"""
Purpose: A unified pipeline that performs end-to-end effort analysis on a dataset of GitHub issues.
It combines agentic deep analysis, single-turn fallbacks, heuristic validation, and CSV export in a single, efficient execution, avoiding redundant file operations.
"""
import argparse
import json
import urllib.request
import os
import subprocess
import re
import concurrent.futures
import threading
import csv
from datetime import datetime
from pathlib import Path

MODEL = "gemini-3-flash-preview"
file_lock = threading.Lock()

tools_decl = [
    {
        "functionDeclarations": [
            {
                "name": "search_codebase",
                "description": "Search the project directory for a string using grep. Returns matching lines and file paths.",
                "parameters": {
                    "type": "OBJECT",
                    "properties": {
                        "pattern": {"type": "STRING", "description": "The text pattern to search for"}
                    },
                    "required": ["pattern"]
                }
            },
            {
                "name": "read_file",
                "description": "Read a specific file to understand its context.",
                "parameters": {
                    "type": "OBJECT",
                    "properties": {
                        "filepath": {"type": "STRING", "description": "The path to the file"}
                    },
                    "required": ["filepath"]
                }
            }
        ]
    }
]

def call_gemini(messages, url):
    data = {
        "contents": messages,
        "tools": tools_decl,
        "generationConfig": {"temperature": 0.1}
    }
    req = urllib.request.Request(url, data=json.dumps(data).encode('utf-8'), headers={'Content-Type': 'application/json'})
    with urllib.request.urlopen(req, timeout=120) as response:
        return json.loads(response.read().decode('utf-8'))

def execute_tool(call, project_path):
    name = call['name']
    args = call.get('args', {})
    
    if name == 'search_codebase':
        pattern = args.get('pattern', '').replace('"', '\\"')
        try:
            cmd = f'grep -rn "{pattern}" "{project_path}" | grep -vE "node_modules|dist|build|\\.test\\." | head -n 20'
            res = subprocess.check_output(cmd, shell=True, text=True, stderr=subprocess.STDOUT)
            return res if res else "No matches found."
        except subprocess.CalledProcessError as e:
            return e.output if e.output else "No matches found."
    elif name == 'read_file':
        filepath = args.get('filepath', '')
        if not filepath.startswith('/'):
            filepath = os.path.join(project_path, filepath)
        
        try:
            if not os.path.exists(filepath):
                basename = os.path.basename(filepath)
                find_cmd = f'find "{project_path}" -name "{basename}" | head -n 1'
                found_path = subprocess.check_output(find_cmd, shell=True, text=True).strip()
                if found_path: filepath = found_path
                else: return f"File {filepath} not found."
            
            cmd = f'head -n 300 "{filepath}"'
            res = subprocess.check_output(cmd, shell=True, text=True, stderr=subprocess.STDOUT)
            return res
        except Exception as e:
            return str(e)
    return "Unknown tool"

def analyze_issue_agentic(issue, url, project_path):
    system_instruction = """You are a senior software engineer analyzing bug/feature reports. 
You MUST use the provided tools to investigate the codebase and pinpoint exactly which files and logic are responsible. 
DO NOT GUESS.

Rating Effort Level:
- small (1 day): Localized fix/change (1-2 files), clear logic.
- medium (2-3 days): Harder to trace, state management, touches multiple components.
- large (>3 days): Architectural issues, core protocol changes, or very complex multi-package bugs.

REPRODUCTION RULE:
If a bug is hard to reproduce (specific OS, complex setup, intermittent/flickering), it MUST NOT be rated as small.

Output format (ONLY valid JSON, NO markdown):
{
  "analysis": "technical analysis of root cause and fix",
  "effort_level": "small|medium|large",
  "reasoning": "justification with specific files/lines/logic you found",
  "recommended_implementation": "code snippets or specific logic changes (only if small)"
}
"""
    prompt = f"{system_instruction}\n\nIssue Title: {issue.get('title')}\nIssue Body: {issue.get('body', '')[:1500]}"
    messages = [{"role": "user", "parts": [{"text": prompt}]}]
    
    for turn in range(15):  # Limit turns to 15 for efficiency in unified loop
        try:
            res = call_gemini(messages, url)
            candidate = res['candidates'][0]['content']
            parts = candidate.get('parts', [])
            
            if 'role' not in candidate: candidate['role'] = 'model'
            messages.append(candidate)
            
            function_calls = [p for p in parts if 'functionCall' in p]
            
            if function_calls:
                tool_responses = []
                for fcall in function_calls:
                    call_data = fcall['functionCall']
                    result = execute_tool(call_data, project_path)
                    tool_responses.append({
                        "functionResponse": {
                            "name": call_data['name'],
                            "response": {"result": result[:5000]}
                        }
                    })
                messages.append({"role": "user", "parts": tool_responses})
            else:
                text = parts[0].get('text', '')
                if not text: continue
                text = text.replace('```json', '').replace('```', '').strip()
                return json.loads(text)
        except Exception as e:
            break
            
