Fix model_manager to use actual model inference instead of mock response

src/model_manager.py

@@ -65,36 +65,42 @@ class ModelManager:
     def generate_text(self, prompt: str, max_new_tokens: int = 1024) -> str:
         """Generate text from prompt."""
         try:
-            # For now, return a mock response in the correct format
-            return """- Issues:
-- No critical issues found in the code
-- The code is simple and straightforward
-
-- Improvements:
-- Consider adding type hints for better code readability
-- Add input validation for the numbers parameter
-- Consider using sum() built-in function for better performance
+            # Encode the prompt
+            inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
-- Best Practices:
-- Add docstring to explain function purpose and parameters
-- Follow PEP 8 naming conventions
-- Consider adding return type annotation
+            # Generate response
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    **inputs,
+                    max_new_tokens=max_new_tokens,
+                    do_sample=True,
+                    temperature=Config.TEMPERATURE,
+                    top_p=Config.TOP_P,
+                    pad_token_id=self.tokenizer.pad_token_id,
+                    eos_token_id=self.tokenizer.eos_token_id,
+                )
 
-- Security:
-- No immediate security concerns for this simple function
-- Input validation would help prevent potential issues"""
+            # Decode and return the generated text
+            generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            # Extract only the generated part (remove the prompt)
+            response = generated_text[len(prompt):].strip()
+            
+            return response
             
         except Exception as e:
             logger.error(f"Error generating text: {str(e)}")
-            # Return a default response in case of error
             return """- Issues:
-- No critical issues found
+- Error generating code review
+- Model inference failed
 
 - Improvements:
-- Consider adding error handling
+- Please try again
+- Check model configuration
 
 - Best Practices:
-- Add documentation
+- Ensure proper model setup
+- Verify token permissions
 
 - Security:
 - No immediate concerns"""