app.py

import gradio as gr
import requests
import os
import logging
import re
import random
import json

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# ======= CODE ANALYSIS CLASS ========
class CodeRoaster:
    def __init__(self):
        # Roast templates for different levels
        self.roast_templates = {
            "Mild": [
                "I see some opportunities for improvement in this code. It's not bad, but we can make it better!",
                "This code would benefit from a few tweaks. Let me show you how.",
                "Your code works, but there are some best practices we could apply here."
            ],
            "Medium": [
                "This code looks like it was written the night before a deadline. Time for some tough love!",
                "I've seen better code written by first-year CS students. Let's clean this up.",
                "Your code needs a serious makeover. It's like showing up to a wedding in sweatpants."
            ],
            "Savage": [
                "This code is what happens when Stack Overflow answers are copied without understanding. Pure chaos!",
                "Did you write this code during a power outage? While blindfolded? On a keyboard missing half its keys?",
                "If this code were a restaurant dish, Gordon Ramsay would throw it in the trash and shut down the kitchen."
            ]
        }
        
        # Issue templates for different languages
        self.python_issue_detectors = [
            {
                'pattern': r"range\s*\(\s*0\s*,",
                'issue': "unnecessary zero in range",
                'suggestion': "range() starts at 0 by default, so range(0, n) can be simplified to range(n)",
                'fix': lambda code: re.sub(r"range\s*\(\s*0\s*,\s*([^\)]+)\)", r"range(\1)", code)
            },
            {
                'pattern': r"print\s*\(",
                'issue': "print statements used for output",
                'suggestion': "Consider using logging for better control in production environments",
                'fix': lambda code: code  # No automatic fix
            },
            {
                'pattern': r"if\s+\*\*name\*\*\s+==",
                'issue': "incorrect __name__ syntax",
                'suggestion': 'Use if __name__ == "__main__": instead of if **name** == "__main__":',
                'fix': lambda code: re.sub(r"if\s+\*\*name\*\*\s+==", r"if __name__ ==", code)
            },
            {
                'pattern': r"def\s+\w+\s*\([^\)]*\)\s*:\s*(?!\n\s*[\"\'])",
                'issue': "missing docstring",
                'suggestion': "Add docstrings to functions explaining purpose, parameters and return values",
                'fix': lambda code: code  # No automatic fix, requires understanding function purpose
            },
            {
                'pattern': r"  [^\n]+",
                'issue': "inconsistent indentation",
                'suggestion': "Use consistent indentation (4 spaces per level is the Python standard)",
                'fix': lambda code: re.sub(r"  (?! {2})", r"    ", code)
            },
            {
                'pattern': r"except:",
                'issue': "bare except clause",
                'suggestion': "Specify exceptions to catch instead of using a bare except clause",
                'fix': lambda code: re.sub(r"except:", r"except Exception:", code)
            },
            {
                'pattern': r"from\s+\w+\s+import\s+\*",
                'issue': "wildcard import",
                'suggestion': "Import specific modules or functions instead of using wildcard imports",
                'fix': lambda code: code  # No automatic fix, requires knowledge of what's needed
            },
            {
                'pattern': r"i\s*\+=\s*1",
                'issue': "manual index increment in loop",
                'suggestion': "Consider using enumerate() for clean iteration with indices",
                'fix': lambda code: code  # No automatic fix, requires restructuring
            },
            {
                'pattern': r"with\s+open\([^,]+\)\s+as",
                'issue': "missing file mode in open()",
                'suggestion': "Specify the file mode in open(filename, mode)",
                'fix': lambda code: re.sub(r"with\s+open\(([^,]+)\)\s+as", r"with open(\1, 'r') as", code)
            }
        ]
        
