app.py

import os
import uuid
import gradio as gr
from openai import OpenAI

# Get API key from environment
api_key = os.environ.get("NEBIUS_API_KEY")
if not api_key:
    print("Warning: NEBIUS_API_KEY environment variable not set. LLM features will be disabled.")
    client = None
else:
    client = OpenAI(
        base_url="https://api.studio.nebius.com/v1/",
        api_key=api_key,
    )

# Import visualization tools and helper
from tools.visualizer_linked_list import generate_linked_list_gif
from tools.visualizer_sort import generate_bubble_sort_gif
from tools.utility_helpers import ensure_output_dir
from tools.code_analyzer import CodeAnalyzer

ensure_output_dir()  # make sure ./output/ exists
code_analyzer = CodeAnalyzer()

PLACEHOLDER_IMG = os.path.join("output", "no_visualization.gif")

def create_placeholder_gif():
    # Create a simple placeholder GIF if it doesn't exist
    if not os.path.exists(PLACEHOLDER_IMG):
        from PIL import Image, ImageDraw
        img = Image.new('RGB', (400, 100), color='white')
        d = ImageDraw.Draw(img)
        d.text((10, 40), "No visualization available", fill=(0, 0, 0))
        img.save(PLACEHOLDER_IMG, format='GIF')

create_placeholder_gif()

def explain_algorithm_with_llm(code_snippet: str, code_info: dict = None):
    """
    Send a prompt to Nebius-hosted LLM (Meta-Llama-3.1-70B-Instruct).
    Returns the model's explanation text.
    """
    if not client:
        return "LLM features are disabled. Please set the NEBIUS_API_KEY environment variable to enable AI explanations."
    
    if code_info:
        prompt = code_analyzer.get_explanation_prompt(code_info)
        prompt += f"\n\nCode:\n{code_snippet}"
    else:
        prompt = f"Please explain this code step by step:\n\n{code_snippet}"
    
    response = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3.1-70B-Instruct",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.6
    )
    return response.choices[0].message.content

def extract_array_from_code(code: str):
    import re
    # Try to find Python list or comma-separated numbers
    array_match = re.search(r'\[([\d\s,]+)\]', code)
    if array_match:
        try:
            return [int(x.strip()) for x in array_match.group(1).split(',') if x.strip()]
        except Exception:
            pass
    # Try to find comma-separated numbers in the code
    csv_match = re.search(r'(\d+(?:\s*,\s*\d+)+)', code)
    if csv_match:
        try:
            return [int(x.strip()) for x in csv_match.group(1).split(',') if x.strip()]
        except Exception:
            pass
    return None

def extract_linked_list_ops_from_code(code: str):
    import re
    operations = []
    for line in code.split('\n'):
        if 'insert' in line.lower():
            num_match = re.search(r'insert\s+(\d+)', line.lower())
            if num_match:
                operations.append(f"insert {num_match.group(1)}")
        elif 'delete' in line.lower():
            operations.append("delete")
    return operations if operations else None

def generate_visualization(code_info: dict, code: str) -> str:
    """
    Generate appropriate visualization based on code analysis.
    Returns the path to the generated GIF.
    """
    gif_name = f"{uuid.uuid4()}.gif"
    output_path = os.path.join("output", gif_name)
    
    # Extract values for visualization from the code
    try:
        # For sorting algorithms, try to find array/list in the code
        if any('sort' in algo for algo in code_info['patterns']['algorithms']):
            values = extract_array_from_code(code)
            if values:
                generate_bubble_sort_gif(values, output_path)
                return output_path
        
        # For linked list operations
        if any('linked_list' in ds for ds in code_info['patterns']['data_structures']):
            operations = extract_linked_list_ops_from_code(code)
            if operations:
                generate_linked_list_gif(operations, output_path)
                return output_path
    except Exception as e:
        print(f"Error generating visualization: {e}")
    
    return PLACEHOLDER_IMG

def process_code(code_snippet: str):
    """
    Process the input code:
    1. Analyze the code
    2. Generate visualization
    3. Get LLM explanation
    """
    # Analyze the code
    code_info = code_analyzer.analyze_code(code_snippet)
    debug_log = []
    if code_info.get('type') == 'error':
        return f"❌ Code analysis failed: {code_info.get('error')}", PLACEHOLDER_IMG
    