    return {"analysis": "Failed to analyze autonomously", "effort_level": "medium", "reasoning": "Agent loop exceeded limit or errored."}

def extract_keywords(text):
    words = re.findall(r'\b[A-Z][a-zA-Z0-9]+\b|\b\w+\.tsx?\b|\b\w+Service\b|\b\w+Command\b', text)
    words = list(set([w for w in words if len(w) > 4]))
    return words[:8]

def search_codebase_static(keywords, project_path):
    context = ""
    for kw in keywords:
        try:
            kw_clean = kw.replace('"', '\\"')
            cmd = f'grep -rn "{kw_clean}" "{project_path}" | grep -vE "node_modules|dist|build|\\.test\\." | head -n 8'
            out = subprocess.check_output(cmd, shell=True, text=True, stderr=subprocess.STDOUT)
            if out:
                context += f"\n--- Matches for {kw_clean} ---\n{out}\n"
        except:
            pass
    return context

def analyze_issue_single_turn(issue, url, project_path):
    title = issue.get('title', '')
    body = issue.get('body', '')[:1500]
    
    keywords = extract_keywords(title + " " + body)
    code_context = search_codebase_static(keywords, project_path)

    prompt = f"""You are a senior software engineer analyzing issues. 
Based on the description and codebase search context, pinpoint exactly which files and logic are responsible. 
DO NOT GUESS. If the context isn't enough, provide your best technical hypothesis.

Rating Effort Level:
- small (1 day): Localized fix (1-2 files), clear cause.
- medium (2-3 days): Touches multiple components or hard to trace.
- large (>3 days): Architectural issues, Windows/WSL-specific, core protocols.

Issue Title: {title}
Issue Body: {body}

Codebase Search Context:
{code_context[:8000]}

Output ONLY valid JSON (no markdown block):
{{
  "analysis": "technical analysis of root cause and fix",
  "effort_level": "small|medium|large",
  "reasoning": "justification with specific files/lines found"
}}
"""
    data = {
        "contents": [{"role": "user", "parts": [{"text": prompt}]}],
        "generationConfig": {"temperature": 0.1}
    }
    
    try:
        req = urllib.request.Request(url, data=json.dumps(data).encode('utf-8'), headers={'Content-Type': 'application/json'})
        with urllib.request.urlopen(req, timeout=60) as response:
            res = json.loads(response.read().decode('utf-8'))
            txt = res['candidates'][0]['content']['parts'][0]['text']
            txt = txt.replace('```json', '').replace('```', '').strip()
            return json.loads(txt)
    except Exception as e:
        return {"analysis": "Failed to analyze autonomously", "effort_level": "medium", "reasoning": str(e)}

def dynamic_validate_effort(issue, url):
    title = issue.get('title', '')
    body = issue.get('body', '')[:1000]
    analysis = issue.get('analysis', '')
    current_effort = issue.get('effort_level', 'small')
    reasoning = issue.get('reasoning', '')

    prompt = f"""You are a principal software engineer performing quality assurance on a junior engineer's effort estimation for an issue.

Your job is to read the junior's analysis and dynamically determine if the effort level ('small', 'medium', or 'large') needs to be upgraded due to hidden complexities.
Do NOT use simple keyword matching. Look for semantic meaning and architectural impact.

RULES FOR UPGRADING TO 'LARGE' (>3 days):
- Involves OS-level integrations (Windows/WSL support, process spawning, PTY, POSIX signals).
- Involves complex multi-threading, race conditions, memory leaks, or performance bottlenecks.
- Involves core architectural refactoring, custom protocols (like MCP or A2A), or network streams.
- The bug is described as intermittent, flickering, or hard to reproduce.

RULES FOR UPGRADING TO 'MEDIUM' (2-3 days):
- Involves complex UI state management (React hooks, Ink TUI lifecycle).
- Involves asynchronous control flow (Promises, async/await chaining) where failure states are complex.
- Requires modifying parsers, schemas, or complex regex.

Original Issue:
Title: {title}
Body: {body}

Junior's Analysis: {analysis}
Junior's Current Effort: {current_effort}
Junior's Reasoning: {reasoning}

Evaluate if the effort level needs to be raised. If the junior's estimate is accurate based on the rules, keep it. If it underestimates the complexity, upgrade it to medium or large.

