OpenAI_GPT_WUI.py

import openai
from openai import OpenAI

import streamlit as st
import extra_streamlit_components as stx
from streamlit_extras.stoggle import stoggle

from PIL import Image
import base64
import io
import math

import json
import copy

import os.path
import tempfile

import common_functions as cf
import common_functions_WUI as cfw

import OpenAI_GPT as OAI_GPT

##########
class OAI_GPT_WUI:
    def __init__(self, oai_gpt: OAI_GPT, enable_vision: bool = True, prompt_presets_dir: str = None, prompt_presets_file: str = None) -> None:
        self.last_gpt_query = "last_gpt_query"

        self.oai_gpt = oai_gpt
        self.save_location = oai_gpt.get_save_location()
        self.models = oai_gpt.get_models()
        self.model_help = oai_gpt.get_model_help()
        self.models_status = oai_gpt.get_models_status()
        self.model_capability = oai_gpt.get_model_capability()
        self.gpt_roles = oai_gpt.get_gpt_roles()
        self.gpt_roles_help = oai_gpt.get_gpt_roles_help()
        self.gpt_presets = oai_gpt.get_gpt_presets()
        self.gpt_presets_help = oai_gpt.get_gpt_presets_help()
        self.per_model_help = oai_gpt.get_per_model_help()
        self.beta_models = oai_gpt.get_beta_models()

        self.enable_vision = enable_vision

        self.prompt_presets_dir = prompt_presets_dir
        self.prompt_presets = {}

        self.prompt_presets_file = prompt_presets_file
        self.prompt_presets_settings = {}

        err = self.load_prompt_presets()
        if cf.isNotBlank(err):
            st.error(err)
            cf.error_exit(err)


    def resize_rectangle(self, original_width, original_height, max_width, max_height):
        aspect_ratio = original_width / original_height
        max_area = max_width * max_height
        original_area = original_width * original_height

        # Calculate scaling factor for proportional fit
        scale_factor = math.sqrt(max_area / original_area)

        # Scale the dimensions
        new_width = original_width * scale_factor
        new_height = original_height * scale_factor

        # Check if resizing would make the rectangle larger
        if new_width >= original_width or new_height >= original_height:
            return original_width, original_height
    
        # Adjust if necessary to fit within max dimensions
        if new_width > max_width:
            new_width = max_width
            new_height = new_width / aspect_ratio

        if new_height > max_height:
            new_height = max_height
            new_width = new_height * aspect_ratio

        return new_width, new_height

    def img_resize_core(self, im, max_x, max_y):
        new_x, new_y = self.resize_rectangle(im.size[0], im.size[1], max_x, max_y)
        return int(new_x), int(new_y)

    def img_resize(self, tfilen, details_selection):
        with Image.open(tfilen) as im:
            new_x, new_y = im_x, im_y = im.size[0], im.size[1]
            if details_selection == "low":
                new_x, new_y = self.img_resize_core(im, 512, 512)
            else:
                new_x, new_y = self.img_resize_core(im, 2048, 2048)

            if new_x == im_x and new_y == im_y:
                return new_x, new_y

            im = im.resize((new_x, new_y))
            im.save(tfilen, format="png")
            return new_x, new_y

    def file_uploader(self, details_selection):
        # File uploader: [OpenAI supports] PNG (.png), JPEG (.jpeg and .jpg), WEBP (.webp), and non-animated GIF (.gif).
        uploaded_file = st.file_uploader("Upload a PNG/JPEG/WebP image (automatic resize to a value closer to the selected \"details\" selected, see its \"?\")", type=['png','jpg','jpeg','webp'])
        if uploaded_file is not None:
            placeholder = st.empty()
            tfile = tempfile.NamedTemporaryFile(delete=False)
            tfilen = str(tfile.name)
            with open(tfilen, "wb") as outfile:
                outfile.write(uploaded_file.getvalue())

            # confirm it is a valid PNG, JPEG, WEBP image
            im_fmt = None
            im_det = None
            im_area = None
            try:
                with Image.open(tfilen) as im:
                    im_fmt = im.format
                    im_det = f"{im.size[0]}x{im.size[1]}"
            except OSError:
                pass

            if im_fmt == "PNG" or im_fmt == "JPEG" or im_fmt == "WEBP":
                im_x, im_y = self.img_resize(tfilen, details_selection)
                tk_cst = (170 * im_x * im_y) // (512*512) + 85
                tk_cst = 1105 if tk_cst > 1105 else tk_cst # following the details of "Calculating costs"
                n_img_det = f"{im_x}x{im_y}"
                res_txt = f"resized to: {n_img_det} " if n_img_det != im_det else ""
                tkn_txt = f"(est. token cost -- \"high\": {tk_cst} | \"low\": 85)"
                placeholder.info(f"Uploaded image: {im_fmt} orig size: {im_det} {res_txt} {tkn_txt}")
                return tfilen
            else:
                placeholder.error(f"Uploaded image ({im_fmt}) is not a valid/supported PNG, JPEG, or WEBP image")
                return None

