added image generation tab

.gitignore

@@ -0,0 +1 @@
+secrets.env

app.py

@@ -1,16 +1,15 @@
-# %% [markdown]
-# # ChatBot app with Gradio
-
-# %%
 import os
 from dotenv import load_dotenv, find_dotenv
 import gradio as gr
 import openai
+import requests
+from PIL import Image
+from io import BytesIO
 
+# load the secrets if running locally
 _ = load_dotenv(find_dotenv(filename="secrets.env", raise_error_if_not_found=False))
 
 # Global variable
-# ROOT_DIR = os.environ["ROOT_DIR"]
 AUTH_USERNAME = os.environ["AUTH_USERNAME"]
 AUTH_PASSWORD = os.environ["AUTH_PASSWORD"]
 
@@ -20,12 +19,8 @@ openai.api_key = os.environ["OPENAI_API_KEY"]
 SYSTEM_PROMPT = "You are a helpful assistant and do your best to answer the user's questions.\
 	You do not make up answers."
 
-# %% [markdown]
-# ## Define and test the API calls
-
-# %%
-# define the function that will make the API calls 
-def APIcall(prompt:str, temperature = 0.7, max_tokens = 1024, model="GPT-3.5", stream=True):
+# define the function that will make the API calls for the catbot
+def chatBotCompletionApiCall(prompt:str, temperature = 0.7, max_tokens = 1024, model="GPT-3.5", stream=True):
 	if model == "GPT-3.5":
 		model = "gpt-3.5-turbo-0125"
 	else:
@@ -47,13 +42,8 @@ def APIcall(prompt:str, temperature = 0.7, max_tokens = 1024, model="GPT-3.5", s
 	else:
 		output = response.choices[0].message.content # when Stream is set to False
 
-
-# %% [markdown]
-# ## Building the ChatBot with Gradio
-
-# %%
-# Helper function: format the prompt to include history
-def formatPrompt(newMsg:str, chatHistory, instruction):
+# Helper function: format the prompt to include history for fhe chatbot
+def chatBotFormatPrompt(newMsg:str, chatHistory, instruction):
 	
 	# start with the system prompt
 	messages = []
@@ -89,17 +79,17 @@ def formatPrompt(newMsg:str, chatHistory, instruction):
 	return messages
 
 # def the response function (to get the answer as one block after generation)
-def response(newMsg:str, chatHistory, instruction, temperature, max_tokens, model, stream=False):
-	prompt = formatPrompt(newMsg=newMsg, chatHistory=chatHistory, instruction=instruction)
-	response = APIcall(prompt=prompt, temperature=temperature, max_tokens=max_tokens, model=model)
+def responseChatBot(newMsg:str, chatHistory, instruction, temperature, max_tokens, model, stream=False):
+	prompt = chatBotFormatPrompt(newMsg=newMsg, chatHistory=chatHistory, instruction=instruction)
+	response = chatBotCompletionApiCall(prompt=prompt, temperature=temperature, max_tokens=max_tokens, model=model)
 	chatHistory.append([newMsg, response])
 	return "", chatHistory
 
 # def the streamResponse function, to stream the results as they are generated
-def streamResponse(newMsg:str, chatHistory, instruction, temperature, max_tokens, model, stream = True):
+def streamResponseChatBot(newMsg:str, chatHistory, instruction, temperature, max_tokens, model, stream = True):
 	chatHistory.append([newMsg, ""])
-	prompt = formatPrompt(newMsg=newMsg, chatHistory=chatHistory, instruction=instruction)
-	stream = APIcall(prompt=prompt, temperature=temperature, max_tokens=max_tokens, model=model)
+	prompt = chatBotFormatPrompt(newMsg=newMsg, chatHistory=chatHistory, instruction=instruction)
+	stream = chatBotCompletionApiCall(prompt=prompt, temperature=temperature, max_tokens=max_tokens, model=model)
 	for chunk in stream:
 		if chunk != None:
 			chatHistory[-1][1] += chunk
@@ -107,6 +97,26 @@ def streamResponse(newMsg:str, chatHistory, instruction, temperature, max_tokens
 		else:
 			return "", chatHistory
 
+# helper function for image generation
+def generateImageOpenAI(prompt, size = "1024x1024", quality = "standard", model = "dall-e-3", n=1):
+	'''
+	Make an API call to OpenAI's DALL-E model and return the generated image in PIL format
+	'''
+	print("request sent")
+	openAIresponse = openai.images.generate(model=model, prompt=prompt,size=size,quality=quality,n=n,)
+	image_url = openAIresponse.data[0].url
+
+	# get the image in Bytes format
+	imageResponse = requests.get(url=image_url)
+	imageBytes = imageResponse.content
+	
+	# convert it to PIL format
+	image = Image.open(BytesIO(imageBytes))
+
+	print("image received!")
+	# return the result
+	return image
+
 # Define some components
 model = gr.Dropdown(
 	choices=["GPT-3.5", "GPT-4"],
@@ -136,42 +146,122 @@ max_token = gr.Slider(
 	info="Maximum number of token the model will take into consideration"
 )
 
