On Query decorator example

# Import Required libraries
from uagents import Agent, Context, Model
from uagents.setup import fund_agent_if_low
from serpapi import GoogleSearch
 
# Define Request and Response Models
class Request(Model):
    query: str
class Response(Model):
    response: str
 
# Define function to handle job serach queries
async def get_job_summary(query, api_key):
    # Parameters for the API request
    params = {
      "engine": "google_jobs",
      "q": query,
      "hl": "en",
      "api_key": api_key
    }
 
    # Perform the search
    search = GoogleSearch(params)
    results = search.get_dict()
    jobs_results = results.get("jobs_results", [])
    
    # Initialize the result string
    res_str = ""
    # Get summary for the first job result
    if jobs_results:
        job = jobs_results[0]  # Get the first job
        res_str += (
            "\n_______________________________________________"
            + f"\nJob Title: {job.get('title', 'N/A')}"
            + f"Company Name: {job.get('company_name', 'N/A')}"
            + f"Location: {job.get('location', 'N/A')}"
            + f"Posted: {job['detected_extensions'].get('posted_at', 'N/A')}"
            + f"Type: {job['detected_extensions'].get('schedule_type', 'N/A')}"
            + f"Description: {job.get('description', 'N/A')[:500]}"
            + "_______________________________________________"
        )
    else:
        res_str = "No job results found."
 
    return res_str
 
 
# Define Search Agent
SearchAgent = Agent(
    name="SearchAgent",
    port=8000,
    seed="SearchAgent secret phrase",
    endpoint=["http://127.0.0.1:8000/submit"],
)
 
# Registering agent on Almananc and funding it.
fund_agent_if_low(SearchAgent.wallet.address())
 
# On agent startup printing address
@SearchAgent.on_event('startup')
async def agent_details(ctx: Context):
    ctx.logger.info(f'Search Agent Address is {SearchAgent.address}')
 
# On_query handler
@SearchAgent.on_query(model=Request, replies={Response})
async def query_handler(ctx: Context, sender: str, msg: Request):
    ctx.logger.info("Query received")
    try:
        ctx.logger.info(f'Fetching job details for query: {msg.query}')
        response = await get_job_summary(msg.query, "<YOUR_JOB_SEARCH_SERP_API_KEY>")
        # Ensure the response is a string
        response_str = str(response)
        ctx.logger.info(f'Response: {response_str}')
        await ctx.send(sender, Response(response=response_str))
    except Exception as e:
        error_message = f"Error fetching job details: {str(e)}"
        ctx.logger.error(error_message)
        # Ensure the error message is sent as a string
        await ctx.send(sender, Response(response=str(error_message)))
 
 
if __name__ == "__main__":
    SearchAgent.run()

# Import required libraries
from flask import Flask, request, jsonify
from uagents.query import query
from uagents import Model
import json
import asyncio
 
# Initialise flask app
app = Flask(__name__)
 
AGENT_ADDRESS = 'Provide your job search agent address'
 
# Definining request model
class Request(Model):
    query: str
 
# Defining endpoint to handle search requests
@app.route('/search-jobs')
def search_jobs():
    keyword = request.args.get('q')  # Get the query parameter
    if not keyword:
        return jsonify({"error": "No query provided."}), 400
 
    response = synchronous_agent_query(keyword)
    
    # Assuming 'response' is the data you want to return
    return jsonify({"results": response})
 
# Define an asynchronous function to query the agent with the specified request
async def synchronous_agent_query(keyword):
    req = Request(query=keyword)
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    response = loop.run_until_complete(make_agent_call(req))
    loop.close()
    return f'Search results for {keyword}: {response}'
 
# Define an asynchronous function to query the agent with the specified request
async def agent_query(req):
    response = await query(destination=AGENT_ADDRESS, message=req, timeout=15.0)
    data = json.loads(response.decode_payload())
    return data["response"]
 
# Define an asynchronous function to handle the agent call, which will be used within the synchronous Flask route
async def make_agent_call(req: Request):
    try:
        response = await agent_query(req)
        return f"{response}"
    except Exception as e:
        return f"unsuccessful agent call - {str(e)}"
 
# Run the Flask application if this script is executed directly
if __name__ == "__main__":
    app.run(debug=True)

INFO:     [SearchAgent]: Almanac registration is up to date!
INFO:     [SearchAgent]: Search Agent Address is agent1qdmgt4txxlvve4fmk8cw6jcp4gd2l709ctllx7jvz0kx4jzace5hu8n00hj
INFO:     [SearchAgent]: Starting server on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO:     [SearchAgent]: Query received
INFO:     [SearchAgent]: Fetching job details for query: software developer
INFO:     [SearchAgent]: Response: _______________________________________________
Job Title: Software DeveloperCompany Name: SAIC Career SiteLocation:   Chantilly, VA   Posted: 1 day agoType: Full-timeDescription: Description

INTRODUCTION: The Sponsor supports a diverse set of corporate goals across the organization by conducting technical risk assessments and providing technical risk mitigation guidance on the use of various enabling technologies. The Sponsor requires subject matter expertise in technical risk analysis of enterprise and mission systems, IT systems and networks, mobile and wireless... networks, cloud-based computing, network management platforms, communication protocols, scripting or pro_______________________________________________

{
  "results": "Search results for software developer: _______________________________________________\nJob Title: Software DeveloperCompany Name: SAIC Career SiteLocation:   Chantilly, VA   Posted: 1 day agoType: Full-timeDescription: Description\n\nINTRODUCTION: The Sponsor supports a diverse set of corporate goals across the organization by conducting technical risk assessments and providing technical risk mitigation guidance on the use of various enabling technologies. The Sponsor requires subject matter expertise in technical risk analysis of enterprise and mission systems, IT systems and networks, mobile and wireless... networks, cloud-based computing, network management platforms, communication protocols, scripting or pro_______________________________________________"
}