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DeepCode
/
cli
/
cli_interface.py
DeepCode
/
cli
/
cli_interface.py
cli_interface.py 48.14 KB
一键复制 编辑 原始数据 按行查看 历史
Zongwei Li 提交于 2025年11月20日 19:30 +08:00 . release new UI
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#!/usr/bin/env python3
"""
Enhanced CLI Interface Module for DeepCode
增强版CLI界面模块 - 专为DeepCode设计
"""
import os
import time
import platform
from typing import Optional
class Colors:
"""ANSI color codes for terminal styling"""
HEADER = "033円[95m"
OKBLUE = "033円[94m"
OKCYAN = "033円[96m"
OKGREEN = "033円[92m"
WARNING = "033円[93m"
FAIL = "033円[91m"
ENDC = "033円[0m"
BOLD = "033円[1m"
UNDERLINE = "033円[4m"
# Gradient colors
PURPLE = "033円[35m"
MAGENTA = "033円[95m"
BLUE = "033円[34m"
CYAN = "033円[36m"
GREEN = "033円[32m"
YELLOW = "033円[33m"
class CLIInterface:
"""Enhanced CLI interface with modern styling for DeepCode"""
def __init__(self):
self.uploaded_file = None
self.is_running = True
self.processing_history = []
self.enable_indexing = (
False # Default configuration (matching UI: fast mode by default)
)
# Load segmentation config from the same source as UI
self._load_segmentation_config()
# Initialize tkinter availability
self._init_tkinter()
def _load_segmentation_config(self):
"""Load segmentation configuration from mcp_agent.config.yaml"""
try:
from utils.llm_utils import get_document_segmentation_config
seg_config = get_document_segmentation_config()
self.segmentation_enabled = seg_config.get("enabled", True)
self.segmentation_threshold = seg_config.get("size_threshold_chars", 50000)
except Exception as e:
print(f"⚠️ Warning: Failed to load segmentation config: {e}")
# Fall back to defaults
self.segmentation_enabled = True
self.segmentation_threshold = 50000
def _save_segmentation_config(self):
"""Save segmentation configuration to mcp_agent.config.yaml"""
import yaml
import os
# Get the project root directory (where mcp_agent.config.yaml is located)
current_file = os.path.abspath(__file__)
cli_dir = os.path.dirname(current_file) # cli directory
project_root = os.path.dirname(cli_dir) # project root
config_path = os.path.join(project_root, "mcp_agent.config.yaml")
try:
# Read current config
with open(config_path, "r", encoding="utf-8") as f:
config = yaml.safe_load(f)
# Update document segmentation settings
if "document_segmentation" not in config:
config["document_segmentation"] = {}
config["document_segmentation"]["enabled"] = self.segmentation_enabled
config["document_segmentation"]["size_threshold_chars"] = (
self.segmentation_threshold
)
# Write updated config
with open(config_path, "w", encoding="utf-8") as f:
yaml.dump(config, f, default_flow_style=False, allow_unicode=True)
print(
f"{Colors.OKGREEN}✅ Document segmentation configuration updated{Colors.ENDC}"
)
except Exception as e:
print(
f"{Colors.WARNING}⚠️ Failed to update segmentation config: {str(e)}{Colors.ENDC}"
)
def _init_tkinter(self):
"""Initialize tkinter availability check"""
# Check tkinter availability for file dialogs
self.tkinter_available = True
try:
import tkinter as tk
# Test if tkinter can create a window
test_root = tk.Tk()
test_root.withdraw()
test_root.destroy()
except Exception:
self.tkinter_available = False
def clear_screen(self):
"""Clear terminal screen"""
os.system("cls" if os.name == "nt" else "clear")
def print_logo(self):
"""Print enhanced ASCII logo for DeepCode CLI"""
logo = f"""
{Colors.CYAN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ ║
{Colors.BOLD}{Colors.MAGENTA}██████╗ ███████╗███████╗██████╗ ██████╗ ██████╗ ██████╗ ███████╗{Colors.CYAN}
{Colors.BOLD}{Colors.PURPLE}██╔══██╗██╔════╝██╔════╝██╔══██╗██╔════╝██╔═══██╗██╔══██╗██╔════╝{Colors.CYAN}
{Colors.BOLD}{Colors.BLUE}██║ ██║█████╗ █████╗ ██████╔╝██║ ██║ ██║██║ ██║█████╗ {Colors.CYAN}
{Colors.BOLD}{Colors.OKBLUE}██║ ██║██╔══╝ ██╔══╝ ██╔═══╝ ██║ ██║ ██║██║ ██║██╔══╝ {Colors.CYAN}
{Colors.BOLD}{Colors.OKCYAN}██████╔╝███████╗███████╗██║ ╚██████╗╚██████╔╝██████╔╝███████╗{Colors.CYAN}