Output ONLY valid JSON (no markdown):
{{
  "effort_level": "small|medium|large",
  "validation_msg": "Brief explanation of why you kept or upgraded the effort level based on architectural complexity."
}}
"""
    data = {
        "contents": [{"role": "user", "parts": [{"text": prompt}]}],
        "generationConfig": {"temperature": 0.1}
    }
    
    try:
        req = urllib.request.Request(url, data=json.dumps(data).encode('utf-8'), headers={'Content-Type': 'application/json'})
        with urllib.request.urlopen(req, timeout=60) as response:
            res = json.loads(response.read().decode('utf-8'))
            txt = res['candidates'][0]['content']['parts'][0]['text']
            txt = txt.replace('```json', '').replace('```', '').strip()
            parsed = json.loads(txt)
            return parsed.get('effort_level', current_effort), parsed.get('validation_msg', 'AI validation successful.')
    except Exception as e:
        return current_effort, f"Dynamic validation failed: {str(e)}"


def process_pipeline_task(args_tuple):
    issue, url, project_path, input_file, all_issues = args_tuple
    
    needs_analysis = not issue.get('analysis') or issue.get('analysis') == "Failed to analyze autonomously" or len(issue.get('analysis', '')) < 30
    
    if needs_analysis:
        print(f"[{issue.get('number', 'unknown')}] Starting Agentic Analysis...")
        result = analyze_issue_agentic(issue, url, project_path)
        
        if result.get('analysis') == "Failed to analyze autonomously":
            print(f"[{issue.get('number', 'unknown')}] Agentic failed. Falling back to Single-Turn Context Analysis...")
            result = analyze_issue_single_turn(issue, url, project_path)

        issue['analysis'] = result.get('analysis', 'Failed to analyze')
        issue['effort_level'] = result.get('effort_level', 'medium')
        issue['reasoning'] = result.get('reasoning', 'Could not determine')
        if 'recommended_implementation' in result:
            issue['recommended_implementation'] = result['recommended_implementation']
        
        with file_lock:
            with open(input_file, 'w') as f:
                json.dump(all_issues, f, indent=2)

    # Validation
    old_effort = issue.get('effort_level')
    print(f"[{issue.get('number', 'unknown')}] Starting dynamic AI validation (Current estimate: {old_effort})...")
    new_effort, validation_reason = dynamic_validate_effort(issue, url)
    issue['effort_level'] = new_effort
    
    existing_reasoning = issue.get('reasoning', '')
    existing_reasoning = existing_reasoning.split(' | Codebase validation:')[0]
    existing_reasoning = existing_reasoning.split(' | No specific files identified')[0]
    existing_reasoning = existing_reasoning.split(' | AI Validation:')[0]
    issue['reasoning'] = f"{existing_reasoning} | AI Validation: {validation_reason}".strip(' |')
    
    if needs_analysis or old_effort != new_effort:
        with file_lock:
            with open(input_file, 'w') as f:
                json.dump(all_issues, f, indent=2)
                
    print(f"[{issue.get('number', 'unknown')}] Completed -> {issue.get('effort_level')}")
    return issue

def export_csv(issues, output_csv):
    today = datetime.now().strftime("%Y-%m-%d")
    with open(output_csv, 'w', newline='', encoding='utf-8') as f:
        writer = csv.writer(f, delimiter='\t')
        writer.writerow([
            'Issue ID', 'Title', 'Status', 'Assignee', 'Labels', 
            'Last Sync', 'Link', 'analysis', 'effort_level', 
            'reasoning', 'recommended_implementation'
        ])
        
        for issue in issues:
            assignee_list = issue.get('assignees', [])
            if isinstance(assignee_list, dict) and 'nodes' in assignee_list: assignee_list = assignee_list['nodes']
            assignee = ", ".join([a.get('login', '') for a in assignee_list])
            
            labels_list = issue.get('labels', [])
            if isinstance(labels_list, dict) and 'nodes' in labels_list: labels_list = labels_list['nodes']
            labels = ", ".join([l.get('name', '') for l in labels_list])
            
            writer.writerow([
                issue.get('number'),
                issue.get('title', ''),
                issue.get('state', 'OPEN'),
                assignee,
                labels,
                today,
                issue.get('url', ''),
                issue.get('analysis', ''),
                issue.get('effort_level', ''),
                issue.get('reasoning', ''),
                issue.get('recommended_implementation', '')
            ])
    print(f"Exported successfully to {output_csv}")

def main():
    parser = argparse.ArgumentParser(description="Unified Effort Analysis Pipeline.")
    parser.add_argument("--api-key", required=True, help="Gemini API Key")
    parser.add_argument("--input", default="data/bugs.json", help="Input JSON file")
    parser.add_argument("--project", default="../../packages", help="Project root to analyze")
    parser.add_argument("--workers", type=int, default=4, help="Number of concurrent workers")
    args = parser.parse_args()

    url = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL}:generateContent?key={args.api_key}"

    with open(args.input, 'r') as f:
        issues = json.load(f)

    print(f"Starting unified analysis pipeline on {len(issues)} issues...")
    
    tasks = [(issue, url, args.project, args.input, issues) for issue in issues]
    with concurrent.futures.ThreadPoolExecutor(max_workers=args.workers) as executor:
        list(executor.map(process_pipeline_task, tasks))
        
    output_csv = args.input.replace('.json', '.csv')
    export_csv(issues, output_csv)
    print("Pipeline fully complete!")

if __name__ == '__main__':
    main()