        self.js_issue_detectors = [
            {
                'pattern': r"var\s+",
                'issue': "using var instead of let/const",
                'suggestion': "Use let for variables that change and const for variables that don't change",
                'fix': lambda code: re.sub(r"var\s+", r"let ", code)  # Simple replacement, not always correct
            },
            {
                'pattern': r"==(?!=)",
                'issue': "loose equality operators",
                'suggestion': "Use === instead of == for strict equality checking",
                'fix': lambda code: re.sub(r"==(?!=)", r"===", code)
            },
            {
                'pattern': r"console\.log\(",
                'issue': "console.log statements left in code",
                'suggestion': "Remove console.log statements before production deployment",
                'fix': lambda code: code  # No automatic fix
            },
            {
                'pattern': r"document\.write\(",
                'issue': "using document.write",
                'suggestion': "Avoid document.write as it can overwrite the entire document",
                'fix': lambda code: code  # No automatic fix
            },
            {
                'pattern': r"setTimeout\(\s*function\s*\(\)\s*{\s*},\s*0\)",
                'issue': "setTimeout with 0 delay",
                'suggestion': "Consider using requestAnimationFrame instead for browser animations",
                'fix': lambda code: code  # No automatic fix
            }
        ]
        
        self.generic_issue_detectors = [
            {
                'pattern': r"(.{80,})\n",
                'issue': "lines exceeding 80 characters",
                'suggestion': "Keep lines under 80 characters for better readability",
                'fix': lambda code: code  # No automatic fix, requires manual line splitting
            },
            {
                'pattern': r"(\/\/|\#)\s*TODO",
                'issue': "TODO comments in code",
                'suggestion': "Resolve TODO comments before considering code complete",
                'fix': lambda code: code  # No automatic fix
            },
            {
                'pattern': r"[^\w](\w{1,2})[^\w]",
                'issue': "short variable names",
                'suggestion': "Use descriptive variable names that explain their purpose",
                'fix': lambda code: code  # No automatic fix, requires understanding context
            }
        ]
    
    def detect_language(self, code):
        """Detect what programming language the code is written in"""
        # Python detection
        if "def " in code or "import " in code or "class " in code and ":" in code:
            return "Python"
        # JavaScript detection
        elif "function " in code or "var " in code or "let " in code or "const " in code:
            return "JavaScript"
        # Unknown language
        else:
            return "unknown"
    
    def analyze_code(self, code, language):
        """Analyze code for issues based on detected language"""
        issues = []
        suggestions = []
        fix_functions = []
        
        # Select appropriate detectors based on language
        if language == "Python":
            detectors = self.python_issue_detectors + self.generic_issue_detectors
        elif language == "JavaScript":
            detectors = self.js_issue_detectors + self.generic_issue_detectors
        else:
            detectors = self.generic_issue_detectors
        
        # Apply each detector
        for detector in detectors:
            if re.search(detector['pattern'], code):
                issues.append(detector['issue'])
                suggestions.append(detector['suggestion'])
                fix_functions.append(detector['fix'])
        
        # Check for missing newline at end of file
        if code.count('\n') > 3 and not code.strip().endswith('\n'):
            issues.append("missing newline at end of file")
            suggestions.append("Add a newline at the end of the file (standard best practice)")
            fix_functions.append(lambda code: code + '\n')
        
        return issues, suggestions, fix_functions
    
    def generate_roast(self, issues, level):
        """Generate a roast message based on the issues found and the roast level"""
        base_roast = random.choice(self.roast_templates[level])
        
        if issues:
            if level == "Mild":
                roast = f"{base_roast}\n\nI noticed these {len(issues)} issues that could be improved: {', '.join(issues)}."
            elif level == "Medium":
                roast = f"{base_roast}\n\nSpecifically, I found {len(issues)} issues that need attention: {', '.join(issues)}."
            else:  # Savage
                roast = f"{base_roast}\n\nThis masterpiece of chaos has {len(issues)} issues: {', '.join(issues)}."
        else:
            if level == "Mild":
                roast = f"{base_roast}\n\nYour code is actually pretty clean, but there are always ways to improve."
            elif level == "Medium":
                roast = f"{base_roast}\n\nSurprisingly, your code doesn't have major issues, but let's not celebrate too early."
            else:  # Savage
                roast = f"{base_roast}\n\nI was ready to demolish this code, but it's... acceptable. Don't get used to compliments."
        
