Spaces:
Sleeping
Sleeping
File size: 21,735 Bytes
60344c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 |
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"β
Repository successfully cloned! Found {total_files} files in {self.repo_name}"
except subprocess.TimeoutExpired:
return False, "β 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"β 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, "β 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"β
Repository successfully downloaded! Found {total_files} files"
else:
return False, "β Error extracting downloaded repository"
except requests.RequestException as e:
return False, f"β Network error downloading repository: {str(e)}"
except Exception as e:
return False, f"β 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"β
Zip file successfully extracted! Found {total_files} files"
except Exception as e:
return False, f"β Error extracting zip file: {str(e)}"
def initialize_vector_store(self, namespace):
"""Initialize Pinecone vector store"""
try:
if self.vector_store is None:
print("π Initializing vector store...")
self.vector_store = PineconeVectorStore(namespace=namespace)
print("β
Vector store initialized!")
return True, "Vector store ready"
except Exception as e:
error_msg = f"β 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, "β 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"π Creating chunks for {self.repo_name}...")
self.chunks = self.chunker.chunk_repository(self.repo_path)
if not self.chunks:
return False, "β 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("π 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("π 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"""β
Repository processing complete!
π **Processing Summary:**
- Repository: {self.repo_name}
- Total chunks created: {len(self.chunks)}
- Successfully stored: {result['successful_upserts']}
- Failed: {result['failed_upserts']}
π **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\nπ **Ready for queries!** You can now ask questions about your code."
return True, summary
else:
return False, f"β 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"β 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 "β 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"π 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"""π€ 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"""π€ **AI Analysis:**
{llm_response["response"]}
---
π **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\nβ οΈ 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\nβ³ **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"β Error querying repository: {str(e)}"
# Format response
# response = f"""π **Query Results for:** "{query_text}"
# π **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})
# π 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"π§ Function: `{signature}`\n"
# if class_name:
# response += f"π¦ Class: `{class_name}`\n"
# elif chunk_type == 'class':
# class_name = metadata.get('class_name', 'unknown')
# methods = metadata.get('methods', [])
# response += f"π¦ Class: `{class_name}`\n"
# if methods:
# response += f"π§ Methods: {', '.join(methods[:5])}\n"
# elif chunk_type == 'file':
# language = metadata.get('language', 'unknown')
# total_lines = metadata.get('total_lines', 'unknown')
# response += f"π 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"""π‘ **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"β 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("π Starting LLM model loading...")
self.llm.load_model_async()
self.llm_loading_started = True
return "π LLM model loading started in background..."
elif self.llm.is_model_ready():
return "β
LLM model is ready!"
else:
return "β³ 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"""π **Search Results for:** "{query_text}"
π **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})
π 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"π§ Function: `{signature}`\n"
if class_name:
response += f"π¦ Class: `{class_name}`\n"
elif chunk_type == 'class':
class_name = metadata.get('class_name', 'unknown')
methods = metadata.get('methods', [])
response += f"π¦ Class: `{class_name}`\n"
if methods:
response += f"π§ Methods: {', '.join(methods[:5])}\n"
elif chunk_type == 'file':
language = metadata.get('language', 'unknown')
total_lines = metadata.get('total_lines', 'unknown')
response += f"π 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"""π‘ **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
|