code-compass / scripts /RepositoryHandler.py
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Git to HF
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import gradio as gr
import os
import zipfile
import tempfile
import shutil
import requests
import re
from pathlib import Path
from urllib.parse import urlparse
import subprocess
import threading
import time
import logging
# Import our custom modules
from .chunker import HierarchicalChunker
from .vectorstore import PineconeVectorStore
from .llm_service import QwenCoderLLM
from config import MODEL_PATH
from typing import List, Dict, Any
logger = logging.getLogger("code_compass")
class RepositoryHandler:
def __init__(self):
self.temp_dir = None
self.repo_path = None
self.is_loaded = False
self.repo_name = None
self.chunks = []
# Initialize chunker and vector store
self.chunker = HierarchicalChunker()
self.vector_store = None # Will be initialized when needed
self.processing_status = {"status": "idle", "progress": 0, "message": ""}
# Initialize LLM service
self.llm = QwenCoderLLM(model_path=MODEL_PATH, n_gpu_layers=-1) # Adjust n_gpu_layers based on your GPU memory
self.llm_loading_started = False
def validate_github_url(self, url):
"""Validate if URL is a proper GitHub repository URL"""
github_pattern = r'https://github\.com/[\w\-\.]+/[\w\-\.]+'
return bool(re.match(github_pattern, url))
def validate_zip_file(self, zip_file):
"""Validate if uploaded file is a proper zip file"""
if zip_file is None:
return False, "No file uploaded"
try:
# Check if file exists and has .zip extension
if not zip_file.name.lower().endswith('.zip'):
return False, "File must be a .zip file"
# Try to open and validate the zip file
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
# Test if zip file is valid
zip_ref.testzip()
# Check if it contains at least one file
file_list = zip_ref.namelist()
if not file_list:
return False, "Zip file is empty"
# Check if it looks like a code repository
code_extensions = ['.py', '.js', '.java', '.cpp', '.c', '.go', '.rs', '.php', '.rb', '.ts']
has_code_files = any(
any(fname.endswith(ext) for ext in code_extensions)
for fname in file_list
)
if not has_code_files:
return False, "Zip file doesn't appear to contain code files"
return True, f"Valid zip file with {len(file_list)} files"
except zipfile.BadZipFile:
return False, "Invalid or corrupted zip file"
except Exception as e:
return False, f"Error validating zip file: {str(e)}"
def download_github_repo(self, github_url):
"""Download GitHub repository using git clone"""
try:
# Create temporary directory
self.temp_dir = tempfile.mkdtemp(prefix="repo_")
# Extract repo name for folder
self.repo_name = github_url.split('/')[-1].replace('.git', '')
self.repo_path = os.path.join(self.temp_dir, self.repo_name)
# Clone the repository
result = subprocess.run([
'git', 'clone', github_url, self.repo_path
], capture_output=True, text=True, timeout=300)
if result.returncode != 0:
# If git clone fails, try downloading as zip
return self._download_repo_as_zip(github_url)
# Count files in repository
total_files = sum(1 for _ in Path(self.repo_path).rglob('*') if _.is_file())
self.is_loaded = True
return True, f"βœ… Repository successfully cloned! Found {total_files} files in {self.repo_name}"
except subprocess.TimeoutExpired:
return False, "❌ Download timeout - repository might be too large"
except FileNotFoundError:
# Git not installed, fallback to zip download
return self._download_repo_as_zip(github_url)
except Exception as e:
return False, f"❌ Error downloading repository: {str(e)}"
def _download_repo_as_zip(self, github_url):
"""Fallback method to download repo as zip if git is not available"""
try:
# Convert GitHub URL to zip download URL
zip_url = github_url.rstrip('/') + '/archive/refs/heads/main.zip'
# Try main branch, if fails try master
for branch in ['main', 'master']:
try:
zip_url = github_url.rstrip('/') + f'/archive/refs/heads/{branch}.zip'