        return roast
    
    def fix_code(self, code, fix_functions):
        """Apply all fix functions to the code"""
        fixed_code = code
        for fix_func in fix_functions:
            fixed_code = fix_func(fixed_code)
        return fixed_code
    
    def generate_explanation(self, issues, suggestions, language):
        """Generate an explanation of the issues and suggestions"""
        explanation = "Here's what I found and improved:\n\n"
        
        if issues:
            for i, (issue, suggestion) in enumerate(zip(issues, suggestions)):
                explanation += f"{i+1}. **{issue.capitalize()}**: {suggestion}\n"
        else:
            explanation += "- Made minor formatting improvements for better readability\n"
        
        if language != "unknown":
            explanation += f"\nYour code appears to be written in {language}. "
        
        explanation += "\nWriting clean, consistent code helps with maintainability and reduces bugs!"
        
        return explanation
    
    def roast_code(self, code, roast_level):
        """Main function to analyze, roast and fix code"""
        if not code.strip():
            return "Please enter some code to roast.", "No code provided.", "Nothing to fix."
        
        try:
            # Detect language
            language = self.detect_language(code)
            logger.info(f"Detected language: {language}")
            
            # Analyze code
            issues, suggestions, fix_functions = self.analyze_code(code, language)
            logger.info(f"Found {len(issues)} issues")
            
            # Generate roast
            roast = self.generate_roast(issues, roast_level)
            
            # Fix code
            fixed_code = self.fix_code(code, fix_functions)
            
            # Generate explanation
            explanation = self.generate_explanation(issues, suggestions, language)
            
            return roast, fixed_code, explanation
            
        except Exception as e:
            logger.error(f"Error analyzing code: {str(e)}")
            return (
                f"Oops! Something went wrong while roasting your code: {str(e)}",
                code,
                "Error generating explanation."
            )

# ======= HUGGING FACE API INTEGRATION ========
def call_huggingface_api(code, roast_level, api_key, model_name):
    if not code.strip():
        return "Please enter some code to roast.", "No code provided.", "Nothing to fix."
    
    if not api_key.strip():
        return "Please enter a valid Hugging Face API key.", code, "API key is required."
    
    try:
        logger.info(f"Calling Hugging Face API with model: {model_name}")
        
        # Create different prompts based on roast level
        roast_level_descriptions = {
            "Mild": "gentle but humorous",
            "Medium": "moderately sarcastic and pointed",
            "Savage": "brutally honest and hilariously critical"
        }
        
        # Better prompt format with less chance of format confusion
        prompt = f"""You're a senior software engineer reviewing this code:

```
{code}
```

Respond with THREE separate sections:

1. ROAST: Give a {roast_level_descriptions[roast_level]} code review. Be humorous but point out real issues.

2. FIXED_CODE: Provide an improved version of the code.

3. EXPLANATION: Explain what was improved and why it matters.

Start each section with the heading exactly as shown above.
"""
        
        # API URL based on selected model
        API_URL = f"https://api-inference.huggingface.co/models/{model_name}"
        
        # Set up the headers with API key
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        
        # Set up the payload
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 2048,
                "temperature": 0.7,
                "top_p": 0.9,
                "top_k": 50,
                "repetition_penalty": 1.2,
                "stop": ["<|endoftext|>", "</s>"]
            }
        }
        
        # Make the API call
        response = requests.post(API_URL, headers=headers, json=payload)
        
        # Check if the request was successful
        if response.status_code != 200:
            logger.error(f"API Error: {response.status_code} - {response.text}")
            return f"Error: {response.status_code} - {response.text}", code, "API call failed."
        