-# Build the app
-with gr.Blocks(theme='Insuz/Mocha') as app:
-	with gr.Row():
-		with gr.Column(scale = 8, elem_classes=["float-left"]):
-			gr.Markdown("# Private GPT")
-			gr.Markdown("This chatbot is powered by the openAI GPT series.\
-					The default model is `GPT-3.5`, but `GPT-4` can be selected in the advanced options.\
-					\nAs it uses the openAI API, user data is not used to train openAI models (see their official [website](https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance)).")
-			chatbot = gr.Chatbot() # Associated variable: chatHistory
-			msg = gr.Textbox(label="Message")
-			with gr.Row():
-				with gr.Column(scale=4):
-					Button = gr.Button(value="Submit")
-				with gr.Column(scale=4):
-					clearButton = gr.ClearButton([chatbot, msg])
-			msg.submit(
-				fn=streamResponse,
-				inputs=[msg, chatbot, instruction, temperature, max_token, model],
-				outputs=[msg, chatbot]
-			)
-			Button.click(
-				fn=streamResponse,
-				inputs=[msg, chatbot, instruction, temperature, max_token, model],
-				outputs=[msg, chatbot]
-			)
-		with gr.Column(scale = 1, elem_classes=["float-right"]):
-			with gr.Accordion(label="Advanced options", open=True):
-				model.render()
-				instruction.render()
-				temperature.render()
-				max_token.render()
-gr.close_all()
-app.queue().launch(auth=(AUTH_USERNAME, AUTH_PASSWORD))
-# app.queue().launch()
+# Components for Image generator 
+genImage = gr.Image(
+	label="Result", 
+	type="pil", 
+	render = False
+	) # Box for generated image
+
+# def helper function to update and render the component
+def generateAndRender(prompt:str, size, quality,):
+	'''
+	Send the request to the API endpoint and update the components. Outputs:
+	- oldPrompt
+	- genImage
+	- promptBox
+	'''
 
-# %%
+	# get the image
+	image = generateImageOpenAI(prompt, size, quality)
+	
+	# update the components
+	oldPrompt = gr.Textbox(value=prompt, label = "Your prompt", render=True)
+	genImage = gr.Image(value=image, label="Result", type="pil", render = True)
+	promptBox = gr.Textbox(label="Enter your prompt", lines=3)
+	
+	# return the components
+	return oldPrompt, genImage, promptBox
+
+# Build the app
+with gr.Blocks(theme='Insuz/Mocha', css="style.css") as app:
+	
+	# First tab: chatbot
+	with gr.Tab(label="ChatBot"):
+		with gr.Row():
+			with gr.Column(scale = 8, elem_classes=["float-left"]):
+				gr.Markdown("# Private GPT")
+				gr.Markdown("This chatbot is powered by the openAI GPT series.\
+						The default model is `GPT-3.5`, but `GPT-4` can be selected in the advanced options.\
+						\nAs it uses the openAI API, user data is not used to train openAI models (see their official [website](https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance)).")
+				chatbot = gr.Chatbot() # Associated variable: chatHistory
+				msg = gr.Textbox(label="Message")
+				with gr.Row():
+					with gr.Column(scale=4):
+						Button = gr.Button(value="Submit")
+					with gr.Column(scale=4):
+						clearButton = gr.ClearButton([chatbot, msg])
+				msg.submit(
+					fn=streamResponseChatBot,
+					inputs=[msg, chatbot, instruction, temperature, max_token, model],
+					outputs=[msg, chatbot]
+				)
+				Button.click(
+					fn=streamResponseChatBot,
+					inputs=[msg, chatbot, instruction, temperature, max_token, model],
+					outputs=[msg, chatbot]
+				)
+			with gr.Column(scale = 1, elem_classes=["float-right"]):
+				with gr.Accordion(label="Advanced options", open=True):
+					model.render()
+					instruction.render()
+					temperature.render()
+					max_token.render()
 
+	# Second Tab: image generation
+	with gr.Tab(label="Image Creation"):
+		# Title and description
+		gr.Markdown("# Image generation")
+		gr.Markdown("Powered by OpenAI's `DALL-E 3` Model under the hood.\n\
+				You can change the `size` as well as the `quality`.")
+		
+		# First row: prompt
+		with gr.Row():
+			prompt = gr.Textbox(label="Enter your prompt", lines=3)
+		
+		# Second row: allow for advanced customization
+		with gr.Accordion(label="Advanced option", open=False): # should not be visible by default
 
+			# Three columns of advanced options
+			with gr.Row():
+				with gr.Column():
+					size = gr.Dropdown(
+						choices = ["1024x1024", "1024x1792","1792x1024"],
+						value = "1024x1024",
+						info = "Choose the size of the image",
+					)
+				with gr.Column():
+					quality = gr.Dropdown(
+						choices = ["standard", "hd"],
+						value = "standard",
+						info="Define the quality of the image",
+					)
+				model = gr.Text(value="dall-e-3", render=False)
+				n = gr.Text(value=1, render=False)
+		
+		# Button
+		# Submit and clear
+		with gr.Row():
+			with gr.Column():
+				button = gr.Button(value="submit", min_width=30, )
+			with gr.Column():
+				clearImageButton = gr.ClearButton(components=[prompt, genImage])
+		
+		# Generated Image
+		genImage.render()
 
+		# Not rendered - logic of the app
+		button.click(
+			fn=generateImageOpenAI,
+			inputs=[prompt, size, quality],
+			outputs=[genImage],
+		)
+		prompt.submit(
+			fn=generateImageOpenAI,
+			inputs=[prompt, size, quality],
+			outputs=[genImage],
+		)
+		
+gr.close_all()
+# app.queue().launch(auth=(AUTH_USERNAME, AUTH_PASSWORD))
+app.queue().launch(share=False)

style.css

@@ -0,0 +1,3 @@
+footer {
+	visibility: hidden;
+}