{Colors.BOLD}{Colors.GREEN}╚═════╝ ╚══════╝╚══════╝╚═╝ ╚═════╝ ╚═════╝ ╚═════╝ ╚══════╝{Colors.CYAN}
║ ║
{Colors.BOLD}{Colors.GREEN}🧬 OPEN-SOURCE CODE AGENT • DATA INTELLIGENCE LAB @ HKU 🚀 {Colors.CYAN}
{Colors.BOLD}{Colors.GREEN}⚡ REVOLUTIONIZING RESEARCH REPRODUCIBILITY ⚡ {Colors.CYAN}
║ ║
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
"""
print(logo)
def print_welcome_banner(self):
"""Print enhanced welcome banner"""
banner = f"""
{Colors.BOLD}{Colors.CYAN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ WELCOME TO DEEPCODE CLI ║
╠═══════════════════════════════════════════════════════════════════════════════╣
{Colors.YELLOW}Open-Source Code Agent | Data Intelligence Lab @ HKU | MIT License {Colors.CYAN}
{Colors.GREEN}Status: Ready | Engine: Multi-Agent Architecture Initialized {Colors.CYAN}
{Colors.PURPLE}Mission: Revolutionizing Research Reproducibility {Colors.CYAN}
║ ║
{Colors.BOLD}{Colors.OKCYAN}💎 CORE CAPABILITIES:{Colors.ENDC}{Colors.CYAN}
{Colors.BOLD}{Colors.OKCYAN}▶ Automated Paper-to-Code Reproduction {Colors.CYAN}
{Colors.BOLD}{Colors.OKCYAN}▶ Collaborative Multi-Agent Architecture {Colors.CYAN}
{Colors.BOLD}{Colors.OKCYAN}▶ Intelligent Code Implementation & Validation {Colors.CYAN}
{Colors.BOLD}{Colors.OKCYAN}▶ Future Vision: One Sentence → Complete Codebase {Colors.CYAN}
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
"""
print(banner)
def print_separator(self, char="═", length=79, color=Colors.CYAN):
"""Print a styled separator line"""
print(f"{color}{char * length}{Colors.ENDC}")
def print_status(self, message: str, status_type: str = "info"):
"""Print status message with appropriate styling"""
status_styles = {
"success": f"{Colors.OKGREEN}✅",
"error": f"{Colors.FAIL}❌",
"warning": f"{Colors.WARNING}⚠️ ",
"info": f"{Colors.OKBLUE}i️ ",
"processing": f"{Colors.YELLOW}⏳",
"upload": f"{Colors.PURPLE}📁",
"download": f"{Colors.CYAN}📥",
"analysis": f"{Colors.MAGENTA}🔍",
"implementation": f"{Colors.GREEN}⚙️ ",
"complete": f"{Colors.OKGREEN}🎉",
}
icon = status_styles.get(status_type, status_styles["info"])
timestamp = time.strftime("%H:%M:%S")
print(
f"[{Colors.BOLD}{timestamp}{Colors.ENDC}] {icon}{Colors.BOLD}{message}{Colors.ENDC}"
)
def create_menu(self):
"""Create enhanced interactive menu"""
# Display current configuration
pipeline_mode = "🧠 COMPREHENSIVE" if self.enable_indexing else "⚡ OPTIMIZED"
index_status = "✅ Enabled" if self.enable_indexing else "🔶 Disabled"
segmentation_mode = (
"📄 SMART" if self.segmentation_enabled else "📋 TRADITIONAL"
)
menu = f"""
{Colors.BOLD}{Colors.CYAN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ MAIN MENU ║
╠═══════════════════════════════════════════════════════════════════════════════╣
{Colors.OKGREEN}🌐 [U] Process URL {Colors.CYAN}{Colors.PURPLE}📁 [F] Upload File {Colors.CYAN}{Colors.MAGENTA}💬 [T] Chat Input{Colors.CYAN}
{Colors.BLUE}🧠 [R] Req. Analysis {Colors.CYAN}{Colors.OKCYAN}⚙️ [C] Configure {Colors.CYAN}{Colors.YELLOW}📊 [H] History{Colors.CYAN}
{Colors.FAIL}❌ [Q] Quit{Colors.CYAN}
║ ║
{Colors.BOLD}🤖 Current Pipeline Mode: {pipeline_mode}{Colors.CYAN}
{Colors.BOLD}🗂️ Codebase Indexing: {index_status}{Colors.CYAN}
{Colors.BOLD}📄 Document Processing: {segmentation_mode}{Colors.CYAN}
║ ║
{Colors.YELLOW}📝 URL Processing:{Colors.CYAN}
{Colors.YELLOW} ▶ Enter research paper URL (arXiv, IEEE, ACM, etc.) {Colors.CYAN}
{Colors.YELLOW} ▶ Supports direct PDF links and academic paper pages {Colors.CYAN}
║ ║
{Colors.PURPLE}📁 File Processing:{Colors.CYAN}
{Colors.PURPLE} ▶ Upload PDF, DOCX, PPTX, HTML, or TXT files {Colors.CYAN}
{Colors.PURPLE} ▶ Intelligent file format detection and processing {Colors.CYAN}
║ ║
{Colors.MAGENTA}💬 Chat Input:{Colors.CYAN}
{Colors.MAGENTA} ▶ Describe your coding requirements in natural language {Colors.CYAN}
{Colors.MAGENTA} ▶ AI generates implementation plan and code automatically {Colors.CYAN}
║ ║
{Colors.BLUE}🧠 Requirement Analysis (NEW):{Colors.CYAN}
{Colors.BLUE} ▶ Get AI-guided questions to refine your requirements {Colors.CYAN}
