Updated pylint

.pylintrc

@@ -431,7 +431,10 @@ disable=raw-checker-failed,
         use-symbolic-message-instead,
         use-implicit-booleaness-not-comparison-to-string,
         use-implicit-booleaness-not-comparison-to-zero,
-        unspecified-encoding
+        unspecified-encoding,
+        too-many-arguments,
+        too-many-positional-arguments,
+        too-many-instance-attributes
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

esbmc_ai/__main__.py

@@ -417,10 +417,15 @@ def main() -> None:
     if len(str(Config.get_user_chat_initial().content)) > 0:
         printv("Using initial prompt from file...\n")
         anim.start("Model is parsing ESBMC output... Please Wait")
-        response = chat.send_message(
-            message=str(Config.get_user_chat_initial().content),
-        )
-        anim.stop()
+        try:
+            response = chat.send_message(
+                message=str(Config.get_user_chat_initial().content),
+            )
+        except Exception as e:
+            print("There was an error while generating a response: {e}")
+            sys.exit(1)
+        finally:
+            anim.stop()
 
         if response.finish_reason == FinishReason.length:
             raise RuntimeError(f"The token length is too large: {chat.ai_model.tokens}")

esbmc_ai/ai_models.py

@@ -243,6 +243,7 @@ def is_valid_ai_model(
     """Accepts both the AIModel object and the name as parameter. It checks the
     openai servers to see if a model is defined on their servers, if not, then
     it checks the internally defined AI models list."""
+    from openai import Client
 
     # Get the name of the model
     name: str = ai_model.name if isinstance(ai_model, AIModel) else ai_model
@@ -251,8 +252,6 @@ def is_valid_ai_model(
     # NOTE: This is not tested as no way to mock API currently.
     if api_keys and api_keys.openai:
         try:
-            from openai import Client
-
             for model in Client(api_key=api_keys.openai).models.list().data:
                 if model.id == name:
                     return True

esbmc_ai/chats/base_chat_interface.py

@@ -1,5 +1,8 @@
 # Author: Yiannis Charalambous
 
+"""Contains code for the base class for interacting with the LLMs in a 
+conversation-based way."""
+
 from abc import abstractmethod
 from typing import Optional
 
@@ -14,7 +17,7 @@
 from esbmc_ai.ai_models import AIModel
 
 
-class BaseChatInterface(object):
+class BaseChatInterface:
     """Base class for interacting with an LLM. It allows for interactions with
     text generation LLMs and also chat LLMs."""
 
@@ -32,12 +35,14 @@ def __init__(
 
     @abstractmethod
     def compress_message_stack(self) -> None:
+        """Compress the message stack, is abstract and needs to be implemented."""
         raise NotImplementedError()
 
     def push_to_message_stack(
         self,
         message: BaseMessage,
     ) -> None:
+        """Pushes a message to the message stack without querying the LLM."""
         self.messages.append(message)
 
     def apply_template_value(self, **kwargs: str) -> None:
@@ -81,31 +86,28 @@ def send_message(self, message: Optional[str] = None) -> ChatResponse:
         all_messages = self._system_messages.copy()
         all_messages.extend(self.messages.copy())
 
-        response: ChatResponse
-        try:
-            response_message: BaseMessage = self.llm.invoke(input=all_messages)
+        response_message: BaseMessage = self.llm.invoke(input=all_messages)
+
+        self.push_to_message_stack(message=response_message)
 
-            self.push_to_message_stack(message=response_message)
+        # Check if token limit has been exceeded.
+        all_messages.append(response_message)
+        new_tokens: int = self.llm.get_num_tokens_from_messages(
+            messages=all_messages,
+        )
 