response = requests.get(zip_url, timeout=60)
response.raise_for_status()
break
except:
continue
else:
return False, "❌ Could not download repository - check if it's public and accessible"
# Create temp directory and save zip
self.temp_dir = tempfile.mkdtemp(prefix="repo_")
zip_path = os.path.join(self.temp_dir, "repo.zip")
with open(zip_path, 'wb') as f:
f.write(response.content)
# Extract zip
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(self.temp_dir)
# Find the extracted folder (usually repo-name-branch)
extracted_folders = [d for d in os.listdir(self.temp_dir)
if os.path.isdir(os.path.join(self.temp_dir, d))]
if extracted_folders:
self.repo_path = os.path.join(self.temp_dir, extracted_folders[0])
total_files = sum(1 for _ in Path(self.repo_path).rglob('*') if _.is_file())
self.is_loaded = True
return True, f"βœ… Repository successfully downloaded! Found {total_files} files"
else:
return False, "❌ Error extracting downloaded repository"
except requests.RequestException as e:
return False, f"❌ Network error downloading repository: {str(e)}"
except Exception as e:
return False, f"❌ Error downloading repository: {str(e)}"
def extract_zip_file(self, zip_file):
"""Extract uploaded zip file"""
try:
# Create temporary directory
self.temp_dir = tempfile.mkdtemp(prefix="repo_")
# Extract zip file
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
zip_ref.extractall(self.temp_dir)
# Find the main folder or use temp_dir if files are in root
extracted_items = os.listdir(self.temp_dir)
# If there's only one folder, use it as repo_path
if len(extracted_items) == 1 and os.path.isdir(os.path.join(self.temp_dir, extracted_items[0])):
self.repo_path = os.path.join(self.temp_dir, extracted_items[0])
self.repo_name = os.path.basename(self.repo_path)
else:
# Files are in root of zip
self.repo_path = self.temp_dir
# Count files
total_files = sum(1 for _ in Path(self.repo_path).rglob('*') if _.is_file())
self.is_loaded = True
return True, f"βœ… Zip file successfully extracted! Found {total_files} files"
except Exception as e:
return False, f"❌ Error extracting zip file: {str(e)}"
def initialize_vector_store(self, namespace):
"""Initialize Pinecone vector store"""
try:
if self.vector_store is None:
print("πŸ”„ Initializing vector store...")
self.vector_store = PineconeVectorStore(namespace=namespace)
print("βœ… Vector store initialized!")
return True, "Vector store ready"
except Exception as e:
error_msg = f"❌ Error initializing vector store: {str(e)}"
print(error_msg)
return False, error_msg
def process_and_store_chunks(self):
"""Process repository into chunks and store in vector database"""
if not self.is_loaded or not self.repo_path:
return False, "❌ No repository loaded"
try:
self.processing_status = {"status": "chunking", "progress": 10, "message": "Creating hierarchical chunks..."}
namespace = self.repo_name + "_namespace"
# Step 1: Create chunks
logger.info(f"πŸ”„ Creating chunks for {self.repo_name}...")
self.chunks = self.chunker.chunk_repository(self.repo_path)
if not self.chunks:
return False, "❌ No chunks generated from repository"
# self.processing_status = {"status": "embedding", "progress": 40, "message": f"Generating embeddings for {len(self.chunks)} chunks..."}
# Step 2: Initialize vector store
success, message = self.initialize_vector_store(namespace=namespace)
if not success:
return False, message
# Step 3: Generate embeddings
# print("πŸ”„ Generating embeddings...")
# self.chunks = self.vector_store.generate_embeddings(self.chunks)
self.processing_status = {"status": "storing", "progress": 70, "message": "Storing chunks in vector database..."}
# Step 4: Store in Pinecone
logger.info("πŸ”„ Storing chunks in vector database...")
result = self.vector_store.upsert_chunks(self.chunks)
self.processing_status = {"status": "complete", "progress": 100, "message": "Processing complete!"}
if result['status'] == 'success':
summary = f"""βœ… Repository processing complete!