{Colors.BLUE} ▶ Generate detailed requirement documents from your answers {Colors.CYAN}
║ ║
{Colors.OKCYAN}🔄 Processing Pipeline:{Colors.CYAN}
{Colors.OKCYAN} ▶ Intelligent agent orchestration → Code synthesis {Colors.CYAN}
{Colors.OKCYAN} ▶ Multi-agent coordination with progress tracking {Colors.CYAN}
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
"""
print(menu)
def get_user_input(self):
"""Get user input with styled prompt"""
print(f"\n{Colors.BOLD}{Colors.OKCYAN}➤ Your choice: {Colors.ENDC}", end="")
return input().strip().lower()
def upload_file_gui(self) -> Optional[str]:
"""Enhanced file upload interface with better error handling"""
if not self.tkinter_available:
self.print_status(
"GUI file dialog not available - using manual input", "warning"
)
return self._get_manual_file_path()
def select_file():
try:
import tkinter as tk
from tkinter import filedialog
root = tk.Tk()
root.withdraw()
root.attributes("-topmost", True)
file_types = [
("Research Papers", "*.pdf;*.docx;*.doc"),
("PDF Files", "*.pdf"),
("Word Documents", "*.docx;*.doc"),
("PowerPoint Files", "*.pptx;*.ppt"),
("HTML Files", "*.html;*.htm"),
("Text Files", "*.txt;*.md"),
("All Files", "*.*"),
]
if platform.system() == "Darwin":
file_types = [
("Research Papers", ".pdf .docx .doc"),
("PDF Files", ".pdf"),
("Word Documents", ".docx .doc"),
("PowerPoint Files", ".pptx .ppt"),
("HTML Files", ".html .htm"),
("Text Files", ".txt .md"),
("All Files", ".*"),
]
file_path = filedialog.askopenfilename(
title="Select Research File - DeepCode CLI",
filetypes=file_types,
initialdir=os.getcwd(),
)
root.destroy()
return file_path
except Exception as e:
self.print_status(f"File dialog error: {str(e)}", "error")
return self._get_manual_file_path()
self.print_status("Opening file browser dialog...", "upload")
file_path = select_file()
if file_path:
self.print_status(
f"File selected: {os.path.basename(file_path)}", "success"
)
return file_path
else:
self.print_status("No file selected", "warning")
return None
def _get_manual_file_path(self) -> Optional[str]:
"""Get file path through manual input with validation"""
self.print_separator("─", 79, Colors.YELLOW)
print(f"{Colors.BOLD}{Colors.YELLOW}📁 Manual File Path Input{Colors.ENDC}")
print(
f"{Colors.CYAN}Please enter the full path to your research paper file:{Colors.ENDC}"
)
print(
f"{Colors.CYAN}Supported formats: PDF, DOCX, PPTX, HTML, TXT, MD{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.YELLOW)
while True:
print(f"\n{Colors.BOLD}{Colors.OKCYAN}📂 File path: {Colors.ENDC}", end="")
file_path = input().strip()
if not file_path:
self.print_status(
"Empty path entered. Please try again or press Ctrl+C to cancel.",
"warning",
)
continue
file_path = os.path.expanduser(file_path)
file_path = os.path.abspath(file_path)
if not os.path.exists(file_path):
self.print_status(f"File not found: {file_path}", "error")
retry = (
input(f"{Colors.YELLOW}Try again? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if retry != "y":
return None
continue
if not os.path.isfile(file_path):
self.print_status(f"Path is not a file: {file_path}", "error")
continue
supported_extensions = {
".pdf",
".docx",
".doc",
".pptx",
".ppt",
".html",
".htm",
".txt",
".md",
}
file_ext = os.path.splitext(file_path)[1].lower()
if file_ext not in supported_extensions:
self.print_status(f"Unsupported file format: {file_ext}", "warning")
proceed = (
input(f"{Colors.YELLOW}Process anyway? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if proceed != "y":
continue
self.print_status(
f"File validated: {os.path.basename(file_path)}", "success"
)
return file_path
def get_url_input(self) -> str:
"""Enhanced URL input with validation"""
self.print_separator("─", 79, Colors.GREEN)
print(f"{Colors.BOLD}{Colors.GREEN}🌐 URL Input Interface{Colors.ENDC}")
print(
f"{Colors.CYAN}Enter a research paper URL from supported platforms:{Colors.ENDC}"
)
print(
f"{Colors.CYAN}• arXiv (arxiv.org) • IEEE Xplore (ieeexplore.ieee.org){Colors.ENDC}"
