import gradio as gr
import google.generativeai as genai
from huggingface_hub import HfApi, create_repo, upload_folder, space_info
import os
import time
import json
from datetime import datetime
import tempfile
import shutil
import re
import requests

# Load Prompts from JSON
PROMPTS = {}
try:
    with open("prompts.json", "r") as f:
        PROMPTS = json.load(f)
    print("✅ Loaded prompts from prompts.json")
except Exception as e:
    print(f"❌ Error loading prompts.json: {e}")
    # Define minimal fallback prompts if file load fails to prevent crash
    PROMPTS = {
        "architect": "You are the Architect Agent.",
        "developer": "You are the Developer Agent. Ensure you include a get_performance_metrics function with api_name='get_metrics'.",
        "debugger": "You are the Debugger Agent.",
        "tester": "You are the Tester Agent."
    }

class MetricCollector:
    """Collects real metrics from deployed Gradio apps"""
    
    def __init__(self, space_name: str):
        self.space_name = space_name
        self.base_url = f"https://{space_name.replace('/', '-')}.hf.space"
        
    def wait_for_space_ready(self, timeout: int = 300) -> bool:
        """Wait for space to be accessible and responding"""
        start_time = time.time()
        
        print(f"🔍 Waiting for space at {self.base_url}")
        
        while time.time() - start_time < timeout:
            try:
                response = requests.get(self.base_url, timeout=10)
                if response.status_code == 200:
                    print("✓ Space is responding to HTTP requests")
                    
                    # Verify the Gradio API is actually available
                    try:
                        info_response = requests.get(f"{self.base_url}/info", timeout=10)
                        if info_response.status_code == 200:
                            print("✓ Gradio API is available")
                            # Give it extra time to fully initialize
                            time.sleep(30)
                            return True
                    except:
                        print("⏳ Gradio API not ready yet...")
                        
            except Exception as e:
                elapsed = int(time.time() - start_time)
                print(f"⏳ Still waiting... ({elapsed}s) - {str(e)[:50]}")
            
            time.sleep(15)
        
        print(f"❌ Timeout after {timeout}s")
        return False
    
    def test_app_functionality(self) -> bool:
        """Test if the app is actually functional by trying to use it"""
        print("🧪 Testing app functionality...")
        
        try:
            # Get available endpoints
            info_response = requests.get(f"{self.base_url}/info", timeout=10)
            if info_response.status_code != 200:
                print("❌ Cannot fetch app info")
                return False
            
            info = info_response.json()
            endpoints = info.get('named_endpoints', {})
            
            if not endpoints:
                print("❌ No endpoints found")
                return False
            
            print(f"✓ Found {len(endpoints)} endpoints")
            
            # Try to call at least one endpoint to verify app works
            for endpoint_path in endpoints.keys():
                endpoint_name = endpoint_path.lstrip('/')
                print(f"🧪 Testing endpoint: {endpoint_name}")
                
                try:
                    test_result = self.call_gradio_api(endpoint_name, [], timeout=30)
                    if test_result is not None:
                        print(f"✓ Endpoint {endpoint_name} is functional")
                        return True
                except Exception as e:
                    print(f"⚠️ Endpoint {endpoint_name} failed: {str(e)[:100]}")
                    continue
            
            print("❌ No functional endpoints found")
            return False
            
        except Exception as e:
            print(f"❌ Functionality test failed: {str(e)}")
            return False
    
    def call_gradio_api(self, endpoint: str, data: list = None, timeout: int = 60):
        """Call a Gradio API endpoint with better error handling"""
        try:
            if data is None:
                data = []
                
            api_url = f"{self.base_url}/api/{endpoint}"
            print(f"📡 Calling API: {api_url}")
            
            response = requests.post(
                api_url,
                json={"data": data},
                timeout=timeout,
                headers={"Content-Type": "application/json"}
            )
            
            print(f"📡 Response status: {response.status_code}")
            
            if response.status_code == 200:
                result = response.json()
                data_result = result.get("data", [])
                print(f"✓ API call successful, got {len(data_result)} data items")
                return data_result
            else:
                print(f"⚠️ API call failed: {response.status_code}")
                print(f"Response: {response.text[:200]}")
                return None
                
        except requests.exceptions.Timeout:
            print(f"⚠️ API call timed out after {timeout}s")
            return None
        except Exception as e:
            print(f"⚠️ Error calling API: {str(e)}")
            return None
    
    def extract_metrics_from_app(self, max_retries: int = 3):
        """Extract real performance metrics from the deployed app with retries"""
        
        for attempt in range(max_retries):
            print(f"\n🎯 Metrics collection attempt {attempt + 1}/{max_retries}")
            
            metrics = {
                "throughput": 0,
                "latency_avg": 0,
                "latency_p50": 0,
                "latency_p95": 0,
                "latency_p99": 0,
                "cpu_efficiency": 0,
                "memory_usage": 0,
                "performance_score": 0,
                "source": "failed"
            }
            
            # Wait for space to be ready
            if not self.wait_for_space_ready(timeout=300):
                print(f"⚠️ Attempt {attempt + 1}: Space not ready")
                if attempt < max_retries - 1:
                    print("⏳ Waiting 30s before retry...")
                    time.sleep(30)
                continue
            
            # Test if app is functional
            if not self.test_app_functionality():
                print(f"⚠️ Attempt {attempt + 1}: App not functional")
                if attempt < max_retries - 1:
                    print("⏳ Waiting 30s before retry...")
                    time.sleep(30)
                continue
            
            try:
                # Try to get the app's info endpoint
                info_response = requests.get(f"{self.base_url}/info", timeout=10)
                
                if info_response.status_code == 200:
                    info = info_response.json()
                    endpoints = info.get('named_endpoints', {})
                    print(f"✓ Found {len(endpoints)} named endpoints: {list(endpoints.keys())}")
                    
                    # Priority 1: Look for the get_metrics endpoint
                    if '/get_metrics' in endpoints:
                        print("🎯 Found get_metrics endpoint!")
                        result = self.call_gradio_api("get_metrics", [], timeout=60)
                        
                        if result:
                            print(f"📊 Raw result from get_metrics: {str(result)[:200]}")
                            metrics_data = self.parse_metrics_from_result(result)
                            if metrics_data.get("performance_score", 0) > 0:
                                metrics.update(metrics_data)
                                metrics["source"] = "real_api"
                                print(f"✅ SUCCESS! Collected real metrics: Score {metrics['performance_score']}/100")
                                return metrics
                            else:
                                print("⚠️ Metrics parsed but score is 0")
                        else:
                            print("⚠️ get_metrics returned None")
                    else:
                        print("⚠️ 'get_metrics' endpoint NOT found in named_endpoints!")
                    
