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Update ImageClient code to use latest Dall-E available version #8

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5 changes: 3 additions & 2 deletions PodcastSocialMediaCopilot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@
openai_api_type = "azure"
openai_api_base = "https://TODO.openai.azure.com/" # Your endpoint will look something like this: https://YOUR_AOAI_RESOURCE_NAME.openai.azure.com/
openai_api_key = "TODO" # Your key will look something like this: 00000000000000000000000000000000
gpt4_deployment_name="gpt-4"
gpt4_deployment_name = "gpt-4"
dalle_deployment_name = "Dalle3"

# We are assuming that you have all model deployments on the same Azure OpenAI service resource above. If not, you can change these settings below to point to different resources.
gpt4_endpoint = openai_api_base # Your endpoint will look something like this: https://YOUR_AOAI_RESOURCE_NAME.openai.azure.com/
Expand Down Expand Up @@ -250,7 +251,7 @@ def bing_grounding(input_dict:dict) -> dict:
print("Calling DALL-E model on Azure OpenAI Service to get an image for social media...\n")

# Establish the client class instance
client = ImageClient(dalle_endpoint, dalle_api_key, verbose=False) # change verbose to True for including debug print statements
client = ImageClient(dalle_endpoint, dalle_api_key, dalle_deployment_name, verbose=False) # change verbose to True for including debug print statements

# Generate an image
imageURL, postImage = client.generateImage(dalle_prompt)
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1 change: 1 addition & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@ Next, update the PodcastSocialMediaCopilot.py file with your settings.
+ Update **openai_api_base** with the name of your Azure OpenAI resource; this value should look like this: "https://YOUR_AOAI_RESOURCE_NAME.openai.azure.com/"
+ Update **openai_api_key** with the corresponding API key for your Azure OpenAI resource.
+ Update **gpt4_deployment_name** with the name of your model deployment for GPT-4 in your Azure OpenAI resource.
+ Update **dalle_deployment_name** with the name of your model deployment for Dall-E in your Azure OpenAI resource.
+ If your model deployments for gpt-4, dalle, and the plugins-capable model are all on the same Azure OpenAI resource, you're all set! If not, you can override the individual endpoints and keys for the resources for the various model deployments using the variables **gpt4_endpoint**, **gpt4_api_key**, **dalle_endpoint**, **dalle_api_key**, **plugin_model_url**, and **plugin_model_api_key**.
+ Optionally, you can also update the **podcast_url** and **podcast_audio_file** to reflect your own podcast.

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84 changes: 24 additions & 60 deletions dalle_helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,42 +5,51 @@
import time

class ImageClient:
def __init__(self, endpoint, key, api_version = "2022-08-03-preview", verbose=False):
def __init__(self, endpoint, key, deployment_name = "Dalle3", api_version = "2023-12-01-preview", verbose=False):
# These are the paramters for the class:
# ### endpoint: The endpoint for your Azure OpenAI resource
# ### key: The API key for your Azure OpenAI resource
# ### deployment_name: The deployment name for Dall-E
# ### api_version: The API version to use. This is optional and defaults to the latest version
self.endpoint = endpoint
self.api_key = key
self.api_version = api_version
self.deployment_name = deployment_name
self.verbose = verbose

def text_to_image(self, prompt):
# this method makes the text-to-image API call. It will return the raw response from the API call

reqURL = requests.models.PreparedRequest()
params = {'api-version':self.api_version}
#the full endpoint will look something like this https://YOUR_AOAI_RESOURCE_NAME.openai.azure.com/dalle/text-to-image
reqURL.prepare_url(self.endpoint + "dalle/text-to-image", params)
#the full endpoint will look something like this https://YOUR_AOAI_RESOURCE_NAME.openai.azure.com/openai/deployments/<deployment-name>/images/generations
reqURL.prepare_url(self.endpoint + f"openai/deployments/{self.deployment_name}/images/generations", params)
if self.verbose:
print("Sending a POST call to the following URL: {URL}".format(URL=reqURL.url))

#Construct the data payload for the call. This includes the prompt text as well as many optional parameters.
payload = { "caption": prompt}

payload = {"prompt": prompt}
r = requests.post(reqURL.url,
headers={
"Api-key": self.api_key,
"Content-Type": "application/json"
},
json = payload
)
# Response Body example: { "id": "80b095cb-4248-4fa7-90c2-933f0907fb2a", "status": "Running" }
# Key headers:
# Operation-Location: URL to get response
# Retry-after: 3 //seconds to respond

if r.status_code != 202:
# Response Body example:
# {
# "created": 1698342300,
# "data": [
# {
# "url": "https://dalletipusw2.blob.core.windows.net/private/images/e5451cc6-b1ad-4747-bd46-b89a3a3b8bc3/generated_00.png?se=2023-10-27T17%3A45%3A09Z&..."
# },
# {
# "url": "https://dalletipusw2.blob.core.windows.net/private/images/e5451cc6-b1ad-4747-bd46-b89a3a3b8bc3/generated_01.png?se=2023-10-27T17%3A45%3A09Z&..."
# }],
# "revised_prompt": "A vivid, natural representation of Microsoft Clippy wearing a cowboy hat."
# }

if r.status_code != 200:
print("Error: {error}".format(error=r.json()))

data = r.json()
Expand All @@ -49,34 +58,6 @@ def text_to_image(self, prompt):
print(data)
return r

def getImageResults(self, operation_location):
# This method will make an API call to get the status/results of the text-to-image API call using the
# Operation-Location header from the original API call

params = {'api-version':self.api_version}
# the full endpoint will look something like this
# https://YOUR_RESOURCE_NAME.openai.azure.com/dalle/text-to-image/operations/OPERATION_ID_FROM_PRIOR_RESPONSE?api-version=2022-08-03-preview

if self.verbose:
print("Sending a POST call to the following URL: {URL}".format(URL=operation_location))

r = requests.get(operation_location,
headers={
"Api-key": self.api_key,
"Content-Type": "application/json"
}
)

data = r.json()

if self.verbose:
print('Get Image results call response body')
print(data)
return r

# Sending a POST call to the following URL: <operatino-location>
# {'id': 'd63fc675-f751-40b7-a297-e692c3b966b9', 'result': {'caption': 'An avocado chair.', 'contentUrl': '<image location>', 'contentUrlExpiresAt': '2022-08-13T22:52:45Z', 'createdDateTime': '2022-08-13T21:50:55Z'}, 'status': 'Succeeded'}


def getImage(self, contentUrl):
# Download the images from the given URL
Expand All @@ -85,35 +66,18 @@ def getImage(self, contentUrl):


def generateImage(self, prompt):
submission = self.text_to_image( prompt)
submission = self.text_to_image(prompt)
if self.verbose:
print('Response code from submission')
print(submission.status_code)
print('Response body:')
print(submission.json())
if submission.status_code == 202:
operation_location = submission.headers['Operation-Location']
retry_after = submission.headers['Retry-after']
if submission.status_code == 200:
contentUrl = submission.json()['data'][0]['url']
else:
print('Not a 202 response')
print('Not a 200 response')
return "-1"

#wait to request
status = "not running"
while status != "Succeeded":
if self.verbose:
print('retry after: ' + retry_after)
time.sleep(int(retry_after))
r = self.getImageResults(operation_location)
# print(r.status_code)
# print(r.headers)
# print(r.json())
status = r.json()['status']
# print(status)
if status == "Failed":
return "-1"

contentUrl = r.json()['result']['contentUrl']
image = self.getImage(contentUrl)
return contentUrl, image.content

Expand Down