app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login
import os
import logging
from datetime import datetime
import json
from typing import List, Dict
import warnings
import spaces

# Filter out warnings
warnings.filterwarnings('ignore')

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Environment variables
HF_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
MODEL_NAME = os.getenv("MODEL_NAME", "google/gemma-2b-it")

# Cache directory for model
CACHE_DIR = "/home/user/.cache/huggingface"
os.makedirs(CACHE_DIR, exist_ok=True)

# History file
HISTORY_FILE = "/home/user/review_history.json"

class Review:
    def __init__(self, code: str, language: str, suggestions: str):
        self.code = code
        self.language = language
        self.suggestions = suggestions
        self.timestamp = datetime.now().isoformat()
        self.response_time = 0.0
    
    def to_dict(self):
        return {
            'timestamp': self.timestamp,
            'language': self.language,
            'code': self.code,
            'suggestions': self.suggestions,
            'response_time': self.response_time
        }
    
    @classmethod
    def from_dict(cls, data):
        review = cls(data['code'], data['language'], data['suggestions'])
        review.timestamp = data['timestamp']
        review.response_time = data['response_time']
        return review

class CodeReviewer:
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.device = None
        self.review_history: List[Review] = []
        self.metrics = {
            'total_reviews': 0,
            'avg_response_time': 0.0,
            'reviews_today': 0
        }
        self._initialized = False
        self.load_history()
        
    def load_history(self):
        """Load review history from file."""
        try:
            if os.path.exists(HISTORY_FILE):
                with open(HISTORY_FILE, 'r') as f:
                    data = json.load(f)
                    self.review_history = [Review.from_dict(r) for r in data['history']]
                    self.metrics = data['metrics']
                logger.info(f"Loaded {len(self.review_history)} reviews from history")
        except Exception as e:
            logger.error(f"Error loading history: {e}")
    
    def save_history(self):
        """Save review history to file."""
        try:
            data = {
                'history': [r.to_dict() for r in self.review_history],
                'metrics': self.metrics
            }
            with open(HISTORY_FILE, 'w') as f:
                json.dump(data, f)
            logger.info("Saved review history")
        except Exception as e:
            logger.error(f"Error saving history: {e}")
        
    @spaces.GPU
    def ensure_initialized(self):
        """Ensure model is initialized."""
        if not self._initialized:
            self.initialize_model()
            self._initialized = True
        
    def initialize_model(self):
        """Initialize the model and tokenizer."""
        try:
            if HF_TOKEN:
                login(token=HF_TOKEN, add_to_git_credential=False)
            
            logger.info("Loading tokenizer...")
            self.tokenizer = AutoTokenizer.from_pretrained(
                MODEL_NAME,
                token=HF_TOKEN,
                trust_remote_code=True,
                cache_dir=CACHE_DIR
            )
            special_tokens = {
                'pad_token': '[PAD]',
                'eos_token': '</s>',
                'bos_token': '<s>'
            }
            num_added = self.tokenizer.add_special_tokens(special_tokens)
            logger.info(f"Added {num_added} special tokens")
            logger.info("Tokenizer loaded successfully")
            
            logger.info("Loading model...")
            self.model = AutoModelForCausalLM.from_pretrained(
                MODEL_NAME,
                device_map="auto",
                torch_dtype=torch.float16,
                trust_remote_code=True,
                low_cpu_mem_usage=True,
                cache_dir=CACHE_DIR,
                token=HF_TOKEN
            )
            if num_added > 0:
                logger.info("Resizing model embeddings for special tokens")
                self.model.resize_token_embeddings(len(self.tokenizer))
            
            self.device = next(self.model.parameters()).device
            logger.info(f"Model loaded successfully on {self.device}")
            self._initialized = True
            return True
        except Exception as e:
            logger.error(f"Error initializing model: {e}")
            self._initialized = False
            return False

    def create_review_prompt(self, code: str, language: str) -> str:
        """Create a structured prompt for code review."""
        return f"""Review this {language} code. List specific points in these sections:
Issues:
Improvements:
Best Practices:
Security:

Code:
```{language}
{code}
```"""

    @spaces.GPU
    def review_code(self, code: str, language: str) -> str:
        """Perform code review using the model."""
        try:
            if not self._initialized and not self.initialize_model():
                return "Error: Model initialization failed. Please try again later."
            
            start_time = datetime.now()
            prompt = self.create_review_prompt(code, language)
            
            try:
                inputs = self.tokenizer(
                    prompt,
                    return_tensors="pt",
                    truncation=True,
                    max_length=512,
                    padding=True
                )
                if inputs is None:
                    raise ValueError("Failed to tokenize input")
                inputs = inputs.to(self.device)
            except Exception as token_error:
                logger.error(f"Tokenization error: {token_error}")
                return "Error: Failed to process input code. Please try again."
            
            try:
                with torch.no_grad():
                    outputs = self.model.generate(
                        **inputs,
                        max_new_tokens=512,
                        do_sample=True,
                        temperature=0.7,
                        top_p=0.95,
                        num_beams=1,
                        early_stopping=True,
                        pad_token_id=self.tokenizer.pad_token_id,
                        eos_token_id=self.tokenizer.eos_token_id
                    )
            except Exception as gen_error:
                logger.error(f"Generation error: {gen_error}")
                return "Error: Failed to generate review. Please try again."
            
            try:
                response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
                suggestions = response[len(prompt):].strip()
            except Exception as decode_error:
                logger.error(f"Decoding error: {decode_error}")
                return "Error: Failed to decode model output. Please try again."
            