)
print(
f"{Colors.CYAN}• ACM Digital Library • SpringerLink • Nature • Science{Colors.ENDC}"
)
print(
f"{Colors.CYAN}• Direct PDF links • Academic publisher websites{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.GREEN)
while True:
print(f"\n{Colors.BOLD}{Colors.OKCYAN}🔗 URL: {Colors.ENDC}", end="")
url = input().strip()
if not url:
self.print_status(
"Empty URL entered. Please try again or press Ctrl+C to cancel.",
"warning",
)
continue
if not url.startswith(("http://", "https://")):
self.print_status("URL must start with http:// or https://", "error")
retry = (
input(f"{Colors.YELLOW}Try again? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if retry != "y":
return ""
continue
academic_domains = [
"arxiv.org",
"ieeexplore.ieee.org",
"dl.acm.org",
"link.springer.com",
"nature.com",
"science.org",
"scholar.google.com",
"researchgate.net",
"semanticscholar.org",
]
is_academic = any(domain in url.lower() for domain in academic_domains)
if not is_academic and not url.lower().endswith(".pdf"):
self.print_status(
"URL doesn't appear to be from a known academic platform", "warning"
)
proceed = (
input(f"{Colors.YELLOW}Process anyway? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if proceed != "y":
continue
self.print_status(f"URL validated: {url}", "success")
return url
def get_chat_input(self) -> str:
"""Enhanced chat input interface for coding requirements"""
self.print_separator("─", 79, Colors.PURPLE)
print(f"{Colors.BOLD}{Colors.PURPLE}💬 Chat Input Interface{Colors.ENDC}")
print(
f"{Colors.CYAN}Describe your coding requirements in natural language.{Colors.ENDC}"
)
print(
f"{Colors.CYAN}Our AI will analyze your needs and generate a comprehensive implementation plan.{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.PURPLE)
# Display examples to help users
print(f"\n{Colors.BOLD}{Colors.YELLOW}💡 Examples:{Colors.ENDC}")
print(f"{Colors.CYAN}Academic Research:{Colors.ENDC}")
print(
" • 'I need to implement a reinforcement learning algorithm for robotic control'"
)
print(
" • 'Create a neural network for image classification with attention mechanisms'"
)
print(f"{Colors.CYAN}Engineering Projects:{Colors.ENDC}")
print(
" • 'Develop a web application for project management with user authentication'"
)
print(" • 'Create a data visualization dashboard for sales analytics'")
print(f"{Colors.CYAN}Mixed Projects:{Colors.ENDC}")
print(
" • 'Implement a machine learning model with a web interface for real-time predictions'"
)
self.print_separator("─", 79, Colors.PURPLE)
print(
f"\n{Colors.BOLD}{Colors.OKCYAN}✏️ Enter your coding requirements below:{Colors.ENDC}"
)
print(
f"{Colors.YELLOW}(Type your description, press Enter twice when finished, or Ctrl+C to cancel){Colors.ENDC}"
)
lines = []
empty_line_count = 0
while True:
try:
if len(lines) == 0:
print(f"{Colors.BOLD}> {Colors.ENDC}", end="")
else:
print(f"{Colors.BOLD}{Colors.ENDC}", end="")
line = input()
if line.strip() == "":
empty_line_count += 1
if empty_line_count >= 2:
# Two consecutive empty lines means user finished input
break
lines.append("") # Keep empty line for formatting
else:
empty_line_count = 0
lines.append(line)
except KeyboardInterrupt:
print(f"\n{Colors.WARNING}Input cancelled by user{Colors.ENDC}")
return ""
# Join all lines and clean up
user_input = "\n".join(lines).strip()
if not user_input:
self.print_status("No input provided", "warning")
return ""
if len(user_input) < 20:
self.print_status(
"Input too short. Please provide more detailed requirements (at least 20 characters)",
"warning",
)
retry = (
input(f"{Colors.YELLOW}Try again? (y/n): {Colors.ENDC}").strip().lower()
)
if retry == "y":
return self.get_chat_input() # Recursive call for retry
return ""
# Display input summary
word_count = len(user_input.split())
char_count = len(user_input)
print(f"\n{Colors.BOLD}{Colors.GREEN}📋 Input Summary:{Colors.ENDC}")
print(f" • {Colors.CYAN}Word count: {word_count}{Colors.ENDC}")
print(f" • {Colors.CYAN}Character count: {char_count}{Colors.ENDC}")
