""" Comprehensive NovaEval Space by Noveum.ai Advanced AI Model Evaluation Platform with Real NovaEval Integration """ import asyncio import json import logging import os import sys import time import uuid from datetime import datetime from typing import Dict, List, Optional, Any import uvicorn from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException from fastapi.responses import HTMLResponse from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel import httpx # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.StreamHandler(sys.stdout), logging.FileHandler('novaeval.log') ] ) logger = logging.getLogger(__name__) app = FastAPI( title="NovaEval by Noveum.ai", description="Advanced AI Model Evaluation Platform", version="1.0.0" ) app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Pydantic Models class EvaluationRequest(BaseModel): models: List[str] dataset: str dataset_subset: Optional[str] = None metrics: List[str] num_samples: int = 10 temperature: float = 0.7 max_tokens: int = 150 top_p: float = 1.0 frequency_penalty: float = 0.0 presence_penalty: float = 0.0 class EvaluationResponse(BaseModel): evaluation_id: str status: str message: str # Global state active_evaluations: Dict[str, Dict] = {} websocket_connections: List[WebSocket] = [] # NovaEval Compatible Models (LLMs only) SUPPORTED_MODELS = { "openai": { "gpt-4o": { "name": "GPT-4o", "provider": "OpenAI", "description": "Latest and most capable GPT-4 model", "context_length": 128000, "cost_per_1k_tokens": 0.005, "capabilities": ["text", "reasoning", "analysis"] }, "gpt-4o-mini": { "name": "GPT-4o Mini", "provider": "OpenAI", "description": "Cost-effective GPT-4 model", "context_length": 128000, "cost_per_1k_tokens": 0.00015, "capabilities": ["text", "reasoning", "analysis"] }, "gpt-4-turbo": { "name": "GPT-4 Turbo", "provider": "OpenAI", "description": "High-performance GPT-4 variant", "context_length": 128000, "cost_per_1k_tokens": 0.01, "capabilities": ["text", "reasoning", "analysis"] }, "gpt-4": { "name": "GPT-4", "provider": "OpenAI", "description": "Original GPT-4 model", "context_length": 8192, "cost_per_1k_tokens": 0.03, "capabilities": ["text", "reasoning", "analysis"] }, "gpt-3.5-turbo": { "name": "GPT-3.5 Turbo", "provider": "OpenAI", "description": "Fast and efficient model", "context_length": 16385, "cost_per_1k_tokens": 0.0015, "capabilities": ["text", "conversation"] } }, "anthropic": { "claude-3-5-sonnet": { "name": "Claude 3.5 Sonnet", "provider": "Anthropic", "description": "Latest Claude model with enhanced capabilities", "context_length": 200000, "cost_per_1k_tokens": 0.003, "capabilities": ["text", "reasoning", "analysis", "coding"] }, "claude-3-opus": { "name": "Claude 3 Opus", "provider": "Anthropic", "description": "Most capable Claude 3 model", "context_length": 200000, "cost_per_1k_tokens": 0.015, "capabilities": ["text", "reasoning", "analysis", "coding"] }, "claude-3-sonnet": { "name": "Claude 3 Sonnet", "provider": "Anthropic", "description": "Balanced Claude 3 model", "context_length": 200000, "cost_per_1k_tokens": 0.003, "capabilities": ["text", "reasoning", "analysis"] }, "claude-3-haiku": { "name": "Claude 3 Haiku", "provider": "Anthropic", "description": "Fast and efficient Claude 3 model", "context_length": 200000, "cost_per_1k_tokens": 0.00025, "capabilities": ["text", "conversation"] } }, "aws_bedrock": { "amazon-titan-text": { "name": "Amazon Titan Text", "provider": "AWS Bedrock", "description": "Amazon's foundation model for text", "context_length": 8000, "cost_per_1k_tokens": 0.0008, "capabilities": ["text", "generation"] }, "cohere-command": { "name": "Cohere Command", "provider": "AWS Bedrock", "description": "Cohere's command model via Bedrock", "context_length": 4096, "cost_per_1k_tokens": 0.0015, "capabilities": ["text", "conversation"] } }, "noveum": { "noveum-gateway": { "name": "Noveum AI Gateway", "provider": "Noveum.ai", "description": "Access to Noveum's curated model collection", "context_length": "Variable", "cost_per_1k_tokens": "Variable", "capabilities": ["text", "reasoning", "analysis", "custom"] } } } # NovaEval Compatible Datasets SUPPORTED_DATASETS = { "mmlu": { "name": "MMLU", "full_name": "Massive Multitask Language Understanding", "description": "57-subject benchmark covering elementary mathematics to advanced professional topics", "type": "multiple_choice", "subsets": [ "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" ], "sample_count": 14042 }, "humaneval": { "name": "HumanEval", "full_name": "Human Eval Code Generation", "description": "Programming problems for evaluating code generation capabilities", "type": "code_generation", "subsets": ["python", "javascript", "java", "cpp"], "sample_count": 164 }, "mbpp": { "name": "MBPP", "full_name": "Mostly Basic Python Problems", "description": "Python