app_local.py

import gradio as gr
import requests
import json
import time
from datetime import datetime

class OllamaSAAPBenchmark:
    def __init__(self, base_url="http://localhost:11434"):
        self.base_url = base_url
        
    def test_agent_response(self, prompt, model, agent_role="General"):
        """Test Agent-spezifische Responses für SAAP"""
        
        # SAAP-spezifische Prompts je nach Agent-Rolle
        saap_prompts = {
            "Jane": f"Als KI-Architektin: {prompt}",
            "John": f"Als Entwickler: {prompt}", 
            "Justus": f"Als Rechtsexperte: {prompt}",
            "General": prompt
        }
        
        final_prompt = saap_prompts.get(agent_role, prompt)
        start_time = time.time()
        
        try:
            response = requests.post(
                f"{self.base_url}/api/generate",
                json={
                    "model": model,
                    "prompt": final_prompt,
                    "stream": False,
                    "options": {"temperature": 0.7, "num_predict": 256}
                },
                timeout=60
            )
            
            end_time = time.time()
            
            if response.status_code == 200:
                result = response.json()
                return {
                    "response": result.get("response", ""),
                    "time": f"{end_time - start_time:.2f}s",
                    "model": model,
                    "agent_role": agent_role,
                    "tokens": len(result.get("response", "").split()),
                    "status": "✅ Success"
                }
            else:
                return {"status": f"❌ Error {response.status_code}", "time": f"{end_time - start_time:.2f}s"}
                
        except Exception as e:
            return {"status": f"❌ Connection Error: {str(e)[:50]}...", "time": f"{time.time() - start_time:.2f}s"}

    def list_models(self):
        try:
            response = requests.get(f"{self.base_url}/api/tags")
            if response.status_code == 200:
                models = response.json().get("models", [])
                return [model["name"] for model in models]
            return ["Connection failed - check if Ollama is running"]
        except:
            return ["❌ Cannot connect to Ollama"]

# Initialize benchmark system
benchmark = OllamaSAAPBenchmark()
available_models = benchmark.list_models()

# SAAP Benchmark Interface
def run_saap_benchmark(prompt, selected_models, agent_role):
    if not prompt.strip():
        return "⚠️ Bitte geben Sie einen Test-Prompt ein."
    
    results = []
    results.append(f"# 🚀 SAAP Multi-Agent Performance Benchmark")
    results.append(f"**Agent Role:** {agent_role}")
    results.append(f"**Test Prompt:** {prompt}")
    results.append(f"**Models:** {', '.join(selected_models)}")
    results.append(f"**Timestamp:** {datetime.now().strftime('%H:%M:%S')}")
    results.append("---")
    
    total_time = 0
    for model in selected_models:
        if model in available_models:
            result = benchmark.test_agent_response(prompt, model, agent_role)
            
            results.append(f"## 🤖 {model.upper()} ({agent_role})")
            results.append(f"**Status:** {result.get('status', '❌ Error')}")
            results.append(f"**Response Time:** {result.get('time', 'N/A')}")
            results.append(f"**Tokens Generated:** {result.get('tokens', 0)}")
            
            if 'response' in result and result['response']:
                preview = result['response'][:100].replace('\n', ' ')
                results.append(f"**Response Preview:** {preview}...")
            
            results.append("---")
            
            # Add to total time for averages
            try:
                time_val = float(result.get('time', '0').rstrip('s'))
                total_time += time_val
            except:
                pass
    
    # Performance Summary
    if selected_models:
        avg_time = total_time / len(selected_models)
        results.append(f"## 📊 Performance Summary")
        results.append(f"**Average Response Time:** {avg_time:.2f}s")
        results.append(f"**Total Models Tested:** {len(selected_models)}")
        
        # SAAP Performance Assessment
        if avg_time < 2.0:
            results.append(f"**SAAP Assessment:** ✅ Excellent for real-time multi-agent coordination")
        elif avg_time < 5.0:
            results.append(f"**SAAP Assessment:** ⚠️ Acceptable for batch processing")
        else:
            results.append(f"**SAAP Assessment:** ❌ Too slow for interactive agents")
    
    return "\n".join(results)

# Gradio Interface
with gr.Blocks(title="SAAP Performance Benchmark", theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🚀 SAAP - satware AI Agent Platform Benchmark")
    gr.Markdown("**Master Thesis:** Hanan Wandji Danga | **Hochschule Worms** | **satware AG**")
    
    with gr.Row():
        with gr.Column(scale=2):
            prompt_input = gr.Textbox(
                label="SAAP Test Prompt",
                placeholder="Beispiel: Entwickle eine Systemarchitektur für Multi-Agent Koordination",
                lines=3,
                value="Erkläre die Vorteile einer On-Premise Multi-Agent-Plattform gegenüber Cloud-Lösungen."
            )
            
            agent_role = gr.Dropdown(
                choices=["General", "Jane", "John", "Justus"],
                label="Agent Role Simulation",
                value="General"
            )
            
        with gr.Column(scale=1):
            model_selection = gr.CheckboxGroup(
                choices=available_models,
                label="Models to Benchmark",
                value=available_models[:2] if len(available_models) >= 2 else available_models
            )
            
            benchmark_btn = gr.Button("🚀 Run SAAP Benchmark", variant="primary", size="lg")
    
    # Results
    results_output = gr.Markdown(label="Benchmark Results")
    
    # Benchmark function
    benchmark_btn.click(
        run_saap_benchmark,
        inputs=[prompt_input, model_selection, agent_role],
        outputs=results_output
    )
    
    # System Info
    with gr.Accordion("ℹ️ System Information", open=False):
        gr.Markdown(f"""
        ### 📋 SAAP Test Environment
        - **Available Models:** {len(available_models)}
        - **Models:** {', '.join(available_models)}
        - **Ollama Server:** {benchmark.base_url}
        
        ### 🎯 SAAP Performance Targets
        - **Real-time Coordination:** < 2s per response
        - **Batch Processing:** < 5s per response
        - **Multi-Agent Sync:** < 10s for complex workflows
        
        ### 🎓 Master Thesis Context
        **Projekt:** SAAP - satware AI Autonomous Agent Platform  
        **Student:** Hanan Wandji Danga  
        **Universität:** Hochschule Worms  
        **Betreuung:** Michael Wegener  
        **Ziel:** On-Premise Multi-Agent-Plattform mit lokalen LLMs
        """)

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