                    # Priority 2: Try common metric endpoint patterns
                    metric_keywords = ['metric', 'benchmark', 'performance', 'test', 'stats']
                    for endpoint_path, endpoint_info in endpoints.items():
                        endpoint_name = endpoint_path.lstrip('/')
                        if any(keyword in endpoint_name.lower() for keyword in metric_keywords):
                            print(f"🧪 Trying endpoint: {endpoint_name}")
                            result = self.call_gradio_api(endpoint_name, [], timeout=60)
                            if result:
                                print(f"📊 Raw result from {endpoint_name}: {str(result)[:200]}")
                                metrics_data = self.parse_metrics_from_result(result)
                                if metrics_data.get("performance_score", 0) > 0:
                                    metrics.update(metrics_data)
                                    metrics["source"] = "real_api"
                                    print(f"✅ SUCCESS! Collected metrics from {endpoint_name}")
                                    return metrics
                    
                    # Priority 3: Try ALL endpoints as a last resort
                    print("🔍 Trying all available endpoints...")
                    for endpoint_path in endpoints.keys():
                        endpoint_name = endpoint_path.lstrip('/')
                        print(f"🧪 Trying endpoint: {endpoint_name}")
                        try:
                            result = self.call_gradio_api(endpoint_name, [], timeout=30)
                            if result:
                                metrics_data = self.parse_metrics_from_result(result)
                                if metrics_data.get("performance_score", 0) > 0:
                                    metrics.update(metrics_data)
                                    metrics["source"] = "real_api"
                                    print(f"✅ SUCCESS! Collected metrics from {endpoint_name}")
                                    return metrics
                        except Exception as e:
                            print(f"⚠️ Endpoint {endpoint_name} failed: {str(e)[:50]}")
                            continue
            
            except Exception as e:
                print(f"⚠️ Attempt {attempt + 1} error: {str(e)}")
            
            if attempt < max_retries - 1:
                print("⏳ Waiting 30s before retry...")
                time.sleep(30)
        
        # All attempts failed
        print("❌ Failed to collect real metrics after all attempts")
        metrics["source"] = "placeholder"
        metrics["performance_score"] = 50  # Default baseline
        return metrics
    
    def parse_metrics_from_result(self, result):
        """Parse metrics from Gradio API result with improved handling"""
        metrics = {}
        
        print(f"🔍 Parsing result type: {type(result)}")
        
        # Result could be a list, dict, or string
        if isinstance(result, list) and len(result) > 0:
            item = result[0]
            print(f"🔍 List item type: {type(item)}")
            
            if isinstance(item, str):
                print(f"🔍 String content: {item[:200]}")
                # Try to parse as JSON
                try:
                    parsed = json.loads(item)
                    print(f"✓ Parsed as JSON: {parsed}")
                    metrics.update(self.extract_metrics_from_dict(parsed))
                except:
                    print("⚠️ Not valid JSON, trying regex")
                    # Try regex parsing
                    metrics.update(self.extract_metrics_from_text(item))
            elif isinstance(item, dict):
                print(f"✓ Direct dict: {item}")
                metrics.update(self.extract_metrics_from_dict(item))
        
        elif isinstance(result, dict):
            print(f"✓ Direct dict result: {result}")
            metrics.update(self.extract_metrics_from_dict(result))
        
        elif isinstance(result, str):
            print(f"🔍 String result: {result[:200]}")
            try:
                parsed = json.loads(result)
                metrics.update(self.extract_metrics_from_dict(parsed))
            except:
                metrics.update(self.extract_metrics_from_text(result))
        
        print(f"📊 Extracted metrics: {metrics}")
        return metrics
    
    def extract_metrics_from_dict(self, data: dict):
        """Extract metrics from dictionary data"""
        metrics = {}
        
        # Common metric keys
        key_mappings = {
            'throughput': ['throughput', 'packets_per_sec', 'pps', 'packets_sec'],
            'latency_avg': ['latency_avg', 'avg_latency', 'latency_mean', 'mean_latency'],
            'latency_p50': ['latency_p50', 'p50', 'median_latency', 'latency_median'],
            'latency_p95': ['latency_p95', 'p95', 'latency_95'],
            'latency_p99': ['latency_p99', 'p99', 'latency_99'],
            'cpu_efficiency': ['cpu_efficiency', 'efficiency', 'ops_per_packet', 'cpu_ops'],
            'memory_usage': ['memory_usage', 'memory_mb', 'memory', 'mem_usage'],
            'performance_score': ['performance_score', 'score', 'total_score', 'overall_score']
        }
        
        for metric_key, possible_keys in key_mappings.items():
            for key in possible_keys:
                if key in data:
                    try:
                        value = float(data[key])
                        metrics[metric_key] = value
                        print(f"✓ Found {metric_key} = {value}")
                        break
                    except (ValueError, TypeError):
                        pass
        
        return metrics
    
    def extract_metrics_from_text(self, text: str):
        """Extract metrics from text output using regex"""
        metrics = {}
        
        patterns = {
            'throughput': r'throughput[:\s]+([0-9,.]+)',
            'latency_avg': r'(?:avg|average|mean)\s*latency[:\s]+([0-9.]+)',
            'latency_p50': r'p50[:\s]+([0-9.]+)',
            'latency_p95': r'p95[:\s]+([0-9.]+)',
            'latency_p99': r'p99[:\s]+([0-9.]+)',
            'cpu_efficiency': r'(?:cpu[_\s]*)?efficiency[:\s]+([0-9.]+)',
            'performance_score': r'(?:performance[_\s]+)?score[:\s]+([0-9]+)'
        }
        
        for key, pattern in patterns.items():
            match = re.search(pattern, text, re.IGNORECASE)
            if match:
                try:
                    value = match.group(1).replace(',', '')
                    metrics[key] = float(value)
                    print(f"✓ Regex found {key} = {value}")
                except:
                    pass
        
        return metrics


class MultiAgentSystem:
    def __init__(self, gemini_api_key, hf_token):
        self.hf_api = HfApi(token=hf_token)
        self.hf_token = hf_token
        genai.configure(api_key=gemini_api_key)
        
