import os from typing import Optional, Tuple import gradio as gr from threading import Lock # possibly needed for loading the environment variables locally. # not needed when hosted on hugging face if using HF secrets #from dotenv import load_dotenv #load_dotenv() from langchain.prompts.chat import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate) from langchain.chains import LLMChain from langchain.chat_models import ChatOpenAI # initialize the LLM as part of the conversatiion chain # temperature of 0.2 produces more creativity def load_chain(): """Logic for loading the chain you want to use should go here.""" template = os.getenv("CAPTION_PROMPT") system_message_prompt = SystemMessagePromptTemplate.from_template(template) human_template = "{text}" human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) #print(f"chat_prompt={chat_prompt}") llm = ChatOpenAI(temperature=0.2, model_name='gpt-3.5-turbo') chain = LLMChain(llm=llm, prompt=chat_prompt) return chain # set the api key and load conversation chain once when the api key changes in input box def set_openai_api_key(api_key: str): """Set the api key and return chain. If no api_key, then None is returned. """ if api_key: os.environ["OPENAI_API_KEY"] = api_key chain = load_chain() os.environ["OPENAI_API_KEY"] = "" return chain # load the hugging face image to text captioner from transformers import pipeline captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-base") import PIL # an image has been selected. it comes to this fn as a numpy ndarray # convert it to a PIL image and feed to the captioner # return the resulting caption # # NOTE: due to an error in gradio, this fn is triggered twice for each img change # only process the first fn call, and keep the first caption result async def image_supplied(img, count: int, last_cap: str): if img is None: return "", count, "" count += 1 if (count & 1) == 0: return last_cap, count, last_cap if img.any(): im = PIL.Image.fromarray(img) caption = captioner(im, max_new_tokens=20) result = caption[0]['generated_text'] #print(f"caption={result}") return result, count, result # class wrapping the chat class ChatWrapper: def __init__(self): self.lock = Lock() def __call__( self, api_key: str, inp: str, #history: Optional[Tuple[str, str]], chain: Optional[LLMChain] ): """Execute the chat functionality.""" self.lock.acquire() try: #history = history or [] # If chain is None, that is because no API key was provided by user. if chain is None: # attempt to load default rate limited key and initialize chain key = openai_api_key_textbox.value #print(key) chain = set_openai_api_key(key) # if chain is still None, the supplied key didn't work if chain is None: #history.append((inp, "Please paste your OpenAI key to use")) #last = history[-1][-1] # get last element as message returned #return last, history return "Please paste your OpenAI key to use" # Set OpenAI key import openai openai.api_key = api_key openai.api_type = 'open_ai' openai.api_base = 'https://api.openai.com/v1' # Run chain and append input. output = chain.run(inp) #history.append((inp, output)) last = output #history[-1][-1] # get last element of list, and then last of that vector except Exception as e: raise e finally: self.lock.release() return last #, history chat = ChatWrapper() # custom css css = """ .gradio-container {background-color: lightgray; background: url('file=./sd1.png'); background-size: cover} footer {visibility: hidden} """ font_name = "Kalam" block = gr.Blocks(title="📷 PunnyPix 📸", css=css, theme=gr.themes.Default( text_size = 'lg', font=[gr.themes.GoogleFont(font_name),"Arial","sans-serif"], spacing_size="sm", radius_size="sm")) # create app layout with block: with gr.Row(): with gr.Column(): gr.Markdown("

📷 PunnyPix 📸

") gr.Markdown("

Load image. Edit automated caption. Click 'Submit' to get a funny (hopefully) caption.

") openai_api_key_textbox = gr.Textbox( label="🔑 Default key is rate limited. Paste your OpenAI API key (sk-...)", placeholder="Paste your OpenAI API key (sk-...)", value = os.getenv("OPENAI_API_KEY"), # default to rate limited key lines=1, type="password" ) with gr.Row(): with gr.Column(): image_box = gr.Image(show_label=False) with gr.Row(): result_box = gr.Textbox(label="Original caption 🗨️", value="", interactive=True, lines=1, scale=3) submit = gr.Button(value="Submit", variant="secondary", size='sm', scale=1) #scale button at 1/3 size of two text boxes caption_box = gr.Textbox( label="Converted caption 🗯️", value="", lines=1, interactive=False, scale=3 ) #gr.Examples( # label="Sample images", # examples=[ # 'carolina.jpg', # 'house.jpg', # 'viceroy.jpg', # 'airplane.jpg', # 'swimming.jpg', # 'cats2.jpg', # 'car.jpg', # 'dogs.jpg', # 'cows2.jpg', # 'mountains.jpg' # ], # inputs=image_box #) gr.HTML( "
Powered by LangChain 🦜️🔗, Hugging Face transformers, OpenAI
" ) #state = gr.State() agent_state = gr.State() # once caption has been confirmed (either through enter in box or hitting "submit") # pass to the chat to process and get result (which goes into caption_box) submit.click(chat, inputs=[openai_api_key_textbox, result_box, agent_state], outputs=[caption_box]) result_box.submit(chat, inputs=[openai_api_key_textbox, result_box, agent_state], outputs=[caption_box]) #submit.click(chat, inputs=[openai_api_key_textbox, result_box, state, agent_state], outputs=[caption_box, state]) #result_box.submit(chat, inputs=[openai_api_key_textbox, result_box, state, agent_state], outputs=[caption_box, state]) count = gr.State(value=0) last_cap = gr.State(value="") # if image has changed, feed it to "image_supplied", and pass result to "result_box" image_box.change( image_supplied, inputs=[image_box, count, last_cap], outputs=[result_box, count, last_cap] ) # if api key in input box has changed, update the key in app openai_api_key_textbox.change( set_openai_api_key, inputs=[openai_api_key_textbox], outputs=[agent_state], ) block.launch(debug=True)