πŸ“Š **Processing Summary:**
- Repository: {self.repo_name}
- Total chunks created: {len(self.chunks)}
- Successfully stored: {result['successful_upserts']}
- Failed: {result['failed_upserts']}
πŸ“ **Chunk Distribution:**"""
# Add chunk type distribution
chunk_types = {}
for chunk in self.chunks:
chunk_type = chunk.chunk_type
chunk_types[chunk_type] = chunk_types.get(chunk_type, 0) + 1
for chunk_type, count in chunk_types.items():
summary += f"\n- {chunk_type.title()}: {count}"
summary += f"\n\nπŸ” **Ready for queries!** You can now ask questions about your code."
return True, summary
else:
return False, f"❌ Error storing chunks: {result.get('message', 'Unknown error')}"
except Exception as e:
self.processing_status = {"status": "error", "progress": 0, "message": f"Error: {str(e)}"}
return False, f"❌ Error processing repository: {str(e)}"
def query_repository(self, query_text, search_type="hybrid",use_llm=True):
"""Query the repository using vector search"""
if not self.vector_store or not self.chunks:
return "❌ Repository not processed yet. Please load and process a repository first."
if not query_text or not query_text.strip():
return "Please enter a query about the repository."
try:
logger.info(f"πŸ” Querying repository: {query_text}")
# Perform hybrid search
results = self.vector_store.hybrid_search(
query_text=query_text.strip(),
repo_names=[self.repo_name],
top_k=10
)
if not results:
return f"""πŸ€– No relevant results found for: "{query_text}"
Try rephrasing your question or asking about:
- Specific functions or classes
- Code patterns or algorithms
- File structure or organization
- Dependencies or imports"""
# Step 2: Use LLM for intelligent response if enabled and ready
if use_llm:
if not self.llm_loading_started:
self.initialize_llm()
if self.llm.is_model_ready():
# Generate intelligent response using LLM
llm_response = self.llm.generate_response(
user_query=query_text.strip(),
retrieved_chunks=results,
use_history=True
)
if llm_response["status"] == "success":
response = f"""πŸ€– **AI Analysis:**
{llm_response["response"]}
---
πŸ“Š **Query Details:**
- Found {len(results)} relevant code sections
- Response generated in {llm_response["metadata"]["generation_time"]:.2f}s
- Conversation length: {llm_response["metadata"]["conversation_length"]} messages
"""
return response
else:
# Fall back to basic response if LLM fails
return self._generate_basic_response(query_text, results) + f"\n\n⚠️ LLM Error: {llm_response.get('message', 'Unknown error')}"
else:
# LLM not ready, provide basic response with loading status
basic_response = self._generate_basic_response(query_text, results)
return basic_response + "\n\n⏳ **Note:** AI model is still loading. You'll get smarter responses once it's ready!"
else:
# Basic response without LLM
return self._generate_basic_response(query_text, results)
except Exception as e:
return f"❌ Error querying repository: {str(e)}"