        # Initialize four specialized agents with prompts loaded from JSON
        self.architect = genai.GenerativeModel(
            model_name='gemini-2.5-flash',
            system_instruction=PROMPTS.get("architect", "You are the Architect Agent.")
        )
        self.developer = genai.GenerativeModel(
            model_name='gemini-2.5-flash',
            system_instruction=PROMPTS.get("developer", "You are the Developer Agent.")
        )
        self.debugger = genai.GenerativeModel(
            model_name='gemini-2.5-flash',
            system_instruction=PROMPTS.get("debugger", "You are the Debugger Agent.")
        )
        self.tester = genai.GenerativeModel(
            model_name='gemini-2.5-flash',
            system_instruction=PROMPTS.get("tester", "You are the Tester Agent.")
        )
        
        self.space_name = None
        self.iteration = 0
        self.shared_context = {
            "current_code": "",
            "performance_history": [],
            "architecture_decisions": [],
            "known_issues": [],
            "optimization_log": []
        }
        self.temp_dir = tempfile.mkdtemp()
        self.conversation_logs = []
        self.target_improvement = 0
        
    def log_conversation(self, agent_name, input_msg, output_msg):
        """Log inter-agent communication"""
        self.conversation_logs.append({
            "timestamp": datetime.now().isoformat(),
            "agent": agent_name,
            "input": input_msg[:1000],
            "output": output_msg[:2000]
        })

    def create_space(self):
        """Create a new HuggingFace Space"""
        try:
            timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
            # Create a simplified space name
            name_suffix = f"optimizer-{timestamp}"
            
            user_info = self.hf_api.whoami()
            username = user_info['name']
            
            # Full repo ID
            repo_id = f"{username}/packet-{name_suffix}"
            
            print(f"Creating Space: {repo_id}")
            create_repo(
                repo_id=repo_id,
                repo_type="space",
                space_sdk="gradio",
                token=self.hf_token,
                private=False
            )
            
            self.space_name = repo_id
            return f"✓ Created Space: {repo_id}"
        except Exception as e:
            return f"✗ Error creating space: {str(e)}"
    
    def upload_files_to_space(self, files):
        """Upload files to the created space"""
        if not self.space_name:
            return "✗ No space created"
            
        try:
            # Create temp directory for upload
            upload_dir = os.path.join(self.temp_dir, "upload")
            if os.path.exists(upload_dir):
                shutil.rmtree(upload_dir)
            os.makedirs(upload_dir)
            
            # Write files
            for filename, content in files.items():
                with open(os.path.join(upload_dir, filename), "w") as f:
                    f.write(content)
            
            # Upload
            print(f"Uploading to {self.space_name}...")
            self.hf_api.upload_folder(
                folder_path=upload_dir,
                repo_id=self.space_name,
                repo_type="space",
                token=self.hf_token
            )
            
            return f"✓ Deployed to {self.space_name}"
        except Exception as e:
            return f"✗ Error uploading: {str(e)}"

    def get_space_logs(self):
        """Get logs from the space (best effort)"""
        if not self.space_name:
            return {"error": "No space"}
            
        try:
            info = space_info(self.space_name, token=self.hf_token)
            runtime = info.runtime
            
            return {
                "stage": runtime.stage if runtime else "UNKNOWN",
                "error": getattr(runtime, "error", None),
                "message": "Check HF Space logs for details" 
            }
        except Exception as e:
            return {"error": str(e)}
            
    def validate_code_locally(self, code):
        """Validate code syntax and required endpoints locally before deploying"""
        try:
            if not code or len(code.strip()) == 0:
                return False, "Code is empty"
            
            # Check for API endpoint requirement
            if 'api_name="get_metrics"' not in code and "api_name='get_metrics'" not in code:
                return False, "Code is missing the required api_name=\"get_metrics\" argument in the Gradio button."
                
            # Check for placeholders that cause syntax errors
            if "..." in code or "pass  # Implement" in code:
                 return False, "Code contains placeholders (...) or incomplete blocks which will cause runtime errors."
            
            # Check for valid syntax
            compile(code, '<string>', 'exec')
            return True, "Valid Python Syntax"
        except Exception as e:
            return False, f"Syntax Error: {str(e)}"
    
    def collect_real_metrics(self):
        """Collect real metrics from deployed space with better validation"""
        if not self.space_name:
            print("⚠️ No space deployed yet")
            return {
                "throughput": 0,
                "latency_avg": 0,
                "performance_score": 0,
                "source": "no_space"
            }
        
        print(f"\n{'='*80}")
        print(f"🔍 COLLECTING REAL METRICS FROM: {self.space_name}")
        print(f"{'='*80}\n")
        
        collector = MetricCollector(self.space_name)
        metrics = collector.extract_metrics_from_app(max_retries=3)
        
        print(f"\n{'='*80}")
        print("📊 FINAL COLLECTED METRICS:")
        print(f"  Source: {metrics.get('source', 'unknown')}")
        print(f"  Throughput: {metrics.get('throughput', 0):,.0f} packets/sec")
        print(f"  Avg Latency: {metrics.get('latency_avg', 0):.2f} ms")
        print(f"  P95 Latency: {metrics.get('latency_p95', 0):.2f} ms")
        print(f"  Performance Score: {metrics.get('performance_score', 0)}/100")
        print(f"{'='*80}\n")
        
        # If we got placeholder metrics, try one more time after a longer wait
        if metrics.get("source") == "placeholder":
            print("⚠️ Got placeholder metrics, trying once more after 60s wait...")
            time.sleep(60)
            metrics = collector.extract_metrics_from_app(max_retries=1)
        
        return metrics
    
    def wait_for_space_build(self, timeout=300):
        """Wait for space to build and verify it's functional"""
        if not self.space_name:
            return False, "No space created"
        
        start_time = time.time()
        print(f"\n⏳ Waiting for space to build (timeout: {timeout}s)...")
        
        while time.time() - start_time < timeout:
            try:
                info = space_info(self.space_name, token=self.hf_token)
                runtime = info.runtime if hasattr(info, 'runtime') else None
                
                if runtime and hasattr(runtime, 'stage'):
                    stage = runtime.stage
                    elapsed = int(time.time() - start_time)
                    print(f"  Stage: {stage} ({elapsed}s elapsed)")
                    
                    if stage == "RUNNING":
                        print("✓ Space is RUNNING!")
                        
                        # Additional verification: test if app is actually functional
                        print("🧪 Verifying app functionality...")
                        time.sleep(15)  # Give it time to fully start
                        
                        collector = MetricCollector(self.space_name)
                        if collector.test_app_functionality():
                            print("✅ Space is running AND functional!")
                            return True, "Space is running and functional"
                        else:
                            print("⚠️ Space is running but app may not be functional yet, waiting...")
                            time.sleep(20)
                            continue
                            
                    elif stage in ["RUNTIME_ERROR", "BUILD_ERROR"]:
                        error_msg = getattr(runtime, 'error', 'Unknown error')
                        return False, f"Space error: {stage} - {error_msg}"
                    
                time.sleep(15)
            except Exception as e:
                print(f"  Error checking status: {str(e)[:50]}")
                time.sleep(15)
                continue
        
        return False, f"Timeout after {timeout}s"
    
    def automatic_error_fix_cycle(self, error_info):
        """Automatically fix errors detected in deployed space"""
        log = "\n🔧 AUTOMATIC ERROR DETECTION AND FIX\n" + "="*80 + "\n"
        log += f"Error detected: {error_info}\n\n"
        
        log += "🔍 PHASE 1: DEBUGGER ANALYZING ERROR...\n"
        debugger_prompt = f"""The deployed space has encountered an error:

ERROR INFO:
{json.dumps(error_info, indent=2)}

CURRENT CODE:
```python
{self.shared_context['current_code']}
```

Analyze:
1. What is causing the error
2. Root cause analysis
3. Specific fixes needed

Provide detailed debugging information."""

        try:
            debugger_response = self.debugger.generate_content(debugger_prompt)
            debugger_output = debugger_response.text
            self.log_conversation("DEBUGGER (ERROR)", debugger_prompt, debugger_output)
            log += "✓ Error analysis complete\n\n"
        except Exception as e:
            return log + f"✗ Error during analysis: {str(e)}\n", None, None
        
        log += "💻 PHASE 2: DEVELOPER FIXING ERROR...\n"
        fix_prompt = f"""DEBUGGER'S ERROR ANALYSIS:
{debugger_output}

ERROR INFO:
{json.dumps(error_info, indent=2)}

CURRENT CODE:
```python
{self.shared_context['current_code']}
```

Fix the error and provide corrected code. Ensure:
1. The specific error is resolved
2. Code is syntactically correct
3. All imports are included
4. The app will run without errors
5. The get_performance_metrics() function with api_name="get_metrics" is included
6. CRITICAL: DO NOT use placeholders like '...'"""

        try:
            fix_response = self.developer.generate_content(fix_prompt)
            fix_output = fix_response.text
            self.log_conversation("DEVELOPER (ERROR FIX)", fix_prompt, fix_output)
            
            fixed_code = self.extract_code(fix_output, "APP_PY")
            requirements = self.extract_code(fix_output, "REQUIREMENTS")
            
            # Local Validation Loop
            attempts = 0
            while attempts < 3:
                is_valid, error_msg = self.validate_code_locally(fixed_code)
                if is_valid:
                    break
                    
                attempts += 1
                print(f"⚠️ Fixed code still has syntax errors (Attempt {attempts}/3): {error_msg}")
                
                refix_prompt = f"""The fixed code still has a SYNTAX ERROR:
                
ERROR: {error_msg}

You likely used placeholders like '...' or left incomplete blocks.
REWRITE the COMPLETE code fixing this error. Do NOT use placeholders."""
                
                fix_response = self.developer.generate_content(refix_prompt)
                fix_output = fix_response.text
                self.log_conversation(f"DEVELOPER (RE-FIX {attempts})", refix_prompt, fix_output)
                fixed_code = self.extract_code(fix_output, "APP_PY")

            if not fixed_code:
                fixed_code = self.shared_context['current_code']
            if not requirements:
                requirements = "gradio==4.44.0\nnumpy>=1.24.0"
            
            self.shared_context['current_code'] = fixed_code
            log += "✓ Error fix implemented\n\n"
            
            return log, fixed_code, requirements
        except Exception as e:
            return log + f"✗ Error during fix: {str(e)}\n", None, None
    
    def extract_metrics_from_output(self, text):
        """Extract performance metrics from agent outputs (fallback)"""
        metrics = {
            "throughput": 0,
            "latency_avg": 0,
            "latency_p95": 0,
            "cpu_efficiency": 0,
            "performance_score": 0
        }
        
        throughput_match = re.search(r'Throughput[:\s]+([0-9,.]+)', text, re.IGNORECASE)
        if throughput_match:
            try:
                metrics["throughput"] = float(throughput_match.group(1).replace(',', ''))
            except:
                pass
        
        latency_match = re.search(r'Avg\s+Latency[:\s]+([0-9.]+)', text, re.IGNORECASE)
        if latency_match:
            try:
                metrics["latency_avg"] = float(latency_match.group(1))
            except:
                pass
        
        p95_match = re.search(r'P95\s+Latency[:\s]+([0-9.]+)', text, re.IGNORECASE)
        if p95_match:
            try:
                metrics["latency_p95"] = float(p95_match.group(1))
            except:
                pass
        
        score_match = re.search(r'PERFORMANCE[_\s]+SCORE[:\s]+([0-9]+)', text, re.IGNORECASE)
        if score_match:
            try:
                metrics["performance_score"] = int(score_match.group(1))
            except:
                pass
        
        return metrics
    
    def initial_design_phase(self):
        """Phase 1: Architect designs initial system"""
        self.iteration += 1
        
        architect_prompt = """Design the initial packet simulation algorithm system optimized for high performance.

Consider:
- Multiple algorithm approaches (basic, batched, optimized)
- Performance metrics to track (throughput, latency, efficiency)
- Benchmarking capabilities built into the UI
- Clear performance visualizations

CRITICAL: The app MUST include a get_performance_metrics() function that returns JSON metrics.

Focus on creating a baseline that we can iteratively improve.
Target initial performance score: 60/100"""

        try:
            architect_response = self.architect.generate_content(architect_prompt)
            architect_output = architect_response.text
            self.log_conversation("ARCHITECT", architect_prompt, architect_output)
            
            self.shared_context["architecture_decisions"].append({
                "iteration": self.iteration,
                "design": architect_output,
                "timestamp": datetime.now().isoformat()
            })
            
            return architect_output
        except Exception as e:
            return f"✗ Architect error: {str(e)}"
    
    def development_phase(self, architect_specs, previous_metrics=None):
        """Phase 2: Developer implements with performance focus"""
        
        context = f"""ARCHITECT'S SPECIFICATIONS:
{architect_specs}

PERFORMANCE CONTEXT:
"""
        if previous_metrics:
            context += f"Previous iteration metrics: {json.dumps(previous_metrics, indent=2)}\n"
            context += "Your goal is to exceed these metrics.\n"
        else:
            context += "This is the initial implementation. Focus on creating a solid baseline with good instrumentation.\n"
        
        developer_prompt = f"""{context}

Implement a complete Gradio application with:
1. Multiple packet simulation algorithms (at least 3)
2. A get_performance_metrics() function with api_name="get_metrics" that returns JSON
3. Built-in benchmarking tools
4. Real-time performance visualization
5. Detailed timing measurements

CRITICAL: Include the get_performance_metrics() function exactly as specified in your system prompt.
CRITICAL: Write the FULL, COMPLETE code. Do not use placeholders like '...' or 'pass' for incomplete blocks.

