## Download the required model for semantic search

from sentence_transformers import SentenceTransformer

import os

sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')

embeddings = model.encode(sentences)

print(embeddings)

## Download dependencises

!git clone https://github.com/cyberytti/ToolHunt

os.chdir("ToolHunt")

!pip install -r requirements.txt

!pip install pyngrok

# =========================

# Directly Run ToolHunt in Colab (Debugging Mode)

# =========================

import sys

import threading

import time

from pyngrok import ngrok

# --- 1. Set up paths and environment ---

project_root = "/content" # Standard Colab working directory

toolhunt_dir = os.path.join(project_root, "ToolHunt")

if not os.path.isdir(toolhunt_dir):

raise FileNotFoundError(f"ToolHunt directory not found at {toolhunt_dir}")

# Crucially, add the project root to Python's path

# This allows 'from backend.main import ...' to work when app.py is run

if project_root not in sys.path:

sys.path.insert(0, project_root)

print(f"Added {project_root} to sys.path")

print(f"Project root: {project_root}")

print(f"ToolHunt dir: {toolhunt_dir}")

print(f"sys.path (relevant parts): {[p for p in sys.path if 'content' in p]}")

# Set ngrok token

ngrok.set_auth_token('your ngrok auth')

# --- 2. Import and run Flask app ---

try:

# Change working directory to ToolHunt so relative paths inside app.py work

# (like loading templates)

os.chdir(toolhunt_dir)

print(f"Changed working directory to: {os.getcwd()}")

# Now we can import app.py as a module because we added /content to sys.path

# and we are running from within the ToolHunt directory for relative file paths.

import app

print("✅ Successfully imported app.py")

# --- 3. Run Flask in a separate thread ---

def run_flask():

print("🚀 Starting Flask app...")

# Run Flask app. Note: debug=True might cause issues in threads/Colab,

# but let's try it first. If problems occur, remove debug=True.

app.app.run(host='127.0.0.1', port=5000, debug=False) # Start without debug first

flask_thread = threading.Thread(target=run_flask)

flask_thread.daemon = True # Dies when main thread dies

flask_thread.start()

print("🧵 Flask thread started.")

# --- 4. Wait and check ---

# Give Flask time to start (especially for model download on first run)

print("⏳ Waiting for Flask to initialize...")

time.sleep(8) # Adjust if needed

# A simple check if the thread is still alive (not a guarantee the server is up)

if flask_thread.is_alive():

print("✅ Flask thread is running.")

else:

print("⚠️ Flask thread has stopped. Check for errors in the logs above.")

# --- 5. Create ngrok tunnel ---

print("🚇 Starting ngrok tunnel...")

tunnel = ngrok.connect(5000, "http")

print("\n🎉 ToolHunt is ready!")

print(f"🔗 Public URL: {tunnel.public_url}")

print("\nPress the 'Interrupt Execution' button (⏹️) in Colab to stop.")

# --- 6. Keep alive ---

# Keep the main thread alive to maintain the tunnel

try:

while True:

time.sleep(1)

except KeyboardInterrupt:

print("\n🛑 Interrupted by user.")

finally:

print("🧹 Cleaning up...")

ngrok.kill()

# Note: Stopping the Flask thread cleanly from here is tricky in Colab.

# The daemon thread will stop when the main program ends.

print("✅ Done.")

except Exception as e:

print(f"❌ An error occurred during startup: {e}")

import traceback

traceback.print_exc()