Blog post

README.md

@@ -1 +1,16 @@
-this is the branch for the pdiscovery-bot!
+this is the branch for the pdiscovery-bot! 
+
+https://blog.projectdiscovery.io/asm-platform-using-projectdiscovery-tools/
+
+To run use the following commands:
+```
+cd ./bin
+pip3 install -r requirements.txt
+sh ./start_db.sh
+flask run
+streamlit run ./bin/ui.py
+python3 worker.py 
+```
+Results can be viewed with the simple UI utilising streamlit.
+
+<img width="1069" alt="image" src="https://user-images.githubusercontent.com/954507/184242099-64a426cc-a224-4187-8e0e-3e02729b97bd.png">

bin/requirements.txt

@@ -1,3 +1,4 @@
 pymongo
 redis
 apscheduler
+streamlit

bin/ui.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+import streamlit as st
+import pymongo
+from pymongo import MongoClient
+import pandas as pd
+import string
+import io
+import re
+import lxml
+
+st.set_page_config(
+     page_title="ASM Viewer",
+     page_icon="🧊",
+     layout="wide",
+     initial_sidebar_state="expanded"
+
+ )
+
+st.title("ASM Viewer")
+st.sidebar.header('ASM Viewer')
+scans = st.sidebar.text_input('Scan Search')
+st.sidebar.info('Please start by typing the target id.')
+
+# Initialize connection.
+# Uses st.experimental_singleton to only run once.
+@st.experimental_singleton
+def init_connection():
+    return pymongo.MongoClient("mongodb://127.0.0.1:27017")
+
+client = init_connection()
+
+# pipeline for subs
+
+
+def query(scans,keywords):
+
+    subspipeline = [
+    {
+        "$match": {
+            'target_id': '%s' % (scans)
+        }
+    }, 
+    {
+        "$sort": {
+            "scan_id": pymongo.ASCENDING
+        }
+    },
+    {
+        "$project": {
+        "_id": 0, # Change to 0 if you wish to ignore "_id" field.
+        "name": 1,
+        "host": 1,
+        "source": 1,
+        "scan_id": 1
+    }
+  }]
+
+    result = client['asm']['subs'].aggregate(subspipeline)
+
+    results_as_dataframe = pd.DataFrame(list(result))
+
+    return results_as_dataframe
+
+
+
+if st.sidebar.button('Search'):
+    df = query(scans,scans)
+    if df.empty:
+        st.error("No matches found!")
+    else:
+        st.write(df)