Remember: Simulate packets safely (NO real network operations)"""

        try:
            developer_response = self.developer.generate_content(developer_prompt)
            developer_output = developer_response.text
            self.log_conversation("DEVELOPER", developer_prompt, developer_output)
            
            code = self.extract_code(developer_output, "APP_PY")
            requirements = self.extract_code(developer_output, "REQUIREMENTS")
            
            # Local Validation Loop
            attempts = 0
            while attempts < 3:
                is_valid, error_msg = self.validate_code_locally(code)
                if is_valid:
                    break
                
                attempts += 1
                print(f"⚠️ Generated code has syntax errors (Attempt {attempts}/3): {error_msg}")
                
                fix_syntax_prompt = f"""The code you wrote has a SYNTAX ERROR and cannot run.
                
ERROR: {error_msg}

You likely used placeholders like '...' or left incomplete blocks.
REWRITE the COMPLETE code fixing this error. Do NOT use placeholders."""

                developer_response = self.developer.generate_content(fix_syntax_prompt)
                developer_output = developer_response.text
                self.log_conversation(f"DEVELOPER (SYNTAX FIX {attempts})", fix_syntax_prompt, developer_output)
                
                code = self.extract_code(developer_output, "APP_PY")
                # requirements usually stay same, but could re-extract if needed
            
            if not requirements:
                requirements = "gradio==4.44.0\nnumpy>=1.24.0"
            
            self.shared_context["current_code"] = code
            
            return developer_output, code, requirements
        except Exception as e:
            return f"✗ Developer error: {str(e)}", "", ""
    
    def testing_phase(self, code):
        """Phase 2.5: Tester runs benchmarks and collects metrics"""
        
        tester_prompt = f"""Analyze this packet simulation code and generate a comprehensive test plan:

CODE:
```python
{code}
```

Generate:
1. Specific test scenarios to run
2. Expected performance characteristics
3. Metrics to collect
4. Benchmark methodology

Provide hypothetical but realistic performance numbers based on the algorithm's design."""

        try:
            tester_response = self.tester.generate_content(tester_prompt)
            tester_output = tester_response.text
            self.log_conversation("TESTER", tester_prompt, tester_output)
            
            metrics = self.extract_metrics_from_output(tester_output)
            
            return tester_output, metrics
        except Exception as e:
            return f"✗ Tester error: {str(e)}", {}
    
    def debugging_phase(self, code, architect_specs, test_metrics, real_metrics=None):
        """Phase 3: Debugger validates and measures REAL performance"""
        
        # Use real metrics if available, otherwise use test estimates
        metrics_to_use = real_metrics if real_metrics and real_metrics.get("source") == "real_api" else test_metrics
        
        debugger_prompt = f"""ARCHITECT'S SPECIFICATIONS:
{architect_specs}

METRICS (Source: {metrics_to_use.get('source', 'test_estimates') if isinstance(metrics_to_use, dict) else 'test_estimates'}):
{json.dumps(metrics_to_use, indent=2)}

DEVELOPER'S CODE:
```python
{code}
```

Validate:
1. Code correctness and safety
2. Performance against specifications
3. Whether optimizations are effective
4. Any bugs or issues

Provide a performance score (0-100) and recommendations."""

        try:
            debugger_response = self.debugger.generate_content(debugger_prompt)
            debugger_output = debugger_response.text
            self.log_conversation("DEBUGGER", debugger_prompt, debugger_output)
            
            # Extract any additional metrics from debugger
            debugger_metrics = self.extract_metrics_from_output(debugger_output)
            
            # Merge metrics: real > debugger > test
            final_metrics = {**test_metrics}
            if debugger_metrics.get("performance_score", 0) > 0:
                final_metrics.update(debugger_metrics)
            if real_metrics and real_metrics.get("source") == "real_api":
                final_metrics.update(real_metrics)
            
            # Update performance history with REAL metrics if available
            self.shared_context["performance_history"].append({
                "iteration": self.iteration,
                "metrics": final_metrics,
                "real_metrics": real_metrics if real_metrics else {},
                "timestamp": datetime.now().isoformat()
            })
            
            ready = "READY_FOR_DEPLOYMENT: YES" in debugger_output or final_metrics.get("performance_score", 0) >= 70
            
            return debugger_output, ready, final_metrics
        except Exception as e:
            return f"✗ Debugger error: {str(e)}", False, {}
    
    def fix_phase(self, code, debugger_findings):
        """Phase 4: Developer fixes issues"""
        
        fix_prompt = f"""DEBUGGER'S FINDINGS:
{debugger_findings}

CURRENT CODE:
```python
{code}
```

Fix all identified issues while maintaining or improving performance.
Ensure the get_performance_metrics() function with api_name="get_metrics" is present.
CRITICAL: DO NOT use placeholders like '...'. Write the FULL code."""

        try:
            fix_response = self.developer.generate_content(fix_prompt)
            fix_output = fix_response.text
            self.log_conversation("DEVELOPER (FIX)", fix_prompt, fix_output)
            
            fixed_code = self.extract_code(fix_output, "APP_PY")
            
            # Local Validation Loop
            attempts = 0
            while attempts < 3:
                is_valid, error_msg = self.validate_code_locally(fixed_code)
                if is_valid:
                    break
                
                attempts += 1
                print(f"⚠️ Fixed code still has syntax errors (Attempt {attempts}/3): {error_msg}")
                
                refix_prompt = f"""The fixed code still has a SYNTAX ERROR:
                
ERROR: {error_msg}

You likely used placeholders like '...' or left incomplete blocks.
REWRITE the COMPLETE code fixing this error. Do NOT use placeholders."""
                
                fix_response = self.developer.generate_content(refix_prompt)
                fix_output = fix_response.text
                self.log_conversation(f"DEVELOPER (RE-FIX {attempts})", refix_prompt, fix_output)
                fixed_code = self.extract_code(fix_output, "APP_PY")

            if not fixed_code:
                fixed_code = code
            
            self.shared_context["current_code"] = fixed_code
            
            return fix_output, fixed_code
        except Exception as e:
            return f"✗ Developer fix error: {str(e)}", code
    
    def autonomous_improvement_cycle(self):
        """Fully autonomous improvement cycle based on REAL performance metrics"""
        self.iteration += 1
        
        # Get previous performance metrics
        prev_metrics = self.shared_context["performance_history"][-1] if self.shared_context["performance_history"] else {}
        prev_score = prev_metrics.get("metrics", {}).get("performance_score", 0)
        
        log = f"\n🚀 AUTONOMOUS IMPROVEMENT CYCLE {self.iteration}\n{'='*80}\n"
        log += f"Previous Performance Score: {prev_score}/100\n"
        log += f"Target: {prev_score + 10}/100\n\n"
        
        # Phase 1: Tester analyzes current performance
        log += "🧪 PHASE 1: TESTER ANALYZING CURRENT PERFORMANCE...\n"
        tester_prompt = f"""Analyze the current system performance:

CURRENT CODE:
```python
{self.shared_context['current_code']}
```

PERFORMANCE HISTORY:
{json.dumps(self.shared_context['performance_history'][-3:], indent=2)}