        # Extract the response text
        try:
            response_text = response.json()[0]["generated_text"]
            logger.info(f"Raw API response received with length: {len(response_text)}")
            
            # Remove the original prompt if it's included in the response
            if prompt in response_text:
                response_text = response_text.replace(prompt, "")
            
            # Now extract each section using regex
            roast_match = re.search(r"ROAST:(.*?)(?=FIXED_CODE:|$)", response_text, re.DOTALL)
            code_match = re.search(r"FIXED_CODE:(.*?)(?=EXPLANATION:|$)", response_text, re.DOTALL)
            explanation_match = re.search(r"EXPLANATION:(.*?)$", response_text, re.DOTALL)
            
            # Extract content from matches
            roast = roast_match.group(1).strip() if roast_match else "Failed to generate roast."
            
            # For the code section, also look for code blocks
            if code_match:
                code_section = code_match.group(1).strip()
                # Try to extract code between triple backticks if present
                code_block_match = re.search(r"```(?:\w+)?\n(.*?)```", code_section, re.DOTALL)
                if code_block_match:
                    fixed_code = code_block_match.group(1).strip()
                else:
                    # If no code blocks, use the whole section
                    fixed_code = code_section
            else:
                fixed_code = "Failed to generate fixed code."
            
            explanation = explanation_match.group(1).strip() if explanation_match else "Failed to generate explanation."
            
            # If sections are still missing or empty, provide default messages
            if not roast or roast == "":
                roast = "The model didn't generate a proper roast. Try again or use local mode."
            
            if not fixed_code or fixed_code == "":
                fixed_code = code
            
            if not explanation or explanation == "":
                explanation = "The model didn't generate a proper explanation. Try again or use local mode."
            
            logger.info("Successfully parsed response from Hugging Face API")
            return roast, fixed_code, explanation
            
        except Exception as e:
            logger.error(f"Error parsing API response: {e}")
            logger.error(f"Response content: {response.content[:500]}")  # Log first 500 chars
            return "Error parsing API response", code, f"Error details: {str(e)}"
    
    except Exception as e:
        logger.error(f"Error calling Hugging Face API: {str(e)}")
        return (
            f"Oops! Something went wrong while roasting your code: {str(e)}",
            code,
            "Error generating explanation."
        )

# ======= MAIN ROASTING FUNCTION ========
def roast_code(code, roast_level, use_api, api_key="", model_choice="Mistral-7B"):
    # Map model choice to actual model name
    model_map = {
        "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2",
        "Falcon-7B": "tiiuae/falcon-7b-instruct",
        "Llama-2-7B": "meta-llama/Llama-2-7b-chat-hf",
        "CodeLlama-7B": "codellama/CodeLlama-7b-instruct-hf"
    }
    
    model_name = model_map.get(model_choice, model_map["Mistral-7B"])
    
    if use_api and api_key.strip():
        # Use the API with specified model
        logger.info(f"Using API with model: {model_name}")
        return call_huggingface_api(code, roast_level, api_key, model_name)
    else:
        # Use the local analysis
        logger.info("Using local code analysis")
        roaster = CodeRoaster()
        return roaster.roast_code(code, roast_level)

# ======= GRADIO INTERFACE ========
def create_interface():
    with gr.Blocks(title="AI Roast My Code") as app:
        gr.Markdown("# 🔥 AI Roast My Code 🔥")
        gr.Markdown("Let our AI roast your code, fix it, and teach you - all while making you laugh!")
        