programming problems for code generation evaluation", "type": "code_generation", "subsets": ["basic", "intermediate", "advanced"], "sample_count": 974 }, "hellaswag": { "name": "HellaSwag", "full_name": "HellaSwag Commonsense Reasoning", "description": "Commonsense reasoning about everyday situations", "type": "multiple_choice", "subsets": ["validation", "test"], "sample_count": 10042 }, "arc": { "name": "ARC", "full_name": "AI2 Reasoning Challenge", "description": "Science questions requiring reasoning", "type": "multiple_choice", "subsets": ["easy", "challenge"], "sample_count": 7787 }, "truthfulqa": { "name": "TruthfulQA", "full_name": "TruthfulQA", "description": "Questions designed to test truthfulness and avoid falsehoods", "type": "multiple_choice", "subsets": ["mc1", "mc2", "generation"], "sample_count": 817 }, "custom": { "name": "Custom Dataset", "full_name": "Upload Custom Dataset", "description": "Upload your own JSON or CSV dataset for evaluation", "type": "custom", "subsets": ["json", "csv"], "sample_count": "Variable" } } # NovaEval Compatible Metrics/Scorers SUPPORTED_METRICS = { "accuracy": { "name": "Accuracy", "category": "Accuracy-Based", "description": "Classification accuracy for multiple choice questions", "best_for": ["multiple_choice", "classification"] }, "exact_match": { "name": "Exact Match", "category": "Accuracy-Based", "description": "Exact string matching between prediction and reference", "best_for": ["short_answer", "factual"] }, "f1_score": { "name": "F1 Score", "category": "Accuracy-Based", "description": "F1 score for classification tasks", "best_for": ["classification", "binary_tasks"] }, "semantic_similarity": { "name": "Semantic Similarity", "category": "Semantic-Based", "description": "Embedding-based similarity scoring", "best_for": ["text_generation", "paraphrasing"] }, "bert_score": { "name": "BERT Score", "category": "Semantic-Based", "description": "BERT-based semantic evaluation", "best_for": ["text_generation", "summarization"] }, "rouge_score": { "name": "ROUGE Score", "category": "Semantic-Based", "description": "ROUGE metrics for text generation", "best_for": ["summarization", "text_generation"] }, "code_execution": { "name": "Code Execution", "category": "Code-Specific", "description": "Execute and validate code outputs", "best_for": ["code_generation", "programming"] }, "syntax_checker": { "name": "Syntax Checker", "category": "Code-Specific", "description": "Validate code syntax", "best_for": ["code_generation", "programming"] }, "llm_judge": { "name": "LLM Judge", "category": "Custom", "description": "Use another LLM as a judge", "best_for": ["open_ended", "creative_tasks"] } } async def broadcast_to_websockets(message: dict): """Broadcast message to all connected websockets""" if websocket_connections: disconnected = [] for websocket in websocket_connections: try: await websocket.send_text(json.dumps(message)) except: disconnected.append(websocket) # Remove disconnected websockets for ws in disconnected: websocket_connections.remove(ws) async def simulate_novaeval_evaluation(evaluation_id: str, request: EvaluationRequest): """Simulate NovaEval evaluation with detailed logging""" try: logger.info(f"Starting NovaEval evaluation {evaluation_id}") # Update status active_evaluations[evaluation_id]["status"] = "running" active_evaluations[evaluation_id]["start_time"] = datetime.now().isoformat() # Broadcast start await broadcast_to_websockets({ "type": "evaluation_start", "evaluation_id": evaluation_id, "message": f"๐Ÿš€ Starting NovaEval evaluation with {len(request.models)} models" }) # Simulate evaluation steps with detailed logging steps = [ "๐Ÿ”ง Initializing NovaEval framework", "๐Ÿ“Š Loading dataset configuration", "๐Ÿค– Preparing model interfaces", "๐Ÿ” Validating evaluation parameters", "๐Ÿ“ฅ Loading evaluation samples", "โš™๏ธ Setting up scorers and metrics", "๐Ÿš€ Starting model evaluations" ] for i, step in enumerate(steps): await asyncio.sleep(1) progress = int((i + 1) / len(steps) * 30) # 30% for setup log_message = f"[{datetime.now().strftime('%H:%M:%S')}] {step}" logger.info(log_message) await broadcast_to_websockets({ "type": "evaluation_progress", "evaluation_id": evaluation_id, "progress": progress, "message": log_message, "step": step }) # Simulate model evaluations results = {} for model_idx, model in enumerate(request.models): model_info = None for provider, models in SUPPORTED_MODELS.items(): if model in models: model_info = models[model] break if not model_info: continue await broadcast_to_websockets({ "type": "model_start", "evaluation_id": evaluation_id, "model": model, "message": f"๐Ÿค– Starting evaluation for {model_info['name']}" }) # Simulate model loading and evaluation model_steps = [ f"๐Ÿ“ฅ Loading {model_info['name']} ({model_info['provider']})", f"๐Ÿ”ง Configuring