Identify:
1. Performance bottlenecks
2. Optimization opportunities
3. Specific metrics to improve
4. Realistic performance targets"""

        tester_output = self.tester.generate_content(tester_prompt).text
        self.log_conversation("TESTER", tester_prompt, tester_output)
        log += "✓ Performance analysis complete\n\n"
        
        # Phase 2: Architect designs optimizations
        log += "🏛️ PHASE 2: ARCHITECT DESIGNING OPTIMIZATIONS...\n"
        architect_prompt = f"""TESTER'S PERFORMANCE ANALYSIS:
{tester_output}

CURRENT PERFORMANCE METRICS:
{json.dumps(prev_metrics.get('metrics', {}), indent=2)}

OPTIMIZATION HISTORY:
{json.dumps(self.shared_context['optimization_log'][-2:], indent=2)}

Design specific optimizations to improve performance score by at least 10 points.
Focus on the biggest bottlenecks identified."""

        architect_output = self.architect.generate_content(architect_prompt).text
        self.log_conversation("ARCHITECT", architect_prompt, architect_output)
        self.shared_context["architecture_decisions"].append({
            "iteration": self.iteration,
            "design": architect_output
        })
        log += "✓ Optimization strategy complete\n\n"
        
        # Phase 3: Developer implements
        log += "💻 PHASE 3: DEVELOPER IMPLEMENTING OPTIMIZATIONS...\n"
        dev_output, code, requirements = self.development_phase(
            architect_output,
            prev_metrics.get('metrics', {})
        )
        log += "✓ Implementation complete\n\n"
        
        # Phase 4: Tester benchmarks new version (estimates)
        log += "🧪 PHASE 4: TESTER BENCHMARKING NEW VERSION...\n"
        test_output, test_metrics = self.testing_phase(code)
        log += "✓ Benchmarking complete\n\n"
        
        # Phase 5: Deploy and collect REAL metrics
        log += "🚀 PHASE 5: DEPLOYING TO COLLECT REAL METRICS...\n"
        deploy_status = self.deploy_to_space(code, requirements)
        log += f"{deploy_status}\n"
        
        log += "⏳ Waiting for space to build...\n"
        success, build_msg = self.wait_for_space_build(timeout=180)
        log += f"{build_msg}\n"
        
        # Collect real metrics
        real_metrics = None
        if success:
            log += "\n📊 COLLECTING REAL METRICS FROM DEPLOYED APP...\n"
            time.sleep(10)  # Extra time for app to stabilize
            real_metrics = self.collect_real_metrics()
            
            if real_metrics.get("source") == "real_api":
                log += f"✓ Real metrics collected successfully!\n"
                log += f"  Performance Score: {real_metrics.get('performance_score', 0)}/100\n"
            else:
                log += "⚠️ Could not collect real metrics, using estimates\n"
        else:
            log += "⚠️ Space failed to build, using test estimates\n"
        
        log += "\n"
        
        # Phase 6: Debugger validates with REAL metrics
        log += "🔍 PHASE 6: DEBUGGER VALIDATING IMPROVEMENTS...\n"
        debug_output, ready, final_metrics = self.debugging_phase(code, architect_output, test_metrics, real_metrics)
        log += "✓ Validation complete\n\n"
        
        # Phase 7: Fix if needed
        if not ready:
            log += "🔧 PHASE 7: DEVELOPER FIXING ISSUES...\n"
            fix_output, code = self.fix_phase(code, debug_output)
            log += "✓ Fixes applied\n\n"
            
            log += "🔄 RE-DEPLOYING AND RE-TESTING...\n"
            deploy_status = self.deploy_to_space(code, requirements)
            success, build_msg = self.wait_for_space_build(timeout=180)
            
            if success:
                time.sleep(10)
                real_metrics = self.collect_real_metrics()
            
            test_output_2, test_metrics_2 = self.testing_phase(code)
            debug_output_2, ready, final_metrics = self.debugging_phase(code, architect_output, test_metrics_2, real_metrics)
            log += "✓ Re-testing complete\n\n"
        
        # Log optimization results
        new_score = final_metrics.get("performance_score", 0)
        improvement = new_score - prev_score
        
        self.shared_context["optimization_log"].append({
            "iteration": self.iteration,
            "previous_score": prev_score,
            "new_score": new_score,
            "improvement": improvement,
            "optimizations": architect_output[:500],
            "real_metrics": real_metrics.get("source") == "real_api" if real_metrics else False
        })
        
        log += f"📈 RESULTS:\n"
        log += f"Previous Score: {prev_score}/100\n"
        log += f"New Score: {new_score}/100\n"
        log += f"Improvement: {'+' if improvement >= 0 else ''}{improvement} points\n"
        log += f"Metrics Source: {final_metrics.get('source', 'estimates')}\n"
        
        return log, architect_output, dev_output, debug_output, code, requirements, final_metrics
    
    def extract_code(self, text, marker):
        """Extract code blocks from agent responses"""
        try:
            if marker == "APP_PY":
                if "```python" in text:
                    start = text.find("```python") + len("```python")
                    end = text.find("```", start)
                    return text[start:end].strip()
            elif marker == "REQUIREMENTS":
                if "REQUIREMENTS:" in text:
                    start = text.find("REQUIREMENTS:") + len("REQUIREMENTS:")
                    if "```" in text[start:]:
                        start = text.find("```", start) + 3
                        end = text.find("```", start)
                        return text[start:end].strip()
            return ""
        except:
            return ""
    
    def deploy_to_space(self, code, requirements):
        """Deploy code to HuggingFace Space without overwriting README"""
        files = {
            "app.py": code,
            "requirements.txt": requirements
        }
        
        return self.upload_files_to_space(files)
    
    def get_performance_summary(self):
        """Get formatted performance history"""
        if not self.shared_context["performance_history"]:
            return "No performance data yet"
        
        summary = "📊 PERFORMANCE HISTORY\n" + "="*80 + "\n\n"
        
        for entry in self.shared_context["performance_history"]:
            metrics = entry["metrics"]
            real_metrics = entry.get("real_metrics", {})
            source = real_metrics.get("source", "estimates")
            
            summary += f"""
Iteration {entry['iteration']} - {entry['timestamp']}
{'='*80}
Metrics Source: {source}
Performance Score: {metrics.get('performance_score', 'N/A')}/100
Throughput: {metrics.get('throughput', 'N/A')} packets/sec
Avg Latency: {metrics.get('latency_avg', 'N/A')} ms
P95 Latency: {metrics.get('latency_p95', 'N/A')} ms
CPU Efficiency: {metrics.get('cpu_efficiency', 'N/A')}
{'='*80}

"""
        
        return summary
    
    def get_agent_communications(self):
        """Get formatted log of all inter-agent communications"""
        if not self.conversation_logs:
            return "No communications yet"
        
        log = "💬 AGENT COMMUNICATION LOG\n" + "="*80 + "\n\n"
        
        for entry in self.conversation_logs[-10:]:
            log += f"""
{'='*80}
⏰ {entry['timestamp']}
🤖 AGENT: {entry['agent']}

📥 INPUT:
{entry['input'][:300]}...