-            # Check if token limit has been exceeded.
-            all_messages.append(response_message)
-            new_tokens: int = self.llm.get_num_tokens_from_messages(
-                messages=all_messages,
+        response: ChatResponse
+        if new_tokens > self.ai_model.tokens:
+            response = ChatResponse(
+                finish_reason=FinishReason.length,
+                message=response_message,
+                total_tokens=self.ai_model.tokens,
+            )
+        else:
+            response = ChatResponse(
+                finish_reason=FinishReason.stop,
+                message=response_message,
+                total_tokens=new_tokens,
             )
-            if new_tokens > self.ai_model.tokens:
-                response = ChatResponse(
-                    finish_reason=FinishReason.length,
-                    message=response_message,
-                    total_tokens=self.ai_model.tokens,
-                )
-            else:
-                response = ChatResponse(
-                    finish_reason=FinishReason.stop,
-                    message=response_message,
-                    total_tokens=new_tokens,
-                )
-        except Exception as e:
-            print(f"There was an unkown error when generating a response: {e}")
-            exit(1)
 
         return response

esbmc_ai/chats/latest_state_solution_generator.py

@@ -1,5 +1,8 @@
 # Author: Yiannis Charalambous
 
+"""Contains code that extends the default solution generator to only use the
+latest state of the code only (removes history)"""
+
 from typing import Optional
 from typing_extensions import override
 from langchain_core.messages import BaseMessage

esbmc_ai/chats/solution_generator.py

@@ -1,5 +1,7 @@
 # Author: Yiannis Charalambous 2023
 
+"""Contains code for automatically repairing code using ESBMC."""
+
 from typing import Optional
 from langchain_core.language_models import BaseChatModel
 from typing_extensions import override
@@ -10,21 +12,25 @@
 from esbmc_ai.solution import SourceFile
 
 from esbmc_ai.ai_models import AIModel
-from .base_chat_interface import BaseChatInterface
 from esbmc_ai.esbmc_util import ESBMCUtil
+from .base_chat_interface import BaseChatInterface
 
 
 class ESBMCTimedOutException(Exception):
-    pass
+    """Error that means that ESBMC timed out and so the error could not be
+    determined."""
 
 
 class SourceCodeParseError(Exception):
-    pass
+    """Error that means that SolutionGenerator could not parse the source code
+    to return the right format."""
 
 
 def get_source_code_formatted(
     source_code_format: str, source_code: str, esbmc_output: str
 ) -> str:
+    """Gets the formatted output source code, based on the source_code_format
+    passed."""
     match source_code_format:
         case "single":
             # Get source code error line from esbmc output
@@ -49,11 +55,14 @@ def get_source_code_formatted(
 
 
 def get_esbmc_output_formatted(esbmc_output_type: str, esbmc_output: str) -> str:
+    """Gets the formatted output ESBMC output, based on the esbmc_output_type
+    passed."""
     # Check for parsing error
     if "ERROR: PARSING ERROR" in esbmc_output:
         # Parsing errors are usually small in nature.
         raise SourceCodeParseError()
-    elif "ERROR: Timed out" in esbmc_output:
+
+    if "ERROR: Timed out" in esbmc_output:
         raise ESBMCTimedOutException()
 
     match esbmc_output_type:
@@ -74,6 +83,9 @@ def get_esbmc_output_formatted(esbmc_output_type: str, esbmc_output: str) -> str
 
 
 class SolutionGenerator(BaseChatInterface):
+    """Class that generates a solution using verifier output and source code
+    that contains a bug."""
+
     def __init__(
         self,
         scenarios: FixCodeScenarios,
@@ -166,6 +178,8 @@ def generate_solution(
         self,
         override_scenario: Optional[str] = None,
     ) -> tuple[str, FinishReason]:
+        """Queries the AI model to get a solution. Accepts an override scenario
+        parameter, in which case the scenario won't be resolved automatically."""
 
         assert (
             self.source_code_raw is not None
@@ -209,9 +223,10 @@ def generate_solution(
                     # Check if it parses
                     line = ESBMCUtil.get_clang_err_line_index(self.esbmc_output)
 
-                assert (
-                    line
-                ), "fix code command: error line could not be found to apply brutal patch replacement"
+                assert line, (
+                    "fix code command: error line could not be found to apply "
+                    "brutal patch replacement"
+                )
                 solution = SourceFile.apply_line_patch(
                     self.source_code_raw, solution, line, line
                 )

esbmc_ai/chats/user_chat.py

@@ -1,5 +1,7 @@
 # Author: Yiannis Charalambous 2023
 
+"""Contains class that handles the UserChat of ESBMC-AI"""
+
 from typing_extensions import override
 
 from langchain.memory import ConversationSummaryMemory
@@ -14,6 +16,9 @@
 
 
 class UserChat(BaseChatInterface):
+    """Simple interface that talks to the LLM and stores the result. The class
+    also stores the fixed results from fix code command."""
+
     solution: str = ""
 
     def __init__(
@@ -50,8 +55,9 @@ def set_solution(self, source_code: str) -> None:
 