# Show preview
preview = user_input[:200] + "..." if len(user_input) > 200 else user_input
print(f"\n{Colors.BOLD}{Colors.CYAN}📄 Preview:{Colors.ENDC}")
print(f"{Colors.YELLOW}{preview}{Colors.ENDC}")
# Confirm with user
confirm = (
input(
f"\n{Colors.BOLD}{Colors.OKCYAN}Proceed with this input? (y/n): {Colors.ENDC}"
)
.strip()
.lower()
)
if confirm != "y":
retry = (
input(f"{Colors.YELLOW}Edit input? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if retry == "y":
return self.get_chat_input() # Recursive call for retry
return ""
self.print_status(
f"Chat input captured: {word_count} words, {char_count} characters",
"success",
)
return user_input
def show_progress_bar(self, message: str, duration: float = 2.0):
"""Show animated progress bar"""
print(f"\n{Colors.BOLD}{Colors.CYAN}{message}{Colors.ENDC}")
bar_length = 50
for i in range(bar_length + 1):
percent = (i / bar_length) * 100
filled = "█" * i
empty = "░" * (bar_length - i)
print(
f"\r{Colors.OKGREEN}[{filled}{empty}] {percent:3.0f}%{Colors.ENDC}",
end="",
flush=True,
)
time.sleep(duration / bar_length)
print(f"\n{Colors.OKGREEN}{message} completed{Colors.ENDC}")
def show_spinner(self, message: str, duration: float = 1.0):
"""Show spinner animation"""
spinner_chars = "⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏"
end_time = time.time() + duration
print(
f"{Colors.BOLD}{Colors.CYAN}{message}... {Colors.ENDC}", end="", flush=True
)
i = 0
while time.time() < end_time:
print(
f"\r{Colors.BOLD}{Colors.CYAN}{message}... {Colors.YELLOW}{spinner_chars[i % len(spinner_chars)]}{Colors.ENDC}",
end="",
flush=True,
)
time.sleep(0.1)
i += 1
print(
f"\r{Colors.BOLD}{Colors.CYAN}{message}... {Colors.OKGREEN}{Colors.ENDC}"
)
def display_processing_stages(
self,
current_stage: int = 0,
enable_indexing: bool = True,
chat_mode: bool = False,
):
"""Display processing pipeline stages with current progress"""
if chat_mode:
# Chat mode - simplified workflow for user requirements
stages = [
("🚀", "Initialize", "Setting up chat engine"),
("💬", "Planning", "Analyzing requirements"),
("🏗️", "Setup", "Creating workspace"),
("📝", "Save Plan", "Saving implementation plan"),
("⚙️", "Implement", "Generating code"),
]
pipeline_mode = "CHAT PLANNING"
elif enable_indexing:
# Full pipeline with all stages
stages = [
("🚀", "Initialize", "Setting up AI engine"),
("📊", "Analyze", "Analyzing research content"),
("📥", "Download", "Processing document"),
("📋", "Plan", "Generating code architecture"),
("🔍", "References", "Analyzing references"),
("📦", "Repos", "Downloading repositories"),
("🗂️", "Index", "Building code index"),
("⚙️", "Implement", "Implementing code"),
]
pipeline_mode = "COMPREHENSIVE"
else:
# Fast mode - skip indexing related stages
stages = [
("🚀", "Initialize", "Setting up AI engine"),
("📊", "Analyze", "Analyzing research content"),
("📥", "Download", "Processing document"),
("📋", "Plan", "Generating code architecture"),
("⚙️", "Implement", "Implementing code"),
]
pipeline_mode = "OPTIMIZED"
print(
f"\n{Colors.BOLD}{Colors.CYAN}📋 {pipeline_mode} PIPELINE STATUS{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.CYAN)
for i, (icon, name, desc) in enumerate(stages):
if i < current_stage:
status = f"{Colors.OKGREEN}✓ COMPLETED{Colors.ENDC}"
elif i == current_stage:
status = f"{Colors.YELLOW}⏳ IN PROGRESS{Colors.ENDC}"
else:
status = f"{Colors.CYAN}⏸️ PENDING{Colors.ENDC}"
print(
f"{icon}{Colors.BOLD}{name:<12}{Colors.ENDC}{desc:<25}{status}"
)
self.print_separator("─", 79, Colors.CYAN)
def print_results_header(self):
"""Print results section header"""
header = f"""
{Colors.BOLD}{Colors.OKGREEN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ PROCESSING RESULTS ║
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
"""
print(header)
def print_error_box(self, title: str, error_msg: str):
"""Print formatted error box"""
print(
f"\n{Colors.FAIL}╔══════════════════════════════════════════════════════════════╗"
)
print(f"║ {Colors.BOLD}ERROR: {title:<50}{Colors.FAIL} ║")