📤 OUTPUT:
{entry['output'][:500]}...

{'='*80}

"""
        
        return log
    
    def cleanup(self):
        """Cleanup temp directory"""
        try:
            shutil.rmtree(self.temp_dir)
        except:
            pass

# Global system instance
system = None

def initialize_system(gemini_key, hf_token):
    """Initialize the multi-agent system"""
    global system
    try:
        system = MultiAgentSystem(gemini_key, hf_token)
        return "✓ Multi-agent system initialized!\n\n🤖 Agents:\n🏛️ Architect\n💻 Developer\n🔍 Debugger\n🧪 Tester"
    except Exception as e:
        return f"✗ Error initializing: {str(e)}"

def run_initial_development():
    """Run the initial development cycle with automatic error fixing and REAL metrics"""
    if system is None:
        return "✗ Initialize system first!", "", "", "", "", "", ""
    
    space_status = system.create_space()
    if "✗" in space_status:
        return space_status, "", "", "", "", "", ""
    
    log = f"🚀 ITERATION 1: INITIAL DEVELOPMENT\n{'='*80}\n\n"
    log += f"{space_status}\n\n"
    
    log += "🏛️ PHASE 1: ARCHITECT DESIGNING SYSTEM...\n"
    architect_output = system.initial_design_phase()
    log += f"✓ Architecture complete\n\n"
    
    log += "💻 PHASE 2: DEVELOPER IMPLEMENTING...\n"
    dev_output, code, requirements = system.development_phase(architect_output)
    log += f"✓ Implementation complete\n\n"
    
    log += "🧪 PHASE 3: TESTER BENCHMARKING (ESTIMATES)...\n"
    test_output, test_metrics = system.testing_phase(code)
    log += f"✓ Benchmarking complete\n\n"
    
    log += "🚀 PHASE 4: DEPLOYING...\n"
    deploy_status = system.deploy_to_space(code, requirements)
    log += f"{deploy_status}\n\n"
    
    log += "⏳ WAITING FOR SPACE TO BUILD...\n"
    success, build_msg = system.wait_for_space_build(timeout=180)
    log += f"{build_msg}\n\n"
    
    # Auto-fix errors
    max_fix_attempts = 3
    fix_attempt = 0
    
    while not success and fix_attempt < max_fix_attempts:
        fix_attempt += 1
        log += f"\n🔧 AUTOMATIC FIX ATTEMPT {fix_attempt}/{max_fix_attempts}\n"
        
        error_info = system.get_space_logs()
        fix_log, fixed_code, fixed_req = system.automatic_error_fix_cycle(error_info)
        log += fix_log
        
        if fixed_code:
            log += "\n🚀 RE-DEPLOYING WITH FIXES...\n"
            deploy_status = system.deploy_to_space(fixed_code, fixed_req)
            log += f"{deploy_status}\n\n"
            
            log += "⏳ WAITING FOR SPACE TO BUILD...\n"
            success, build_msg = system.wait_for_space_build(timeout=180)
            log += f"{build_msg}\n\n"
            
            if success:
                code = fixed_code
                requirements = fixed_req
                break
        else:
            log += "✗ Could not generate fix\n"
            break
    
    # Collect REAL metrics from deployed app
    real_metrics = None
    if success:
        log += "\n📊 COLLECTING REAL METRICS FROM DEPLOYED APP...\n"
        time.sleep(10)  # Give app time to stabilize
        real_metrics = system.collect_real_metrics()
        
        if real_metrics.get("source") == "real_api":
            log += f"✓ Real metrics collected successfully!\n\n"
        else:
            log += "⚠️ Could not collect real metrics, using estimates\n\n"
    
    log += "🔍 PHASE 5: DEBUGGER VALIDATING WITH REAL METRICS...\n"
    debug_output, ready, metrics = system.debugging_phase(code, architect_output, test_metrics, real_metrics)
    log += f"✓ Validation complete\n\n"
    
    if not ready:
        log += "🔧 PHASE 6: DEVELOPER FIXING ISSUES...\n"
        fix_output, code = system.fix_phase(code, debug_output)
        log += f"✓ Fixes applied\n\n"
        
        log += "🔄 RE-DEPLOYING AND RE-TESTING...\n"
        deploy_status = system.deploy_to_space(code, requirements)
        success, build_msg = system.wait_for_space_build(timeout=180)
        
        if success:
            time.sleep(10)
            real_metrics = system.collect_real_metrics()
        
        test_output_2, test_metrics_2 = system.testing_phase(code)
        debug_output_2, ready, metrics = system.debugging_phase(code, architect_output, test_metrics_2, real_metrics)
        debug_output += "\n\n--- AFTER FIXES ---\n" + debug_output_2
        log += f"✓ Re-testing complete\n\n"
    
    log += f"🌐 Space URL: https://huggingface.co/spaces/{system.space_name}\n\n"
    log += f"📊 Initial Performance Score: {metrics.get('performance_score', 'N/A')}/100\n"
    log += f"📈 Metrics Source: {metrics.get('source', 'estimates')}\n"
    
    return log, architect_output, dev_output, debug_output, code, deploy_status, f"Performance Score: {metrics.get('performance_score', 0)}/100 (Source: {metrics.get('source', 'estimates')})"

def run_autonomous_improvement():
    """Run autonomous improvement cycle with REAL metrics collection"""
    if system is None:
        return "✗ Initialize system first!", "", "", "", "", "", ""
    
    if system.iteration == 0:
        return "✗ Run initial development first!", "", "", "", "", "", ""
    
    log, arch_out, dev_out, debug_out, code, req, metrics = system.autonomous_improvement_cycle()
    
    log += f"\n🌐 Space URL: https://huggingface.co/spaces/{system.space_name}\n"
    
    score_summary = f"Performance Score: {metrics.get('performance_score', 0)}/100 (Source: {metrics.get('source', 'estimates')})"
    
    return log, arch_out, dev_out, debug_out, code, "", score_summary

def get_performance_history():
    """Get performance history"""
    if system is None:
        return "No performance data yet"
    return system.get_performance_summary()

def get_communications():
    """Get inter-agent communications"""
    if system is None:
        return "No communications yet"
    return system.get_agent_communications()

# Create Gradio Interface
with gr.Blocks(title="Autonomous Multi-Agent Optimizer") as demo:
    gr.Markdown("""
    # 🤖 Autonomous Multi-Agent Performance Optimizer
    
    Watch AI agents autonomously optimize packet simulation algorithms through iterative improvements.
    
