Update complete app implementation for Spaces

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from huggingface_hub import login
 import os
 import logging
@@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)
 
 # Environment variables
 HF_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
-MODEL_NAME = os.getenv("MODEL_NAME", "google/gemma-2-2b-it")  # Fixed model name
+MODEL_NAME = os.getenv("MODEL_NAME", "google/gemma-2-2b-it")
 
 # Login to Hugging Face with git credential
 if HF_TOKEN:
@@ -35,3 +35,388 @@ os.makedirs(DATA_DIR, exist_ok=True)
 
 # History file
 HISTORY_FILE = os.path.join(DATA_DIR, "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}")
+            # Initialize empty history if file doesn't exist or is corrupted
+            self.review_history = []
+            self.metrics = {
+                'total_reviews': 0,
+                'avg_response_time': 0.0,
+                'reviews_today': 0
+            }
+    
+    def save_history(self):
+        """Save review history to file."""
+        try:
+            data = {
+                'history': [r.to_dict() for r in self.review_history],
+                'metrics': self.metrics
+            }
+            # Ensure the directory exists
+            os.makedirs(os.path.dirname(HISTORY_FILE), exist_ok=True)
+            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:
+            logger.info("Loading tokenizer...")
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                MODEL_NAME,
+                token=HF_TOKEN,
+                trust_remote_code=True
+            )
+            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,
+                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]
+    )
+
+if __name__ == "__main__":
+    iface.launch()