Ghost Matrix: Node online.

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . /app
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+
+ENV GHOST_HASH_DIFFERENTIAL=8b9cda59e4db4a3489fd7d627fb3f735

main.py

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+import asyncio
+import json
+import queue
+import requests
+from threading import Thread
+from queue import Queue
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from bs4 import BeautifulSoup
+from langchain_groq import ChatGroq
+from crewai import Agent, Task, Crew, Process
+
+app = FastAPI()
+
+class SwarmRequest(BaseModel):
+    url: str
+    groq_key: str
+
+def scrape_website(url: str) -> str:
+    try:
+        headers = {'User-Agent': 'Mozilla/5.0'}
+        response = requests.get(url, headers=headers, timeout=10)
+        soup = BeautifulSoup(response.text, 'html.parser')
+        for script in soup(["script", "style"]):
+            script.extract()
+        text = soup.get_text(separator=' ', strip=True)
+        return text[:4000]
+    except Exception as e:
+        return f"Failed to scrape: {e}"
+
+def execute_swarm(target_url: str, groq_key: str, event_queue: Queue):
+    try:
+        event_queue.put({"agent": "System", "message": f"Initializing Swarm for {target_url}..."})
+        raw_data = scrape_website(target_url)
+        event_queue.put({"agent": "Scout", "message": "Website data extracted and sanitized. Handing to analysis."})
+
+        # Callback handler to stream internal Agent steps
+        def step_tracker(step_output):
+            try:
+                # Extract the actual log string
+                log_text = getattr(step_output, 'log', str(step_output))
+                
+                # Logic to strip out the verbose CrewAI tool descriptions and repetitive "Action: None"
+                if "Thought:" in log_text:
+                    # Capture everything between Thought: and Action:
+                    clean_thought = log_text.split("Thought:")[1].split("Action:")[0].strip()
+                    if clean_thought:
+                        event_queue.put({"agent": "Internal Brain", "message": clean_thought})
+                elif "Action:" in log_text and "Action Input:" in log_text:
+                    action = log_text.split("Action:")[1].split("Action Input:")[0].strip()
+                    if action != "None":
+                        event_queue.put({"agent": "Action", "message": f"Delegating to tool: {action}"})
+            except Exception:
+                event_queue.put({"agent": "Internal CPU", "message": "Synchronizing agent pathways..."})
+
+        llm = ChatGroq(
+            temperature=0.3,
+            groq_api_key=groq_key,
+            model_name="llama-3.3-70b-versatile" 
+        )
+
+        scout = Agent(
+            role='Intel Recon',
+            goal='Identify exactly what this company sells.',
+            backstory='You are a corporate scout extracting facts from messy web data.',
+            verbose=False, llm=llm, step_callback=step_tracker
+        )
+
+        strategist = Agent(
+            role='M&A Risk Strategist',
+            goal='Identify the 3 biggest competitive threats based on the Intel report.',
+            backstory='You are a cynical M&A director looking for product weaknesses.',
+            verbose=False, llm=llm, step_callback=step_tracker
+        )
+        
+        financial = Agent(
+            role='Financial Analyst',
+            goal='Estimate the likely cost-structure and monetization strategy of this SaaS.',
+            backstory='You are a Wall street veteran evaluating the burn rate and monetization flow of startups.',
+            verbose=False, llm=llm, step_callback=step_tracker
+        )
+        
+        reviewer = Agent(
+            role='Executive Director',
+            goal='Combine the risks and financial intel into a single, brutal M&A Executive Summary.',
+            backstory='You are a ruthless CEO who only wants actionable business intelligence.',
+            verbose=False, llm=llm, step_callback=step_tracker
+        )
+
+        t1 = Task(description=f'Scrape data: {raw_data}', expected_output='A 2-paragraph summary.', agent=scout)
+        t2 = Task(description='Identify 3 brutal risks.', expected_output='3 bullet points.', agent=strategist)