    ## The Team:
    - 🏛️ **ARCHITECT**: Analyzes metrics and designs optimizations
    - 💻 **DEVELOPER**: Implements high-performance code
    - 🔍 **DEBUGGER**: Validates performance and quality
    - 🧪 **TESTER**: Runs benchmarks and collects metrics
    
    **Fully Autonomous**: Agents optimize algorithms based on REAL performance scores from deployed apps!
    """)
    
    with gr.Tab("🚀 Setup & Initial Build"):
        gr.Markdown("### Step 1: Initialize Multi-Agent System")
        
        with gr.Row():
            gemini_api_key = gr.Textbox(
                label="Gemini API Key",
                placeholder="Enter your Google AI Studio API key",
                type="password"
            )
            hf_token = gr.Textbox(
                label="Hugging Face Token",
                placeholder="Enter your HF token (write access)",
                type="password"
            )
        
        init_btn = gr.Button("🤖 Initialize Agent Team", variant="primary", size="lg")
        init_output = gr.Textbox(label="System Status", lines=5)
        
        init_btn.click(
            initialize_system,
            inputs=[gemini_api_key, hf_token],
            outputs=init_output
        )
        
        gr.Markdown("### Step 2: Initial Development")
        gr.Markdown("Agents will autonomously design, build, deploy, and measure REAL performance metrics.")
        
        develop_btn = gr.Button("🎯 Start Autonomous Development", variant="primary", size="lg")
        
        dev_log = gr.Textbox(label="Development Log", lines=12)
        performance_display = gr.Textbox(label="Initial Performance Score", lines=2)
        
        with gr.Row():
            with gr.Column():
                architect_output = gr.Textbox(label="🏛️ Architect's Design", lines=8)
            with gr.Column():
                developer_output = gr.Textbox(label="💻 Developer's Implementation", lines=8)
        
        with gr.Row():
            with gr.Column():
                debugger_output = gr.Textbox(label="🔍 Debugger's Report", lines=8)
            with gr.Column():
                initial_code = gr.Code(label="Final Code", language="python", lines=8)
        
        deploy_status_1 = gr.Textbox(label="Deployment Status")
        
        develop_btn.click(
            run_initial_development,
            outputs=[dev_log, architect_output, developer_output, debugger_output, initial_code, deploy_status_1, performance_display]
        )
    
    with gr.Tab("🔄 Autonomous Optimization"):
        gr.Markdown("""
        ### Fully Autonomous Improvement Cycles with REAL Metrics
        
        The agents will autonomously:
        1. Tester analyzes current performance and identifies bottlenecks
        2. Architect designs optimizations to improve scores
        3. Developer implements performance improvements
        4. **Deploy and collect REAL metrics from the running app**
        5. Debugger validates improvements using REAL data
        6. Measure actual score improvement
        
        **Goal**: Continuously improve performance scores through autonomous optimization with real feedback!
        """)
        
        gr.Markdown("### Current Performance")
        current_perf = gr.Textbox(label="Current Score", lines=2, value="Run initial development first")
        performance_display.change(lambda x: x, inputs=performance_display, outputs=current_perf)
        
        optimize_btn = gr.Button("🚀 Run Autonomous Optimization Cycle", variant="primary", size="lg")
        
        optimization_log = gr.Textbox(label="Optimization Log", lines=12)
        new_perf = gr.Textbox(label="New Performance Score", lines=2)
        
        with gr.Row():
            with gr.Column():
                architect_improve = gr.Textbox(label="🏛️ Optimization Strategy", lines=8)
            with gr.Column():
                developer_improve = gr.Textbox(label="💻 Performance Improvements", lines=8)
        
        with gr.Row():
            with gr.Column():
                debugger_improve = gr.Textbox(label="🔍 Performance Validation", lines=8)
            with gr.Column():
                improved_code = gr.Code(label="Optimized Code", language="python", lines=8)
        
        deploy_status_2 = gr.Textbox(label="Deployment Status")
        
        new_perf.change(lambda x: x, inputs=new_perf, outputs=current_perf)
        
        optimize_btn.click(
            run_autonomous_improvement,
            outputs=[optimization_log, architect_improve, developer_improve, debugger_improve, improved_code, deploy_status_2, new_perf]
        )
        
        gr.Markdown("### Tip: Run Multiple Cycles")
        gr.Markdown("Keep clicking 'Run Autonomous Optimization Cycle' to watch the agents iteratively improve based on REAL performance data! Each cycle aims to improve the score by 10+ points.")
    
    with gr.Tab("📊 Performance Analytics"):
        gr.Markdown("""
        ### Performance History & Metrics
        
        View complete performance tracking across all iterations, including REAL vs estimated metrics.
        """)
        
        refresh_perf_btn = gr.Button("🔄 Refresh Performance Data")
        perf_history = gr.Textbox(label="Performance History", lines=25)
        
        refresh_perf_btn.click(
            get_performance_history,
            outputs=perf_history
        )
    
    with gr.Tab("💬 Agent Communications"):
        gr.Markdown("""
        ### Inter-Agent Communication Log
        
        See how agents communicate and share context.
        """)
        
        refresh_btn = gr.Button("🔄 Refresh Communications")
        comms_output = gr.Textbox(label="Agent Communications", lines=30)
        
        refresh_btn.click(
            get_communications,
            outputs=comms_output
        )
    
    gr.Markdown("""
    ---
    ### How Autonomous Optimization Works:
    
    **Initial Development:**
    1. Architect designs system architecture
    2. Developer implements code with metrics API endpoint
    3. Deploy to HuggingFace
    4. **Collect REAL metrics from deployed app**
    5. Debugger validates using real performance data
    
    **Optimization Cycles (Fully Autonomous with REAL metrics):**
    1. Tester analyzes current metrics and identifies bottlenecks
    2. Architect designs optimizations based on data
    3. Developer implements performance improvements
    4. Deploy new version
    5. **Collect REAL metrics from the running app**
    6. Debugger validates improvements using actual measured performance
    7. Compare real performance gains
    
    **Performance Scoring (0-100):**
    - Throughput (packets/sec)
    - Latency (average, p95, p99)
    - CPU Efficiency
    - Algorithm Complexity
    - Code Quality
    
    **Key Features:**
    - Fully autonomous optimization (no human input needed)
    - **REAL performance metrics from deployed apps**
    - Performance-driven improvements based on actual data
    - Shared context across all agents
    - Iterative score improvements
    - Complete metric tracking
    - README preservation on deploy
    """)

if __name__ == "__main__":
    demo.launch()