        return None

#####

    def load_prompt_presets(self):
        if self.prompt_presets_dir is None:
            return ""

        prompt_presets = {}
        for file in os.listdir(self.prompt_presets_dir):
            if file.endswith(".json"):
                err = cf.check_file_r(os.path.join(self.prompt_presets_dir, file))
                if cf.isNotBlank(err):
                    return err
                with open(os.path.join(self.prompt_presets_dir, file), "r") as f:
                    prompt_presets[file.split(".json")[0]] = json.load(f)

        self.prompt_presets = prompt_presets

        if self.prompt_presets_file is not None:
            err = cf.check_file_r(self.prompt_presets_file)
            if cf.isNotBlank(err):
                return err
            with open(self.prompt_presets_file, "r") as f:
                self.prompt_presets_settings = json.load(f)
            if 'model' not in self.prompt_presets_settings:
                return f"Could not find 'model' in {self.prompt_presets_file}"
            model = self.prompt_presets_settings['model']
            if model not in self.models:
                return f"Could not find requested 'model' ({model}) in available models: {list(self.models.keys())} (from {self.prompt_presets_file})"
            if 'tokens' not in self.prompt_presets_settings:
                return f"Could not find 'tokens' in {self.prompt_presets_file}"
            tmp = self.prompt_presets_settings['tokens']
            if tmp is None:
                return f"Invalid 'tokens' ({tmp}) in {self.prompt_presets_file}"
            if tmp <= 0:
                return f"Invalid 'tokens' ({tmp}) in {self.prompt_presets_file}"
            if tmp > self.models[model]['max_token']:
                return f"Requested 'tokens' ({tmp}) is greater than model's 'max_token' ({self.models[model]['max_token']}) in {self.prompt_presets_file}"
            if 'temperature' not in self.prompt_presets_settings:
                return f"Could not find 'temperature' in {self.prompt_presets_file}"
            tmp = self.prompt_presets_settings['temperature']
            if tmp is None:
                return f"Invalid 'temperature' ({tmp}) in {self.prompt_presets_file}"
            if tmp < 0:
                return f"Invalid 'temperature' ({tmp}) in {self.prompt_presets_file}"
            if tmp > 1:
                return f"Invalid 'temperature' ({tmp}) in {self.prompt_presets_file}"

        return ""


#####
    def set_ui(self):
        st.sidebar.empty()
        vision_capable = False
        vision_mode = False
        disable_preset_prompts = False
        clear_chat = False
        prompt_preset = None
        msg_extra = None
        beta_model = False

        if 'gpt_last_prompt' in st.session_state:
            if st.session_state['gpt_last_prompt'] != "":
                disable_preset_prompts = True

        with st.sidebar:
            st.text("Check the various ? for help", help=f"[Run Details]\n\nRunID: {cfw.get_runid()}\n\nSave location: {self.save_location}\n\nUTC time: {cf.get_timeUTC()}\n")

            if st.button("Clear Chat"):
                clear_chat = True
                st.session_state['gpt_last_prompt'] = ''
                if self.last_gpt_query in st.session_state:
                    del st.session_state[self.last_gpt_query]
                disable_preset_prompts = False
                st.session_state['gpt_clear_chat'] = True
                if 'gpt_msg_extra' in st.session_state:
                    del st.session_state['gpt_msg_extra'] # only a clear will allow us to set msg_extra again

            # create a location placeholder for the prompt preset selector
            st_preset_placeholder = st.empty()

            if self.prompt_presets_settings == {}:
                # Only available if not in "preset only" mode
                model = st.selectbox("model", options=list(self.models.keys()), index=0, key="model", help=self.model_help)
                if model in self.models_status:
                    st.info(f"{model}: {self.models_status[model]}")
                if self.model_capability[model] == "vision":
                    vision_capable = True
                if model in self.beta_models and self.beta_models[model] is True:
                    beta_model = True
                m_token = self.models[model]['max_token']

                # vision mode bypass
                if self.enable_vision is False:
                    vision_mode = False
                    vision_capable = False

                if vision_capable:
                    vision_mode = st.toggle(label="Vision", value=False, help="Enable the upload of an image. Vision's limitation and cost can be found at https://platform.openai.com/docs/guides/vision/limitations.\n\nDisables the role and presets selectors. Image(s) are resized when over the max of the \'details\' selected. Please be aware that each 512px x 512px title is expected to cost 170 tokens. Using this mode disables roles, presets and chat (the next prompt will not have knowledge of past thread of conversation)")

