Delete openai_client.py

openai_client.py

@@ -1,148 +0,0 @@
-import os
-from dotenv import load_dotenv
-from openai import OpenAI
-import anthropic
-
-# Load environment variables from .env file
-load_dotenv()
-
-client = OpenAI(
-    api_key=os.getenv("OPENAI_API_KEY"),
-    base_url=os.getenv("OPENAI_API_BASE"),  # Uncomment if using a custom base URL
-)
-
-
-# Function to get GPT-4o Mini response
-def get_code_review_response(prompt, max_tokens=1000):
-    try:
-        response = client.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are an AI assistant who helps users in code reviews by deep thinking in points max 5-6 point shortly.",
-                },
-                {"role": "user", "content": prompt},
-            ],
-            max_tokens=max_tokens,
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        return "Sorry, an error occurred while generating your idea. Please try again later."
-
-
-# Function to refactor code
-def refactor_code(code_snippet):
-    try:
-        response = client.chat.completions.create(
-            model="gpt-4o-2024-08-06",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are an expert code refactoring assistant.",
-                },
-                {
-                    "role": "user",
-                    "content": f"Refactor the following code. Do not provide any explanation or comments, just return the refactored code.\n{code_snippet}",
-                },
-            ],
-        )
-        refactored_code = response.choices[0].message.content
-        return refactored_code
-    except Exception as e:
-        return "Sorry, an error occurred while refactoring your code. Please try again later."
-
-
-# Initialize the Anthropic client
-anthropic_client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
-
-
-# Function to get feedback on code using Anthropic
-def code_feedback(code_snippet):
-    try:
-        response = anthropic_client.messages.create(
-            model="claude-3-5-sonnet-20240620",
-            max_tokens=1024,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Please provide feedback on the given code, don't refactor the code:\n{code_snippet}",
-                },
-            ],
-        )
-
-        # Extract feedback text from the response
-        feedback = response.text if hasattr(response, "text") else str(response)
-
-        # Check if feedback is a Message object and extract text if necessary
-        if hasattr(response, "content") and isinstance(response.content, list):
-            feedback = "\n\n".join(
-                [
-                    text_block.text
-                    for text_block in response.content
-                    if hasattr(text_block, "text")
-                ]
-            )
-
-        return feedback
-
-    except Exception as e:
-        return "Sorry, an error occurred while getting feedback on your code. Please try again later."
-
-
-# Function to suggest best coding practices based on given code
-def suggest_best_practices(code_snippet):
-    try:
-        response = anthropic_client.messages.create(
-            model="claude-3-5-sonnet-20240620",
-            max_tokens=1024,
-            messages=[
-                {
-                    "role": "user",
-                    "content": (
-                        f"Based on the following code, suggest best practices max 5-6 point shortly"
-                        f"for coding patterns that align with industry standards: \n{code_snippet}"
-                    ),
-                },
-            ],
-        )
-
-        # Extract suggestions from the response
-        best_practices = response.text if hasattr(response, "text") else str(response)
-
-        # Check if the feedback is a Message object and extract text if necessary
-        if hasattr(response, "content") and isinstance(response.content, list):
-            best_practices = "\n\n".join(
-                [
-                    text_block.text
-                    for text_block in response.content
-                    if hasattr(text_block, "text")
-                ]
-            )
-
-        return best_practices
-
-    except Exception as e:
-        return "Sorry, an error occurred while suggesting best practices. Please try again later."
-
-
-# Function to remove code errors
-def remove_code_errors(code_snippet):
-    try:
-        response = client.chat.completions.create(
-            model="gpt-4o-mini",
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are an expert in debugging code. Provide concise suggestions to remove errors from the following code snippet.",
-                },
-                {
-                    "role": "user",
-                    "content": f"Identify and suggest fixes for errors in the following code:\n{code_snippet}",
-                },
-            ],
-        )
-        error_removal_suggestions = response.choices[0].message.content
-        return error_removal_suggestions
-    except Exception as e:
-        return "Sorry, an error occurred while removing code errors. Please try again later."