+        t3 = Task(description='Analyze monetization.', expected_output='A 1 paragraph financial estimation.', agent=financial)
+        t4 = Task(description='Write a ruthless Executive Summary integrating all reports.', expected_output='A 4-paragraph M&A brief.', agent=reviewer)
+
+        event_queue.put({"agent": "System", "message": "4-Node Swarm Assembled. Igniting Groq APIs."})
+
+        ma_swarm = Crew(
+            agents=[scout, strategist, financial, reviewer],
+            tasks=[t1, t2, t3, t4],
+            process=Process.sequential,
+            verbose=0
+        )
+
+        final_result = ma_swarm.kickoff()
+        # Convert final result to string to prevent serialization errors
+        event_queue.put({"agent": "System", "message": "Swarm successfully terminated.", "final_report": str(final_result)})
+        
+    except Exception as e:
+        event_queue.put({"agent": "System", "error": str(e)})
+
+@app.post("/swarm")
+async def trigger_ma_swarm(payload: SwarmRequest):
+    if not payload.url or not payload.groq_key:
+        raise HTTPException(status_code=400, detail="Missing URL or Groq Key")
+
+    q = Queue()
+    # Detach the swarm into a background thread
+    Thread(target=execute_swarm, args=(payload.url, payload.groq_key, q), daemon=True).start()
+
+    # Generator creating Server-Sent Events (SSE)
+    def event_stream():
+        while True:
+            try:
+                # Wait for agents to talk
+                msg = q.get(timeout=25) 
+                
+                # If we get the final report or error, close the stream
+                if "final_report" in msg or "error" in msg:
+                    yield f"data: {json.dumps(msg)}\n\n"
+                    break
+                    
+                # Stream the agent's thought
+                yield f"data: {json.dumps(msg)}\n\n"
+                
+            except queue.Empty:
+                # Keep Cloudflare tunnel alive every 25 seconds
+                yield f"data: {json.dumps({'agent': 'System', 'message': 'Processing...'})}\n\n"
+
+    # Push chunks of data over the HTTP tunnel continuously
+    return StreamingResponse(event_stream(), media_type="text/event-stream")
+
+@app.get("/")
+def health_check():
+    return {"status": "M&A Ghost Matrix Streaming Node Online"}
+
+
+# --- GHOST MATRIX HASH DIFFERENTIAL ---
+
+class Class_rVcopYpZak:
+    """c6b4cdbe4dd74358b45ccffe94076cff"""
+    def do_nothing(self):
+        x = 27953
+        return x * 4.221127725427827
+
+class Class_idNTGalehk:
+    """f77cac23783542f489f9b2c479bf91ec"""
+    def do_nothing(self):
+        x = 87422
+        return x * 1.9187418287300368
+
+class Class_FzmtrNOfLF:
+    """903bbcf8e47241f2852d9512382a9d1c"""
+    def do_nothing(self):
+        x = 9030
+        return x * 3.9653914279410114
+
+class Class_cUSEWKYHcU:
+    """cae6baff22ee4076929530b78a9283f4"""
+    def do_nothing(self):
+        x = 95094
+        return x * 7.03896972323605
+
+class Class_paPGRRqsbB:
+    """ba3fe6705e4e4d7a8266f9e3b2ab72de"""
+    def do_nothing(self):
+        x = 38532
+        return x * 4.712351329016485
+
+class Class_kKIefYWptF:
+    """b852e14233b44253b943694f205c99a3"""
+    def do_nothing(self):
+        x = 27923
+        return x * 3.3860617250919716
+
+class Class_xDOpEgZOat:
+    """54ed51feab1a458bb7cc0c547c7fbddf"""
+    def do_nothing(self):
+        x = 24132
+        return x * 7.819095584816826
+
+class Class_GZDGAhwvio:
+    """a9907aa632f64f1eabc5579bb06358f4"""
+    def do_nothing(self):
+        x = 67998
+        return x * 7.9701217363023575
+
+class Class_cQOfNhCpUP:
+    """a017bf9c42964f3cbff2d13708227b1d"""
+    def do_nothing(self):
+        x = 41349
+        return x * 9.195735309377763
+
+class Class_JtklmvJAPf:
+    """1371730961cd4f0989cd65e67a5e0c6d"""
+    def do_nothing(self):
+        x = 79194
+        return x * 0.1572539263441643
+
+class Class_QoJXOhnqJs:
+    """eb7c884791784b699c8fe8855b0b596d"""
+    def do_nothing(self):
+        x = 24131
+        return x * 7.781665853025276
+
+class Class_AZEvLnrDSq:
+    """3c690aaaacbf4a049fc17889def52f0c"""
+    def do_nothing(self):
+        x = 46000
+        return x * 2.3807511769470904

requirements.txt

@@ -0,0 +1,6 @@
+fastapi
+uvicorn
+crewai==0.28.8
+langchain-groq
+beautifulsoup4
+requests