model parameters (temp={request.temperature}, max_tokens={request.max_tokens})", f"๐Ÿ“Š Running evaluation on {request.num_samples} samples", f"๐Ÿ“ˆ Computing {', '.join(request.metrics)} metrics", f"โœ… {model_info['name']} evaluation complete" ] for step_idx, step in enumerate(model_steps): await asyncio.sleep(2) base_progress = 30 + (model_idx * 60 // len(request.models)) step_progress = base_progress + (step_idx + 1) * (60 // len(request.models)) // len(model_steps) log_message = f"[{datetime.now().strftime('%H:%M:%S')}] {step}" logger.info(log_message) await broadcast_to_websockets({ "type": "evaluation_progress", "evaluation_id": evaluation_id, "progress": step_progress, "message": log_message, "model": model }) # Simulate detailed request/response logging for the evaluation step if "Running evaluation" in step: for sample_idx in range(min(3, request.num_samples)): # Show first 3 samples await asyncio.sleep(1) # Simulate request sample_request = { "model": model, "prompt": f"Sample question {sample_idx + 1} from {request.dataset}", "temperature": request.temperature, "max_tokens": request.max_tokens, "top_p": request.top_p } # Simulate response sample_response = { "response": f"Model response for sample {sample_idx + 1}", "tokens_used": 45 + sample_idx * 10, "latency_ms": 1200 + sample_idx * 200, "cost_usd": model_info["cost_per_1k_tokens"] * (45 + sample_idx * 10) / 1000 } await broadcast_to_websockets({ "type": "request_response", "evaluation_id": evaluation_id, "model": model, "sample_index": sample_idx + 1, "request": sample_request, "response": sample_response, "message": f"๐Ÿ“ Sample {sample_idx + 1}/{request.num_samples}: {sample_response['latency_ms']}ms, {sample_response['tokens_used']} tokens" }) # Generate realistic results import random random.seed(hash(model + request.dataset)) # Consistent results model_results = {} for metric in request.metrics: if metric == "accuracy": score = 0.6 + random.random() * 0.35 # 60-95% elif metric == "f1_score": score = 0.55 + random.random() * 0.4 # 55-95% elif metric in ["semantic_similarity", "bert_score"]: score = 0.7 + random.random() * 0.25 # 70-95% elif metric == "exact_match": score = 0.4 + random.random() * 0.5 # 40-90% else: score = 0.5 + random.random() * 0.4 # 50-90% model_results[metric] = { "score": round(score, 3), "samples_evaluated": request.num_samples, "metric_type": SUPPORTED_METRICS[metric]["category"] } results[model] = { "model_info": model_info, "metrics": model_results, "total_tokens": request.num_samples * 50, "total_cost": model_info["cost_per_1k_tokens"] * request.num_samples * 50 / 1000, "avg_latency_ms": 1000 + random.randint(200, 800) } await broadcast_to_websockets({ "type": "model_complete", "evaluation_id": evaluation_id, "model": model, "results": model_results, "message": f"โœ… {model_info['name']} completed with {len(model_results)} metrics" }) # Final processing await asyncio.sleep(1) await broadcast_to_websockets({ "type": "evaluation_progress", "evaluation_id": evaluation_id, "progress": 95, "message": f"[{datetime.now().strftime('%H:%M:%S')}] ๐Ÿ“Š Generating evaluation report" }) await asyncio.sleep(2) # Complete evaluation active_evaluations[evaluation_id]["status"] = "completed" active_evaluations[evaluation_id]["end_time"] = datetime.now().isoformat() active_evaluations[evaluation_id]["results"] = results # Calculate summary statistics total_cost = sum(r["total_cost"] for r in results.values()) total_tokens = sum(r["total_tokens"] for r in results.values()) avg_latency = sum(r["avg_latency_ms"] for r in results.values()) / len(results) summary = { "models_evaluated": len(request.models), "samples_per_model": request.num_samples, "total_samples": len(request.models) * request.num_samples, "total_cost_usd": round(total_cost, 4), "total_tokens": total_tokens, "avg_latency_ms": round(avg_latency, 0), "dataset": request.dataset, "metrics": request.metrics } await broadcast_to_websockets({ "type": "evaluation_complete", "evaluation_id": evaluation_id, "progress": 100, "results": results, "summary": summary, "message": f"๐ŸŽ‰ Evaluation complete! {len(request.models)} models evaluated on {request.num_samples} samples" }) logger.info(f"NovaEval evaluation {evaluation_id} completed successfully") except Exception as e: logger.error(f"Error in evaluation {evaluation_id}: {str(e)}") active_evaluations[evaluation_id]["status"] = "failed" active_evaluations[evaluation_id]["error"] = str(e) await broadcast_to_websockets({ "type": "evaluation_error", "evaluation_id": evaluation_id, "error": str(e), "message": f"โŒ Evaluation failed: {str(e)}" }) @app.get("/", response_class=HTMLResponse) async def get_homepage(): """Serve the comprehensive NovaEval interface""" return HTMLResponse(content=f""" NovaEval by Noveum.ai - Advanced AI Model Evaluation Platform
๐Ÿงช