print("╠══════════════════════════════════════════════════════════════╣")
words = error_msg.split()
lines = []
current_line = ""
for word in words:
if len(current_line + word) <= 54:
current_line += word + " "
else:
lines.append(current_line.strip())
current_line = word + " "
if current_line:
lines.append(current_line.strip())
for line in lines:
print(f"║ {line:<56} ║")
print(
f"╚══════════════════════════════════════════════════════════════╝{Colors.ENDC}"
)
def cleanup_cache(self):
"""清理Python缓存文件 / Clean up Python cache files"""
try:
self.print_status("Cleaning up cache files...", "info")
# 清理__pycache__目录
os.system('find . -type d -name "__pycache__" -exec rm -r {} + 2>/dev/null')
# 清理.pyc文件
os.system('find . -name "*.pyc" -delete 2>/dev/null')
self.print_status("Cache cleanup completed", "success")
except Exception as e:
self.print_status(f"Cache cleanup failed: {e}", "warning")
def print_goodbye(self):
"""Print goodbye message"""
# 清理缓存文件
self.cleanup_cache()
goodbye = f"""
{Colors.BOLD}{Colors.CYAN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ GOODBYE ║
╠═══════════════════════════════════════════════════════════════════════════════╣
{Colors.OKGREEN}🎉 Thank you for using DeepCode CLI! {Colors.CYAN}
║ ║
{Colors.YELLOW}🧬 Join our community in revolutionizing research reproducibility {Colors.CYAN}
{Colors.PURPLE}⚡ Together, we're building the future of automated code generation {Colors.CYAN}
║ ║
{Colors.OKCYAN}💡 Questions? Contribute to our open-source mission at GitHub {Colors.CYAN}
{Colors.GREEN}🧹 Cache files cleaned up for optimal performance {Colors.CYAN}
║ ║
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
"""
print(goodbye)
def get_requirement_analysis_input(self) -> str:
"""Enhanced requirement analysis input interface (NEW: matching UI version)"""
self.print_separator("─", 79, Colors.BLUE)
print(
f"{Colors.BOLD}{Colors.BLUE}🧠 Requirement Analysis Interface{Colors.ENDC}"
)
print(
f"{Colors.CYAN}Describe your project idea or requirements briefly.{Colors.ENDC}"
)
print(
f"{Colors.CYAN}Our AI will generate guiding questions to help you refine your vision.{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.BLUE)
# Display examples
print(f"\n{Colors.BOLD}{Colors.YELLOW}💡 Examples:{Colors.ENDC}")
print(
f"{Colors.CYAN} • 'I want to build a machine learning system for image recognition'{Colors.ENDC}"
)
print(
f"{Colors.CYAN} • 'Create a web app for project management with real-time collaboration'{Colors.ENDC}"
)
print(
f"{Colors.CYAN} • 'Develop a data analysis pipeline for financial forecasting'{Colors.ENDC}"
)
self.print_separator("─", 79, Colors.BLUE)
print(
f"\n{Colors.BOLD}{Colors.OKCYAN}✏️ Enter your initial requirements below:{Colors.ENDC}"
)
print(
f"{Colors.YELLOW}(Type your description, press Enter twice when finished, or Ctrl+C to cancel){Colors.ENDC}"
)
lines = []
empty_line_count = 0
while True:
try:
if len(lines) == 0:
print(f"{Colors.BOLD}> {Colors.ENDC}", end="")
else:
print(f"{Colors.BOLD}{Colors.ENDC}", end="")
line = input()
if line.strip() == "":
empty_line_count += 1
if empty_line_count >= 2:
break
lines.append("")
else:
empty_line_count = 0
lines.append(line)
except KeyboardInterrupt:
print(f"\n{Colors.WARNING}Input cancelled by user{Colors.ENDC}")
return ""
user_input = "\n".join(lines).strip()
if not user_input:
self.print_status("No input provided", "warning")
return ""
if len(user_input) < 20:
self.print_status(
"Input too short. Please provide more details (at least 20 characters)",
"warning",
)
retry = (
input(f"{Colors.YELLOW}Try again? (y/n): {Colors.ENDC}").strip().lower()
)
if retry == "y":
return self.get_requirement_analysis_input()
return ""
# Display input summary
word_count = len(user_input.split())
char_count = len(user_input)
print(f"\n{Colors.BOLD}{Colors.GREEN}📋 Input Summary:{Colors.ENDC}")
print(f" • {Colors.CYAN}Word count: {word_count}{Colors.ENDC}")
print(f" • {Colors.CYAN}Character count: {char_count}{Colors.ENDC}")