# Format response
# response = f"""πŸ” **Query Results for:** "{query_text}"
# πŸ“Š **Found {len(results)} relevant code sections:**
# """
# for i, result in enumerate(results[:5], 1): # Show top 5 results
# metadata = result.get('metadata', {})
# score = result.get('score', 0)
# chunk_type = metadata.get('chunk_type', 'unknown')
# file_path = metadata.get('file_path', 'unknown')
# response += f"""**{i}. {chunk_type.title()} Match** (Similarity: {score:.2f})
# πŸ“„ File: `{file_path}`
# """
# if chunk_type == 'function':
# func_name = metadata.get('function_name', 'unknown')
# class_name = metadata.get('class_name')
# signature = metadata.get('signature', func_name)
# response += f"πŸ”§ Function: `{signature}`\n"
# if class_name:
# response += f"πŸ“¦ Class: `{class_name}`\n"
# elif chunk_type == 'class':
# class_name = metadata.get('class_name', 'unknown')
# methods = metadata.get('methods', [])
# response += f"πŸ“¦ Class: `{class_name}`\n"
# if methods:
# response += f"πŸ”§ Methods: {', '.join(methods[:5])}\n"
# elif chunk_type == 'file':
# language = metadata.get('language', 'unknown')
# total_lines = metadata.get('total_lines', 'unknown')
# response += f"πŸ“ Language: {language}, Lines: {total_lines}\n"
# response += "---\n\n"
# # Add repository overview
# if len(results) > 5:
# response += f"... and {len(results) - 5} more results available.\n\n"
# response += f"""πŸ’‘ **Suggestions:**
# - Ask more specific questions about functions or classes
# - Query about code patterns: "Show me error handling code"
# - Ask about structure: "What are the main components?"
# - Request examples: "How is authentication implemented?"
# """
# return response
# except Exception as e:
# return f"❌ Error querying repository: {str(e)}"
def get_processing_status(self):
"""Get current processing status"""
return self.processing_status
def get_repo_structure(self):
"""Get basic repository structure for display"""
if not self.is_loaded or not self.repo_path:
return "No repository loaded"
try:
structure = []
for root, dirs, files in os.walk(self.repo_path):
# Skip hidden directories and common non-code directories
dirs[:] = [d for d in dirs if not d.startswith('.') and d not in ['node_modules', '__pycache__', 'venv', 'env']]
level = root.replace(self.repo_path, '').count(os.sep)
indent = ' ' * level
structure.append(f"{indent}{os.path.basename(root)}/")
# Limit files shown per directory
subindent = ' ' * (level + 1)
for file in files[:10]: # Show max 10 files per directory
if not file.startswith('.'):
structure.append(f"{subindent}{file}")
if len(files) > 10:
structure.append(f"{subindent}... and {len(files) - 10} more files")
# Limit depth to avoid too much output
if level > 3:
dirs.clear()
return '\n'.join(structure[:50]) # Limit total lines
except Exception as e:
return f"Error reading repository structure: {str(e)}"
def cleanup(self):
"""Clean up temporary files"""
if self.temp_dir and os.path.exists(self.temp_dir):
try:
shutil.rmtree(self.temp_dir)
self.temp_dir = None
self.repo_path = None
self.is_loaded = False
except Exception as e:
print(f"Warning: Could not clean up temp directory: {e}")
def initialize_llm(self):
"""Initialize LLM model loading"""
if not self.llm_loading_started:
print("πŸ”„ Starting LLM model loading...")
self.llm.load_model_async()
self.llm_loading_started = True
return "πŸ”„ LLM model loading started in background..."
elif self.llm.is_model_ready():
return "βœ… LLM model is ready!"
else:
return "⏳ LLM model is still loading..."
def _generate_basic_response(self, query_text: str, results: List[Dict[str, Any]]) -> str:
"""Generate basic response without LLM"""
response = f"""πŸ” **Search Results for:** "{query_text}"
πŸ“Š **Found {len(results)} relevant code sections:**
"""
for i, result in enumerate(results[:5], 1): # Show top 5 results
metadata = result.get('metadata', {})
score = result.get('score', 0)
chunk_type = metadata.get('chunk_type', 'unknown')
file_path = metadata.get('file_path', 'unknown')
response += f"""**{i}. {chunk_type.title()} Match** (Similarity: {score:.2f})
πŸ“„ File: `{file_path}`
"""
if chunk_type == 'function':
func_name = metadata.get('function_name', 'unknown')
class_name = metadata.get('class_name')
signature = metadata.get('signature', func_name)
response += f"πŸ”§ Function: `{signature}`\n"
if class_name:
response += f"πŸ“¦ Class: `{class_name}`\n"
elif chunk_type == 'class':
class_name = metadata.get('class_name', 'unknown')
methods = metadata.get('methods', [])
response += f"πŸ“¦ Class: `{class_name}`\n"
if methods:
response += f"πŸ”§ Methods: {', '.join(methods[:5])}\n"
elif chunk_type == 'file':
language = metadata.get('language', 'unknown')
total_lines = metadata.get('total_lines', 'unknown')
response += f"πŸ“ Language: {language}, Lines: {total_lines}\n"
response += "---\n\n"
# Add suggestions
if len(results) > 5:
response += f"... and {len(results) - 5} more results available.\n\n"
response += f"""πŸ’‘ **Suggestions:**
- Ask more specific questions about functions or classes
- Query about code patterns: "Show me error handling code"
- Ask about structure: "What are the main components?"
- Request examples: "How is authentication implemented?"
"""
return response