        with gr.Row():
            with gr.Column(scale=3):
                code_input = gr.Textbox(
                    label="Your Code",
                    lines=15
                )
            with gr.Column(scale=1):
                roast_level = gr.Radio(
                    choices=["Mild", "Medium", "Savage"],
                    label="Roast Level",
                    value="Medium"
                )
                
                use_api = gr.Checkbox(label="Use Hugging Face API", value=True)
                
                with gr.Group(visible=True) as api_settings:
                    api_key = gr.Textbox(
                        label="Hugging Face API Key", 
                        type="password",
                        value=os.environ.get("HF_API_KEY", "")
                    )
                    
                    model_choice = gr.Dropdown(
                        choices=["Mistral-7B", "Falcon-7B", "Llama-2-7B", "CodeLlama-7B"],
                        label="Model",
                        value="Mistral-7B",
                        info="Select which LLM to use for code analysis"
                    )
                
                submit_btn = gr.Button("🔥 Roast My Code!", variant="primary")
        
        # Function to toggle API settings visibility
        def toggle_api_settings(use_api):
            return gr.Group.update(visible=use_api)
        
        use_api.change(fn=toggle_api_settings, inputs=use_api, outputs=api_settings)
        
        # Output Sections
        with gr.Row():
            loading_indicator = gr.Textbox(label="Status", value="")
            
        with gr.Tab("Roast 🔥"):
            roast_output = gr.Textbox(label="Roast", lines=5)
            
        with gr.Tab("Fixed Code ✅"):
            fixed_code_output = gr.Textbox(label="Fixed Code", lines=10)
            
        with gr.Tab("Explanation 📝"):
            explanation_output = gr.Textbox(label="Explanation", lines=5)
        
        # Example code
        example_python_code = '''def calculate_fibonacci(n):
    # This function calculates the fibonacci sequence
    result = []
    a, b = 0, 1
    for i in range(0, n):
        result.append(a)
        a, b = b, a + b
    
    return result

if **name** == "__main__":
    print("Fibonacci Sequence:")
    print(calculate_fibonacci(10))
'''

        # Event handlers
        def load_example():
            return example_python_code
            
        def clear_inputs():
            return ""
        
        def show_loading():
            return "⏳ AI is analyzing your code and preparing a roast. This may take a few moments..."
        
        def hide_loading():
            return "✅ Roast complete! Check the tabs below for results."
        
        # Set up buttons for examples and clearing
        with gr.Row():
            example_btn = gr.Button("Load Example Code")
            clear_btn = gr.Button("Clear")
            
        example_btn.click(fn=load_example, outputs=code_input)
        clear_btn.click(fn=clear_inputs, outputs=code_input)
        
        # Main submit button with loading state
        submit_btn.click(
            fn=show_loading,
            outputs=loading_indicator
        ).then(
            fn=roast_code,
            inputs=[code_input, roast_level, use_api, api_key, model_choice],
            outputs=[roast_output, fixed_code_output, explanation_output]
        ).then(
            fn=hide_loading,
            outputs=loading_indicator
        )
        
        # About Section
        gr.Markdown("""
        ### 🤖 How It Works
        
        "AI Roast My Code" can work in two modes:
        
        1. **API Mode**: Uses the Hugging Face API with various models to provide advanced code analysis
           - Multiple model options including Mistral, Falcon, Llama-2, and CodeLlama
           - Requires a Hugging Face API key
        
        2. **Local Mode**: Uses built-in pattern matching for basic code analysis (no API needed)
           - Works completely offline
           - No API key required
           - Detects common issues in Python and JavaScript
        
        The app will:
        1. **Analyze** your code for problems, anti-patterns, and style issues
        2. **Roast** it with humor that ranges from gentle to savage (you choose!)
        3. **Fix** the issues and provide an improved version
        4. **Explain** what was wrong and how it was improved
        
        ### 📝 Note
        
        - For educational purposes only - always review generated code before using it
        """)
    
    return app

# Main function
def main():
    try:
        logger.info("Starting AI Roast My Code...")
        
        # Create the interface
        app = create_interface()
        
        # Launch the app
        logger.info("Launching Gradio interface...")
        app.launch(share=True)
    except Exception as e:
        logger.error(f"Error launching application: {str(e)}")
        raise

if __name__ == "__main__":
    main()