# Show preview
preview = user_input[:200] + "..." if len(user_input) > 200 else user_input
print(f"\n{Colors.BOLD}{Colors.CYAN}📄 Preview:{Colors.ENDC}")
print(f"{Colors.YELLOW}{preview}{Colors.ENDC}")
# Confirm
confirm = (
input(
f"\n{Colors.BOLD}{Colors.OKCYAN}Proceed with this input? (y/n): {Colors.ENDC}"
)
.strip()
.lower()
)
if confirm != "y":
retry = (
input(f"{Colors.YELLOW}Edit input? (y/n): {Colors.ENDC}")
.strip()
.lower()
)
if retry == "y":
return self.get_requirement_analysis_input()
return ""
self.print_status(
f"Requirement input captured: {word_count} words, {char_count} characters",
"success",
)
return user_input
def display_guiding_questions(self, questions_json: str):
"""Display AI-generated guiding questions (NEW: matching UI version)"""
import json
try:
questions = json.loads(questions_json)
self.print_separator("═", 79, Colors.GREEN)
print(
f"\n{Colors.BOLD}{Colors.GREEN}🤖 AI-Generated Guiding Questions{Colors.ENDC}"
)
print(
f"{Colors.CYAN}Please answer these questions to help refine your requirements:{Colors.ENDC}\n"
)
self.print_separator("─", 79, Colors.GREEN)
for i, q in enumerate(questions, 1):
print(
f"\n{Colors.BOLD}{Colors.YELLOW}Question {i}:{Colors.ENDC}{Colors.CYAN}{q}{Colors.ENDC}"
)
self.print_separator("═", 79, Colors.GREEN)
except json.JSONDecodeError:
self.print_status("Failed to parse questions", "error")
print(questions_json)
def get_question_answers(self, questions_json: str) -> dict:
"""Get user answers to guiding questions (NEW: matching UI version)"""
import json
try:
questions = json.loads(questions_json)
answers = {}
print(
f"\n{Colors.BOLD}{Colors.BLUE}📝 Answer the following questions:{Colors.ENDC}"
)
print(
f"{Colors.CYAN}(Type your answer and press Enter for each question){Colors.ENDC}\n"
)
for i, question in enumerate(questions, 1):
print(
f"\n{Colors.BOLD}{Colors.YELLOW}Q{i}:{Colors.ENDC}{Colors.CYAN}{question}{Colors.ENDC}"
)
print(f"{Colors.BOLD}{Colors.OKCYAN}Your answer:{Colors.ENDC} ", end="")
answer = input().strip()
answers[f"question_{i}"] = answer
if answer:
self.print_status(f"Answer {i} recorded", "success")
else:
self.print_status(f"Answer {i} left blank", "warning")
return answers
except json.JSONDecodeError:
self.print_status("Failed to parse questions", "error")
return {}
def display_requirement_summary(self, summary: str):
"""Display generated requirement document (NEW: matching UI version)"""
self.print_separator("═", 79, Colors.GREEN)
print(
f"\n{Colors.BOLD}{Colors.GREEN}📄 Generated Requirement Document{Colors.ENDC}\n"
)
self.print_separator("─", 79, Colors.GREEN)
print(f"{Colors.CYAN}{summary}{Colors.ENDC}")
self.print_separator("═", 79, Colors.GREEN)
# Ask if user wants to proceed with implementation
proceed = (
input(
f"\n{Colors.BOLD}{Colors.YELLOW}Would you like to proceed with code implementation based on these requirements? (y/n):{Colors.ENDC} "
)
.strip()
.lower()
)
return proceed == "y"
def ask_continue(self) -> bool:
"""Ask if user wants to continue with another paper"""
self.print_separator("─", 79, Colors.YELLOW)
print(f"\n{Colors.BOLD}{Colors.YELLOW}🔄 Process another paper?{Colors.ENDC}")
choice = input(f"{Colors.OKCYAN}Continue? (y/n): {Colors.ENDC}").strip().lower()
return choice in ["y", "yes", "1", "true"]
def add_to_history(self, input_source: str, result: dict):
"""Add processing result to history"""
entry = {
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
"input_source": input_source,
"status": result.get("status", "unknown"),
"result": result,
}
self.processing_history.append(entry)
def show_history(self):
"""Display processing history"""
if not self.processing_history:
self.print_status("No processing history available", "info")
return
print(f"\n{Colors.BOLD}{Colors.CYAN}📚 PROCESSING HISTORY{Colors.ENDC}")
self.print_separator("─", 79, Colors.CYAN)
for i, entry in enumerate(self.processing_history, 1):
status_icon = "✅" if entry["status"] == "success" else "❌"
source = entry["input_source"]
if len(source) > 50:
source = source[:47] + "..."