     @override
     def compress_message_stack(self) -> None:
-        """Uses ConversationSummaryMemory from Langchain to summarize the conversation of all the non-protected
-        messages into one summary message which is added into the conversation as a SystemMessage.
+        """Uses ConversationSummaryMemory from Langchain to summarize the
+        conversation of all the non-protected messages into one summary message
+        which is added into the conversation as a SystemMessage.
         """
 
         memory: ConversationSummaryMemory = ConversationSummaryMemory.from_messages(

esbmc_ai/commands/__init__.py

@@ -3,6 +3,8 @@
 from .fix_code_command import FixCodeCommand
 from .help_command import HelpCommand
 
+"""This module contains built-in commands that can be executed by ESBMC-AI."""
+
 __all__ = [
     "ChatCommand",
     "ExitCommand",

esbmc_ai/commands/fix_code_command.py

@@ -1,7 +1,7 @@
 # Author: Yiannis Charalambous
 
 import sys
-from typing import Any, Optional, Tuple
+from typing import Any, Optional
 from typing_extensions import override
 
 from esbmc_ai.ai_models import AIModel
@@ -42,6 +42,8 @@ def __str__(self) -> str:
 
 
 class FixCodeCommand(ChatCommand):
+    """Command for automatically fixing code using a verifier."""
+
     on_solution_signal: Signal = Signal()
 
     def __init__(self) -> None:
@@ -71,7 +73,7 @@ def print_raw_conversation() -> None:
         )
 
         message_history: str = (
-            kwargs["message_history"] if "message_history" else "normal"
+            kwargs["message_history"] if "message_history" in kwargs else "normal"
         )
 
         api_keys: APIKeyCollection = kwargs["api_keys"]

esbmc_ai/config.py

@@ -90,6 +90,8 @@ def _validate_prompt_template(conv: Dict[str, List[Dict]]) -> bool:
 
 
 class Config:
+    """Config loader for ESBMC-AI"""
+
     api_keys: APIKeyCollection
     raw_conversation: bool = False
     cfg_path: Path
@@ -114,7 +116,7 @@ class Config:
             name="temp_file_dir",
             default_value=None,
             validate=lambda v: isinstance(v, str) and Path(v).is_file(),
-            on_load=lambda v: Path(v),
+            on_load=Path,
             default_value_none=True,
         ),
         ConfigField(
@@ -181,13 +183,13 @@ class Config:
         ConfigField(
             name="user_chat.temperature",
             default_value=1.0,
-            validate=lambda v: isinstance(v, float) and v >= 0 and v <= 2.0,
+            validate=lambda v: isinstance(v, float) and 0 <= v <= 2.0,
             error_message="Temperature needs to be a value between 0 and 2.0",
         ),
         ConfigField(
             name="fix_code.temperature",
             default_value=1.0,
-            validate=lambda v: isinstance(v, float) and v >= 0 and v <= 2.0,
+            validate=lambda v: isinstance(v, float) and 0 <= v <= 2,
             error_message="Temperature needs to be a value between 0 and 2.0",
         ),
         ConfigField(
@@ -210,14 +212,14 @@ class Config:
         ConfigField(
             name="prompt_templates.user_chat.system",
             default_value=None,
-            validate=lambda v: _validate_prompt_template_conversation(v),
-            on_load=lambda v: list_to_base_messages(v),
+            validate=_validate_prompt_template_conversation,
+            on_load=list_to_base_messages,
         ),
         ConfigField(
             name="prompt_templates.user_chat.set_solution",
             default_value=None,
-            validate=lambda v: _validate_prompt_template_conversation(v),
-            on_load=lambda v: list_to_base_messages(v),
+            validate=_validate_prompt_template_conversation,
+            on_load=list_to_base_messages,
         ),
         # Here we have a list of prompt templates that are for each scenario.
         # The base scenario prompt template is required.