toniewing/microservice-layer-13

1import asyncio2import json3import queue4import requests5from threading import Thread6from queue import Queue7from fastapi import FastAPI, HTTPException8from fastapi.responses import StreamingResponse9from pydantic import BaseModel10from bs4 import BeautifulSoup11from langchain_groq import ChatGroq12from crewai import Agent, Task, Crew, Process13 14app = FastAPI()15 16class SwarmRequest(BaseModel):17    url: str18    groq_key: str19 20def scrape_website(url: str) -> str:21    try:22        headers = {'User-Agent': 'Mozilla/5.0'}23        response = requests.get(url, headers=headers, timeout=10)24        soup = BeautifulSoup(response.text, 'html.parser')25        for script in soup(["script", "style"]):26            script.extract()27        text = soup.get_text(separator=' ', strip=True)28        return text[:4000]29    except Exception as e:30        return f"Failed to scrape: {e}"31 32def execute_swarm(target_url: str, groq_key: str, event_queue: Queue):33    try:34        event_queue.put({"agent": "System", "message": f"Initializing Swarm for {target_url}..."})35        raw_data = scrape_website(target_url)36        event_queue.put({"agent": "Scout", "message": "Website data extracted and sanitized. Handing to analysis."})37 38        # Callback handler to stream internal Agent steps39        def step_tracker(step_output):40            try:41                # Extract the actual log string42                log_text = getattr(step_output, 'log', str(step_output))43                44                # Logic to strip out the verbose CrewAI tool descriptions and repetitive "Action: None"45                if "Thought:" in log_text:46                    # Capture everything between Thought: and Action:47                    clean_thought = log_text.split("Thought:")[1].split("Action:")[0].strip()48                    if clean_thought:49                        event_queue.put({"agent": "Internal Brain", "message": clean_thought})50                elif "Action:" in log_text and "Action Input:" in log_text:51                    action = log_text.split("Action:")[1].split("Action Input:")[0].strip()52                    if action != "None":53                        event_queue.put({"agent": "Action", "message": f"Delegating to tool: {action}"})54            except Exception:55                event_queue.put({"agent": "Internal CPU", "message": "Synchronizing agent pathways..."})56 57        llm = ChatGroq(58            temperature=0.3,59            groq_api_key=groq_key,60            model_name="llama-3.1-8b-instant" 61        )62 63        scout = Agent(64            role='Intel Recon',65            goal='Identify exactly what this company sells.',66            backstory='You are a corporate scout extracting facts from messy web data.',67            verbose=False, llm=llm, step_callback=step_tracker68        )69 70        strategist = Agent(71            role='M&A Risk Strategist',72            goal='Identify the 3 biggest competitive threats based on the Intel report.',73            backstory='You are a cynical M&A director looking for product weaknesses.',74            verbose=False, llm=llm, step_callback=step_tracker75        )76        77        financial = Agent(78            role='Financial Analyst',79            goal='Estimate the likely cost-structure and monetization strategy of this SaaS.',80            backstory='You are a Wall street veteran evaluating the burn rate and monetization flow of startups.',81            verbose=False, llm=llm, step_callback=step_tracker82        )83        84        reviewer = Agent(85            role='Executive Director',86            goal='Combine the risks and financial intel into a single, brutal M&A Executive Summary.',87            backstory='You are a ruthless CEO who only wants actionable business intelligence.',88            verbose=False, llm=llm, step_callback=step_tracker89        )90 91        t1 = Task(description=f'Scrape data: {raw_data}', expected_output='A 2-paragraph summary.', agent=scout)92        t2 = Task(description='Identify 3 brutal risks.', expected_output='3 bullet points.', agent=strategist)93        t3 = Task(description='Analyze monetization.', expected_output='A 1 paragraph financial estimation.', agent=financial)94        t4 = Task(description='Write a ruthless Executive Summary integrating all reports.', expected_output='A 4-paragraph M&A brief.', agent=reviewer)95 96        event_queue.put({"agent": "System", "message": "4-Node Swarm Assembled. Igniting Groq APIs."})97 98        ma_swarm = Crew(99            agents=[scout, strategist, financial, reviewer],100            tasks=[t1, t2, t3, t4],101            process=Process.sequential,102            verbose=0,103            max_rpm=15104        )105 106        final_result = ma_swarm.kickoff()107        # Convert final result to string to prevent serialization errors108        event_queue.put({"agent": "System", "message": "Swarm successfully terminated.", "final_report": str(final_result)})109        110    except Exception as e:111        event_queue.put({"agent": "System", "error": str(e)})112 113@app.post("/swarm")114async def trigger_ma_swarm(payload: SwarmRequest):115    if not payload.url or not payload.groq_key:116        raise HTTPException(status_code=400, detail="Missing URL or Groq Key")117 118    q = Queue()119    # Detach the swarm into a background thread120    Thread(target=execute_swarm, args=(payload.url, payload.groq_key, q), daemon=True).start()121 122    # Generator creating Server-Sent Events (SSE)123    def event_stream():124        while True:125            try:126                # Wait for agents to talk127                msg = q.get(timeout=25) 128                129                # If we get the final report or error, close the stream130                if "final_report" in msg or "error" in msg:131                    yield f"data: {json.dumps(msg)}\n\n"132                    break133                    134                # Stream the agent's thought135                yield f"data: {json.dumps(msg)}\n\n"136                137            except queue.Empty:138                # Keep Cloudflare tunnel alive every 25 seconds139                yield f"data: {json.dumps({'agent': 'System', 'message': 'Processing...'})}\n\n"140 141    # Push chunks of data over the HTTP tunnel continuously142    return StreamingResponse(event_stream(), media_type="text/event-stream")143 144@app.get("/")145def health_check():146    return {"status": "M&A Ghost Matrix Streaming Node Online"}147
148
149# --- GHOST MATRIX HASH DIFFERENTIAL ---
150
151class Class_dJFPKBMgje:
152    """f83d8da41f2d44178fb2fe21c946b031"""
153    def do_nothing(self):
154        x = 94013
155        return x * 7.2991468625342195
156
157class Class_GHrDpqAlhB:
158    """fa15c4be8f2f4aa9ba1438710dc78fb1"""
159    def do_nothing(self):
160        x = 89027
161        return x * 8.899009877605067
162
163class Class_ekwDuUBvuW:
164    """fd2658b9e213409eb5ab28d13adeacb4"""
165    def do_nothing(self):
166        x = 99945
167        return x * 2.978269265004507
168
169class Class_xviMYQAxEM:
170    """40ffee09d5ed491a974d42d3d2107b63"""
171    def do_nothing(self):
172        x = 14526
173        return x * 2.7617174108643314
174
175class Class_awSXqpnWqc:
176    """4dbabc47f1b54666afb467411fd01d21"""
177    def do_nothing(self):
178        x = 80675
179        return x * 8.375863439828494
180
181class Class_Qnzsqupaxd:
182    """754d6f8f29cd4ef4bd2b55555ddaef02"""
183    def do_nothing(self):
184        x = 88227
185        return x * 1.5362309000968442
186
187class Class_cTMUiGDeCg:
188    """0f3761be372345b18ef8f541bff34bb8"""
189    def do_nothing(self):
190        x = 85711
191        return x * 1.6646967249974796
192