print(f"{i}. {status_icon}{entry['timestamp']} | {source}")
self.print_separator("─", 79, Colors.CYAN)
def show_configuration_menu(self):
"""Show configuration options menu"""
self.clear_screen()
# Get segmentation config status
segmentation_enabled = getattr(self, "segmentation_enabled", True)
segmentation_threshold = getattr(self, "segmentation_threshold", 50000)
print(f"""
{Colors.BOLD}{Colors.CYAN}╔═══════════════════════════════════════════════════════════════════════════════╗
║ CONFIGURATION MENU ║
╠═══════════════════════════════════════════════════════════════════════════════╣
║ ║
{Colors.BOLD}🤖 Agent Orchestration Engine Configuration{Colors.CYAN}
║ ║
{Colors.OKCYAN}[1] Pipeline Mode:{Colors.CYAN}
{Colors.BOLD}🧠 Comprehensive Mode{Colors.CYAN} - Full intelligence analysis (Default) ║
║ ✓ Research Analysis + Resource Processing ║
║ ✓ Reference Intelligence Discovery ║
║ ✓ Automated Repository Acquisition ║
║ ✓ Codebase Intelligence Orchestration ║
║ ✓ Intelligent Code Implementation Synthesis ║
║ ║
{Colors.BOLD}⚡ Optimized Mode{Colors.CYAN} - Fast processing (Skip indexing) ║
║ ✓ Research Analysis + Resource Processing ║
║ ✓ Code Architecture Synthesis ║
║ ✓ Intelligent Code Implementation Synthesis ║
║ ✗ Reference Intelligence Discovery (Skipped) ║
║ ✗ Repository Acquisition (Skipped) ║
║ ✗ Codebase Intelligence Orchestration (Skipped) ║
║ ║
{Colors.OKCYAN}[2] Document Processing:{Colors.CYAN}
{Colors.BOLD}📄 Smart Segmentation{Colors.CYAN} - Intelligent document analysis (Default) ║
║ ✓ Semantic boundary detection ║
║ ✓ Algorithm integrity preservation ║
║ ✓ Formula chain recognition ║
║ ✓ Adaptive character limits ║
║ ║
{Colors.BOLD}📋 Traditional Processing{Colors.CYAN} - Full document reading ║
║ ✓ Complete document analysis ║
║ ✗ Smart segmentation (Disabled) ║
║ ║
{Colors.YELLOW}Current Settings:{Colors.CYAN}
║ Pipeline: {'🧠 Comprehensive Mode' if self.enable_indexing else '⚡ Optimized Mode'}
║ Document: {'📄 Smart Segmentation' if segmentation_enabled else '📋 Traditional Processing'}
║ Threshold: {segmentation_threshold} characters ║
║ ║
{Colors.OKGREEN}[T] Toggle Pipeline {Colors.BLUE}[S] Toggle Segmentation {Colors.FAIL}[B] Back{Colors.CYAN}
╚═══════════════════════════════════════════════════════════════════════════════╝{Colors.ENDC}
""")
while True:
print(
f"\n{Colors.BOLD}{Colors.OKCYAN}➤ Configuration choice: {Colors.ENDC}",
end="",
)
choice = input().strip().lower()
if choice in ["t", "toggle"]:
self.enable_indexing = not self.enable_indexing
mode = "🧠 Comprehensive" if self.enable_indexing else "⚡ Optimized"
self.print_status(f"Pipeline mode switched to: {mode}", "success")
time.sleep(1)
self.show_configuration_menu()
return
elif choice in ["s", "segmentation"]:
current_state = getattr(self, "segmentation_enabled", True)
self.segmentation_enabled = not current_state
# Save the configuration to file
self._save_segmentation_config()
seg_mode = (
"📄 Smart Segmentation"
if self.segmentation_enabled
else "📋 Traditional Processing"
)
self.print_status(
f"Document processing switched to: {seg_mode}", "success"
)
time.sleep(1)
self.show_configuration_menu()
return
elif choice in ["b", "back"]:
return
else:
self.print_status(
"Invalid choice. Please enter 'T', 'S', or 'B'.", "warning"
)
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