Fixed Size"
+            )
+
+            with gr.Row():
+                chunk_size = gr.Slider(100, 1000, value=300, step=50, label="Chunk Size (chars)")
+                overlap = gr.Slider(0, 50, value=10, step=5, label="Overlap (%)")
+
+            sentences = gr.Slider(1, 10, value=3, step=1, label="Sentences per Chunk")
+
+            visualize_btn = gr.Button("Visualize Chunks", variant="primary")
+
+        with gr.Column(scale=1):
+            summary_output = gr.Textbox(label="Summary", interactive=False)
+            stats_output = gr.Markdown(label="Statistics")
+            chunks_output = gr.Markdown(label="Chunks")
+
+    # Events
+    sample_dropdown.change(load_sample, sample_dropdown, text_input)
+
+    visualize_btn.click(
+        visualize_chunks,
+        inputs=[text_input, strategy, chunk_size, overlap, sentences],
+        outputs=[stats_output, chunks_output, summary_output]
+    )
+
+    # Auto-update on strategy change
+    strategy.change(
+        visualize_chunks,
+        inputs=[text_input, strategy, chunk_size, overlap, sentences],
+        outputs=[stats_output, chunks_output, summary_output]
+    )
+
+    gr.Markdown(
+        "---\n"
+        "**PM Takeaway:** The right chunking strategy depends on your document type. "
+        "FAQs work best with Q&A pair chunking. Product docs work with paragraph or sentence-based. "
+        "Always test with real queries to verify retrieval quality.\n\n"
+        "*AI for Product Managers*"
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+# No additional requirements - uses Gradio only