fix: deployment

README.md

@@ -41,7 +41,6 @@ To ensure the microservice accurately aligns with your intended task a test scen
 ## Quickstart
 ### Requirements
 - OpenAI key with access to GPT-4
-- Create an account at [cloud.jina.ai](https://cloud.jina.ai) where your microservice will be deployed
 
 ### Installation
 ```bash
@@ -64,23 +63,22 @@ To generate your personal microservice two things are required:
 The creation process should take between 5 and 15 minutes.
 During this time, GPT iteratively builds your microservice until it finds a strategy that make your test scenario pass.
 
+Be aware that the costs you have to pay for openai vary between $0.50 and $3.00 per microservice (using GPT-4).
 
 ### Run Microservice
+Run the microservice locally. With this command a playground opens in your browser where you can test the microservice.
 ```bash
 gptdeploy run --path <path to microservice>
 ```
 
 ### Deploy Microservice
-It is required to have a [Jina account](https://cloud.jina.ai/) to deploy your microservice.
+If you want to deploy your microservice to the cloud a [Jina account](https://cloud.jina.ai/) is required.
-
+When creating a Jina account, you get some free credits, which you can use to deploy your microservice ($0.025/hour).
+If you run out of credits, you can purchase more.
 ```bash
 gptdeploy deploy --microservice_path <path to microservice>
 ```
-[//]: # (Once the microservice is generated and deployed, you can test it using the generated Streamlit playground.)
+
-[//]: # (The deployment is made on the Jina`s infrastructure. )
-[//]: # (When creating a Jina account, you get some free credits, which you can use to deploy your microservice &#40;$0.025/hour&#41;.)
-[//]: # (Be aware that the costs you have to pay for openai vary between $0.50 and $3.00 per microservice.)
-[//]: # (If you run out of credits, you can purchase more.)
 
 ### Delete Microservice
 To save credits you can delete your microservice via the following commands:

src/apis/jina_cloud.py

@@ -4,11 +4,13 @@ import os
 import re
 import subprocess
 import threading
+import time
 import webbrowser
 from pathlib import Path
 
 import click
 import hubble
+import requests
 from hubble.executor.helper import upload_file, archive_package, get_request_header
 from jcloud.flow import CloudFlow
 from jina import Flow
@@ -18,9 +20,6 @@ from src.utils.io import suppress_stdout, is_docker_running
 from src.utils.string_tools import print_colored
 
 
-import requests
-import time
-
 def wait_until_app_is_ready(url):
     is_app_ready = False
     while not is_app_ready:
@@ -33,6 +32,7 @@ def wait_until_app_is_ready(url):
             pass
         time.sleep(0.5)
 
+
 def open_streamlit_app():
     url = "http://localhost:8081/playground"
     wait_until_app_is_ready(url)
@@ -129,11 +129,17 @@ def deploy_on_jcloud(executor_name, microservice_path):
         except Exception as e:
             print(f'Could not deploy on Jina Cloud. Trying again in 5 seconds. Error: {e}')
             time.sleep(5)
+        except SystemExit as e:
+            raise SystemExit(f'''
+Looks like your free credits ran out. 
+Please add payment information to your account and try again.
+Visit https://cloud.jina.ai/
+            ''') from e
     if i == 2:
         raise Exception('''
-            Could not deploy on Jina Cloud. 
+Could not deploy on Jina Cloud. 
-            This can happen when the microservice is buggy, if it requires too much memory or if the Jina Cloud is overloaded. 
+This can happen when the microservice is buggy, if it requires too much memory or if the Jina Cloud is overloaded. 
-            Please try again later.
+Please try again later.
 '''
                         )
 
@@ -141,13 +147,15 @@ def deploy_on_jcloud(executor_name, microservice_path):
 Your Microservice is deployed.
 Run the following command to start the playground:
 
-streamlit run {os.path.join(microservice_path, "app.py")} --server.port 8081 --server.address 0.0.0.0 -- --host http://{host}
+streamlit run {os.path.join(microservice_path, "app.py")} --server.port 8081 --server.address 0.0.0.0 -- --host {host}
 '''
           )
     return host
 
+
 def run_streamlit_app(app_path):
-    subprocess.run(['streamlit', 'run', app_path, 'server.address', '0.0.0.0', '--server.port', '8081', '--', '--host', 'grpc://localhost:8080'])
+    subprocess.run(['streamlit', 'run', app_path, 'server.address', '0.0.0.0', '--server.port', '8081', '--', '--host',
+                    'grpc://localhost:8080'])
 
 
 def run_locally(executor_name, microservice_version_path):
@@ -176,6 +184,7 @@ We now start the playground for you.
 
         flow.block()
 
+
 def create_flow_yaml(dest_folder, executor_name, use_docker):
     if use_docker:
         prefix = 'jinaai+docker'