            # Create and save review
            end_time = datetime.now()
            review = Review(code, language, suggestions)
            review.response_time = (end_time - start_time).total_seconds()
            
            # Update metrics first
            self.metrics['total_reviews'] += 1
            total_time = self.metrics['avg_response_time'] * (self.metrics['total_reviews'] - 1)
            total_time += review.response_time
            self.metrics['avg_response_time'] = total_time / self.metrics['total_reviews']
            
            today = datetime.now().date()
            
            # Add review to history
            self.review_history.append(review)
            
            # Update today's reviews count
            self.metrics['reviews_today'] = sum(
                1 for r in self.review_history 
                if datetime.fromisoformat(r.timestamp).date() == today
            )
            
            # Save to file
            self.save_history()
            
            if self.device and self.device.type == "cuda":
                del inputs, outputs
                torch.cuda.empty_cache()
            
            return suggestions
            
        except Exception as e:
            logger.error(f"Error during code review: {e}")
            return f"Error performing code review: {str(e)}"

    def update_metrics(self, review: Review):
        """Update metrics with new review."""
        self.metrics['total_reviews'] += 1
        
        total_time = self.metrics['avg_response_time'] * (self.metrics['total_reviews'] - 1)
        total_time += review.response_time
        self.metrics['avg_response_time'] = total_time / self.metrics['total_reviews']
        
        today = datetime.now().date()
        self.metrics['reviews_today'] = sum(
            1 for r in self.review_history 
            if datetime.fromisoformat(r.timestamp).date() == today
        )

    def get_history(self) -> List[Dict]:
        """Get formatted review history."""
        return [
            {
                'timestamp': r.timestamp,
                'language': r.language,
                'code': r.code,
                'suggestions': r.suggestions,
                'response_time': f"{r.response_time:.2f}s"
            }
            for r in reversed(self.review_history[-10:])
        ]

    def get_metrics(self) -> Dict:
        """Get current metrics."""
        return {
            'Total Reviews': self.metrics['total_reviews'],
            'Average Response Time': f"{self.metrics['avg_response_time']:.2f}s",
            'Reviews Today': self.metrics['reviews_today'],
            'Device': str(self.device) if self.device else "Not initialized"
        }

# Initialize reviewer
reviewer = CodeReviewer()

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as iface:
    gr.Markdown("# Code Review Assistant")
    gr.Markdown("An automated code review system powered by Gemma-2b")
    
    with gr.Tabs():
        with gr.Tab("Review Code"):
            with gr.Row():
                with gr.Column():
                    code_input = gr.Textbox(
                        lines=10,
                        placeholder="Enter your code here...",
                        label="Code"
                    )
                    language_input = gr.Dropdown(
                        choices=["python", "javascript", "java", "cpp", "typescript", "go", "rust"],
                        value="python",
                        label="Language"
                    )
                    submit_btn = gr.Button("Submit for Review", variant="primary")
                with gr.Column():
                    output = gr.Textbox(
                        label="Review Results",
                        lines=10
                    )
        
        with gr.Tab("History"):
            with gr.Row():
                refresh_history = gr.Button("Refresh History", variant="secondary")
            history_output = gr.Textbox(
                label="Review History",
                lines=20,
                value="Click 'Refresh History' to view review history"
            )
        
        with gr.Tab("Metrics"):
            with gr.Row():
                refresh_metrics = gr.Button("Refresh Metrics", variant="secondary")
            metrics_output = gr.JSON(
                label="Performance Metrics"
            )
    
    @spaces.GPU
    def review_code_interface(code: str, language: str) -> str:
        if not code.strip():
            return "Please enter some code to review."
        try:
            reviewer.ensure_initialized()
            result = reviewer.review_code(code, language)
            return result
        except Exception as e:
            logger.error(f"Interface error: {e}")
            return f"Error: {str(e)}"
    
    def get_history_interface() -> str:
        try:
            history = reviewer.get_history()
            if not history:
                return "No reviews yet."
            result = ""
            for review in history:
                result += f"Time: {review['timestamp']}\n"
                result += f"Language: {review['language']}\n"
                result += f"Response Time: {review['response_time']}\n"
                result += "Code:\n```\n" + review['code'] + "\n```\n"
                result += "Suggestions:\n" + review['suggestions'] + "\n"
                result += "-" * 80 + "\n\n"
            return result
        except Exception as e:
            logger.error(f"History error: {e}")
            return "Error retrieving history"
    
    def get_metrics_interface() -> Dict:
        try:
            metrics = reviewer.get_metrics()
            if not metrics:
                return {
                    'Total Reviews': 0,
                    'Average Response Time': '0.00s',
                    'Reviews Today': 0,
                    'Device': str(reviewer.device) if reviewer.device else "Not initialized"
                }
            return metrics
        except Exception as e:
            logger.error(f"Metrics error: {e}")
            return {"error": str(e)}
    
    def update_all_outputs(code: str, language: str) -> tuple:
        """Update all outputs after code review."""
        result = review_code_interface(code, language)
        history = get_history_interface()
        metrics = get_metrics_interface()
        return result, history, metrics
    
    # Connect the interface
    submit_btn.click(
        update_all_outputs,
        inputs=[code_input, language_input],
        outputs=[output, history_output, metrics_output]
    )
    
    refresh_history.click(
        get_history_interface,
        outputs=history_output
    )
    
    refresh_metrics.click(
        get_metrics_interface,
        outputs=metrics_output
    )
    
    # Add example inputs
    gr.Examples(
        examples=[
            ["""def add_numbers(a, b):
    return a + b""", "python"],
            ["""function calculateSum(numbers) {
    let sum = 0;
    for(let i = 0; i < numbers.length; i++) {
        sum += numbers[i];
    }
    return sum;
}""", "javascript"]
        ],
        inputs=[code_input, language_input]
    )

# Launch the app
if __name__ == "__main__":
    iface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        quiet=False
    )