    # Generate visualization
    visualization_path = generate_visualization(code_info, code_snippet)
    no_visual = (visualization_path == PLACEHOLDER_IMG)
    
    # Get LLM explanation
    explanation = explain_algorithm_with_llm(code_snippet, code_info)
    
    # Create a beautiful markdown output
    markdown_output = f"""
# 📊 Code Analysis Report

## 🎯 Code Type & Name
- **Type:** {code_info['type'].title()}
{f"- **Name:** `{code_info['name']}`" if code_info['name'] else ""}
- **Complexity:** `{code_info['complexity']}`

## 🔍 Detected Patterns
"""
    
    if code_info['patterns']['algorithms']:
        markdown_output += "\n### 🧮 Algorithms\n"
        for algo in code_info['patterns']['algorithms']:
            markdown_output += f"- {algo.replace(':', ': ').title()}\n"
    
    if code_info['patterns']['data_structures']:
        markdown_output += "\n### 📚 Data Structures\n"
        for ds in code_info['patterns']['data_structures']:
            markdown_output += f"- {ds.replace(':', ': ').title()}\n"
    
    markdown_output += "\n## 📝 Code Structure\n"
    structure = code_info['structure']
    markdown_output += f"- {'✅' if structure['has_recursion'] else '❌'} Recursion\n"
    markdown_output += f"- {'✅' if structure['has_classes'] else '❌'} Classes\n"
    markdown_output += f"- {'✅' if structure['has_functions'] else '❌'} Functions\n"
    markdown_output += f"- {'✅' if structure['has_loops'] else '❌'} Loops\n"
    
    if no_visual:
        markdown_output += """
## ⚠️ Visualization Note
No visualization could be generated for this code.

**Supported Visualizations:**
- Bubble Sort (with array)
- Linked List (with 'insert N'/'delete' lines)
"""
    
    markdown_output += "\n## 📖 Explanation\n"
    markdown_output += explanation
    
    return markdown_output, visualization_path

# Custom CSS
css = """
.code-input {
    font-family: 'Consolas', 'Monaco', monospace !important;
    font-size: 14px !important;
}
.explanation-output {
    font-size: 14px !important;
    line-height: 1.6 !important;
}
.visualization-output {
    border-radius: 8px !important;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
}
"""

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    gr.Markdown("""
    # 🧠 CodeViz: Visual Learning of Algorithms
    
    Enter your code below to get:
    1. A detailed explanation of how it works
    2. A visualization of the algorithm/data structure
    3. Analysis of its complexity and patterns
    
    Examples:
    ```python
    # Bubble Sort
    def bubble_sort(arr):
        n = len(arr)
        for i in range(n):
            for j in range(0, n-i-1):
                if arr[j] > arr[j+1]:
                    arr[j], arr[j+1] = arr[j+1], arr[j]
        return arr
    
    # Test with array
    arr = [64, 34, 25, 12, 22, 11, 90]
    ```
    
    ```python
    # Linked List Insert
    class Node:
        def __init__(self, data):
            self.data = data
            self.next = None
    
    def insert_at_end(head, data):
        new_node = Node(data)
        if head is None:
            return new_node
        current = head
        while current.next:
            current = current.next
        current.next = new_node
        return head
    ```
    """)

    with gr.Row():
        with gr.Column(scale=1):
            code_input = gr.Textbox(
                label="Enter your code", 
                lines=10, 
                placeholder="Paste your code here...",
                elem_classes=["code-input"],
                type="text"
            )
            run_btn = gr.Button("Visualize & Explain", variant="primary")
        
        with gr.Column(scale=1):
            explanation_output = gr.Markdown(
                label="Explanation & Analysis",
                elem_classes=["explanation-output"]
            )
            visualization_output = gr.Image(
                label="Visualization",
                type="filepath",
                elem_classes=["visualization-output"]
            )

    run_btn.click(
        fn=process_code,
        inputs=[code_input],
        outputs=[explanation_output, visualization_output],
        api_name="process_code"
    )

if __name__ == "__main__":
    demo.queue().launch(
        server_name="0.0.0.0", 
        server_port=7860,
        share=True,  # Enable sharing for easier access
        show_error=True  # Show detailed error messages
    )