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import openai
import os
def analyze_code(code: str) -> str:
"""
Uses OpenAI's GPT-4.1 mini model to analyze the given code.
Returns the analysis as a string.
"""
system_prompt = "You are a helpful assistant. Analyze the code given to you. Provide insights, strengths, weaknesses, and suggestions for improvement."
response = openai.ChatCompletion.create(
model="gpt-4.1-mini", # GPT-4.1 mini
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": code}
],
max_tokens=512,
temperature=0.7
)
return response.choices[0].message["content"]
def combine_repo_files_for_llm(repo_dir="repo_files", output_file="combined_repo.txt"):
"""
Combines all .py and .md files in the given directory (recursively) into a single text file.
Returns the path to the combined file.
"""
combined_content = []
for root, _, files in os.walk(repo_dir):
for file in files:
if file.endswith(".py") or file.endswith(".md"):
file_path = os.path.join(root, file)
try:
with open(file_path, "r", encoding="utf-8") as f:
combined_content.append(f"\n# ===== File: {file} =====\n")
combined_content.append(f.read())
except Exception as e:
combined_content.append(f"\n# Could not read {file_path}: {e}\n")
with open(output_file, "w", encoding="utf-8") as out_f:
out_f.write("\n".join(combined_content))
return output_file
def analyze_combined_file(output_file="combined_repo.txt"):
"""
Reads the combined file and passes its contents to analyze_code, returning the LLM's output.
"""
try:
with open(output_file, "r", encoding="utf-8") as f:
code = f.read()
return analyze_code(code)
except Exception as e:
return f"Error analyzing combined file: {e}"
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