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| import openai | |
| import os | |
| import json | |
| 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. | |
| """ | |
| from openai import OpenAI | |
| client = OpenAI() | |
| system_prompt = ( | |
| "You are a helpful assistant. Analyze the code given to you. " | |
| "Return your response strictly in JSON format with the following keys: " | |
| "'strength', 'weaknesses', 'speciality', 'relevance rating'. " | |
| "Do not include any other text outside the JSON." | |
| "the reply should just be the following format:" | |
| "{" | |
| " 'strength': '...', " | |
| " 'weaknesses': '...', " | |
| " 'speciality': '...', " | |
| " 'relevance rating': '...'" | |
| "}" | |
| ) | |
| response = client.chat.completions.create( | |
| model="gpt-4o-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 parse_llm_json_response(response: str): | |
| try: | |
| return json.loads(response) | |
| except Exception as e: | |
| return {"error": f"Failed to parse JSON: {e}", "raw": response} | |
| 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 = [] | |
| seen_files = set() | |
| # Priority files | |
| priority_files = ["app.py", "README.md"] | |
| for pf in priority_files: | |
| pf_path = os.path.join(repo_dir, pf) | |
| if os.path.isfile(pf_path): | |
| try: | |
| with open(pf_path, "r", encoding="utf-8") as f: | |
| combined_content.append(f"\n# ===== File: {pf} =====\n") | |
| combined_content.append(f.read()) | |
| seen_files.add(os.path.abspath(pf_path)) | |
| except Exception as e: | |
| combined_content.append(f"\n# Could not read {pf_path}: {e}\n") | |
| # All other .py and .md files | |
| 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) | |
| abs_path = os.path.abspath(file_path) | |
| if abs_path in seen_files: | |
| continue | |
| try: | |
| with open(file_path, "r", encoding="utf-8") as f: | |
| combined_content.append(f"\n# ===== File: {file} =====\n") | |
| combined_content.append(f.read()) | |
| seen_files.add(abs_path) | |
| 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: | |
| lines = f.readlines() | |
| code = "".join(lines[:500]) | |
| return analyze_code(code) | |
| except Exception as e: | |
| return f"Error analyzing combined file: {e}" | |