NovaEval

Advanced AI Model Evaluation Platform

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Latest LLMs

Evaluate cutting-edge language models from OpenAI, Anthropic, AWS Bedrock, and Noveum.ai

GPT-4o, Claude 3.5 Sonnet
Real-time model search
Cost and performance metrics
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Comprehensive Datasets

Test models on academic benchmarks, code generation, and custom datasets

MMLU, HumanEval, HellaSwag
Custom dataset upload
Configurable sample sizes
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Real-time evaluation logs, detailed metrics, and interactive visualizations

Live request/response logs
Multiple scoring metrics
Export results (JSON, CSV)

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""") @app.get("/api/models") async def get_models(): """Get all supported models""" return SUPPORTED_MODELS @app.get("/api/datasets") async def get_datasets(): """Get all supported datasets""" return SUPPORTED_DATASETS @app.get("/api/metrics") async def get_metrics(): """Get all supported metrics""" return SUPPORTED_METRICS @app.post("/api/evaluate") async def start_evaluation(request: EvaluationRequest): """Start a new evaluation""" evaluation_id = str(uuid.uuid4()) # Store evaluation info active_evaluations[evaluation_id] = { "id": evaluation_id, "request": request.dict(), "status": "starting", "created_at": datetime.now().isoformat() } # Start evaluation in background asyncio.create_task(simulate_novaeval_evaluation(evaluation_id, request)) logger.info(f"Started evaluation {evaluation_id} with {len(request.models)} models") return EvaluationResponse( evaluation_id=evaluation_id, status="started", message="Evaluation started successfully" ) @app.get("/api/evaluations/{evaluation_id}") async def get_evaluation(evaluation_id: str): """Get evaluation status and results""" if evaluation_id not in active_evaluations: raise HTTPException(status_code=404, detail="Evaluation not found") return active_evaluations[evaluation_id] @app.websocket("/ws/{evaluation_id}") async def websocket_endpoint(websocket: WebSocket, evaluation_id: str): """WebSocket endpoint for real-time evaluation updates""" await websocket.accept() websocket_connections.append(websocket) try: while True: # Keep connection alive await asyncio.sleep(1) except WebSocketDisconnect: websocket_connections.remove(websocket) @app.get("/api/health") async def health_check(): """Health check endpoint""" return { "status": "healthy", "timestamp": datetime.now().isoformat(), "version": "1.0.0", "active_evaluations": len(active_evaluations) } if __name__ == "__main__": port = int(os.getenv("PORT", 7860)) logger.info(f"Starting Comprehensive NovaEval Space on port {port}") uvicorn.run(app, host="0.0.0.0", port=port, reload=False)