Anthropic works

src/codergpt/explainer/explainer.py

@@ -27,11 +27,15 @@ def explain(
         :param classname: The name of the class to explain. Default is None.
         """
         if function:
-            response = self.chain.invoke({"input": f"Explain the following {language} code: \n\n```\n{code}\n```"})
+            response = self.chain.invoke(
+                {"input": f"Explain the function {function} in the following {language} code: \n\n```\n{code}\n```"}
+            )
             # Pretty print the response
             print(f"Explanation for '{function}':\n{response.content}")
         elif classname:
-            response = self.chain.invoke({"input": f"Explain the following {language} code: \n\n```\n{code}\n```"})
+            response = self.chain.invoke(
+                {"input": f"Explain the class {classname} in the following {language} code: \n\n```\n{code}\n```"}
+            )
             # Pretty print the response
             print(f"Explanation for '{classname}':\n{response.content}")
         else:

src/codergpt/main.py

@@ -5,13 +5,14 @@
 from typing import Optional, Union
 
 import yaml
+from langchain_anthropic import ChatAnthropicMessages
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import ChatOpenAI
 from tabulate import tabulate
 
 from codergpt.commenter.commenter import CodeCommenter
-from codergpt.constants import EXTENSION_MAP_FILE, GEMINI, GPT_4_TURBO, INSPECTION_HEADERS
+from codergpt.constants import CLAUDE, EXTENSION_MAP_FILE, GEMINI, GPT_4_TURBO, INSPECTION_HEADERS
 from codergpt.documenter.documenter import CodeDocumenter
 from codergpt.explainer.explainer import CodeExplainer
 from codergpt.optimizer.optimizer import CodeOptimizer
@@ -23,11 +24,16 @@ class CoderGPT:
 
     def __init__(self, model: str = GPT_4_TURBO):
         """Initialize the CoderGPT class."""
+        temp = 0.3
         if model is None or model.startswith("gpt-"):
-            self.llm = ChatOpenAI(openai_api_key=os.environ.get("OPENAI_API_KEY"), temperature=0.3, model=model)
-        # elif model == CLAUDE:
-        #     self.llm = ChatAnthropic()
-        #     print("Coming Soon!")
+            self.llm = ChatOpenAI(openai_api_key=os.environ.get("OPENAI_API_KEY"), temperature=temp, model=model)
+        elif model == CLAUDE:
+            self.llm = ChatAnthropicMessages(
+                model_name=model,
+                anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY"),
+                temperature=temp,
+                max_tokens=2048,
+            )
         elif model == GEMINI:
             self.llm = ChatGoogleGenerativeAI(model=model, convert_system_message_to_human=True)
         else:
@@ -115,7 +121,7 @@ def explainer(self, path: Union[str, Path], function: str = None, classname=None
         """
         code_explainer = CodeExplainer(self.chain)
         code, language = self.get_code(filename=path, function_name=function, class_name=classname)
-        code_explainer.explain(code, language)
+        code_explainer.explain(code=code, function=function, classname=classname, language=language)
 
     def commenter(self, path: Union[str, Path], overwrite: bool = False):
         """

tests/test_explainer.py

@@ -33,7 +33,10 @@ def test_explain_function(self):
         self.code_explainer.explain(code=sample_code, function=sample_function_name, language="python")
 
         # Verify that invoke was called once with the correct parameters
-        expected_invoke_input = {"input": f"Explain the following python code: \n\n```\n{sample_code}\n```"}
+        expected_invoke_input = {
+            "input": f"Explain the function {sample_function_name} "
+            f"in the following python code: \n\n```\n{sample_code}\n```"
+        }
         self.mock_chain.invoke.assert_called_once_with(expected_invoke_input)
 
         # Check if the expected explanation message is in the captured output
@@ -51,7 +54,9 @@ def test_explain_class(self):
         self.code_explainer.explain(code=sample_code, classname=sample_class_name, language="python")
 
         # Verify that invoke was called once with the correct parameters
-        expected_invoke_input = {"input": f"Explain the following python code: \n\n```\n{sample_code}\n```"}
+        expected_invoke_input = {
+            "input": f"Explain the class {sample_class_name} in the following python code: \n\n```\n{sample_code}\n```"
+        }
         self.mock_chain.invoke.assert_called_once_with(expected_invoke_input)
 
         # Check if the expected explanation message is in the captured output