                if vision_mode:
                    vision_details = st.selectbox("Vision Details", options=["auto", "low", "high"], index=0, key="vision_details", help="The model will use the auto setting which will look at the image input size and decide if it should use the low or high setting.\n\n- low: the model will receive a low-res 512px x 512px version of the image, and represent the image with a budget of 85 tokens. This allows the API to return faster responses and consume fewer input tokens for use cases that do not require high detail.\n\n- high will first allows the model to first see the low res image (using 85 tokens) and then creates detailed crops using 170 tokens for each 512px x 512px tile.\n\n\n\nImage inputs are metered and charged in tokens, just as text inputs are. The token cost of a given image is determined by two factors: its size, and the detail option on each image_url block. All images with detail: low cost 85 tokens each. detail: high images are first scaled to fit within a 2048 x 2048 square, maintaining their aspect ratio. Then, they are scaled such that the shortest side of the image is 768px long. Finally, a count of how many 512px squares the image consists of is performed. Each of those squares costs 170 tokens. Another 85 tokens are always added to the final total. More details at https://platform.openai.com/docs/guides/vision/calculating-costs")

                role = list(self.gpt_roles.keys())[0]
                if vision_mode is False:
                    tmp_roles = self.gpt_roles.copy()
                    if beta_model is True:
                        tmp_roles.pop("system")
                    tmp_txt = "" if beta_model is False else "\n\nBeta models do not support the 'system' role"
                    role = st.selectbox("Role", options=tmp_roles, index=0, key="input_role", help = "Role of the input text\n\n" + self.gpt_roles_help + tmp_txt)

                if beta_model is False:
                    max_tokens = st.slider('max_tokens', 0, m_token, 1000, 100, "%i", "max_tokens", "The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model\'s context length.")
                else:
                    max_tokens = st.slider('max_completion_tokens', 0, m_token, 4000, 100, "%i", "max_completion_tokens", "For beta models: control the total number of tokens generated by the model, including both reasoning and visible completion tokens. If the token value is too low, the answer might be empty")

                temperature = 0.5
                if beta_model is False:
                    temperature = st.slider('temperature', 0.0, 1.0, 0.5, 0.01, "%0.2f", "temperature", "The temperature of the model. Higher temperature results in more surprising text.")

                if vision_mode is False and beta_model is False:
                    presets = st.selectbox("GPT Task", options=list(self.gpt_presets.keys()), index=0, key="presets", help=self.gpt_presets_help)
                else:
                    presets = list(self.gpt_presets.keys())[0]
            
            else: # "preset only" mode
                model = self.prompt_presets_settings['model']
                max_tokens = self.prompt_presets_settings['tokens']
                temperature = self.prompt_presets_settings['temperature']
                presets = list(self.gpt_presets.keys())[0]
                role = list(self.gpt_roles.keys())[0]

            # use the location of the placeholder now that we have the vision settings
            if self.prompt_presets_dir is not None:
                prompt_preset = st_preset_placeholder.selectbox("Prompt preset", options=list(self.prompt_presets.keys()), index=None, key="prompt_preset", help="Load a prompt preset. Can only be used with new chats.", disabled=disable_preset_prompts)
                if prompt_preset is not None:
                    if prompt_preset not in self.prompt_presets:
                        st_preset_placeholder.warning(f"Unkown {prompt_preset}")
                    else:
                        if 'messages' in self.prompt_presets[prompt_preset]:
                            if 'gpt_msg_extra' not in st.session_state:
                                msg_extra = self.prompt_presets[prompt_preset]["messages"]
                                st.session_state['gpt_msg_extra'] = msg_extra
                                # msg_extra is also set for vision mode but this check is only needed if not in vision mode to avoid passing the msg_extra each time
                                st.session_state['gpt_clear_chat'] = True
                                # clear the chat history in the GPT call as well
                else:
                    if 'gpt_msg_extra' in st.session_state:
                        del st.session_state['gpt_msg_extra']


            gpt_show_tooltip = st.toggle(label="Show Tips", value=False, help="Show some tips on how to use the tool", key="gpt_show_tooltip")
            gpt_show_history = st.toggle(label='Show Prompt History', value=False, help="Show a list of prompts that you have used in the past (most recent first). Loading a selected prompt does not load the parameters used for the generation.", key="gpt_show_history")
            if gpt_show_history:
                gpt_allow_history_deletion = st.toggle('Allow Prompt History Deletion', value=False, help="This will allow you to delete a prompt from the history. This will delete the prompt and all its associated files. This cannot be undone.", key="gpt_allow_history_deletion")


        # Main window
        if gpt_show_tooltip:
            stoggle('Tips', 'GPT provides a simple but powerful interface to any models. You input some text as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it:<br>- The tool works on text to: answer questions, provide definitions, translate, summarize, and analyze sentiments.<br>- Keep your prompts clear and specific. The tool works best when it has a clear understanding of what you\'re asking it, so try to avoid vague or open-ended prompts.<br>- Use complete sentences and provide context or background information as needed.<br>- Some presets are available in the sidebar, check their details for more information.<br>A few example prompts (to use with "None" preset):<br>- Create a list of 8 questions for a data science interview<br>- Generate an outline for a blog post on MFT<br>- Translate "bonjour comment allez vous" in 1. English 2. German 3. Japanese<br>- write python code to display with an image selector from a local directory using OpenCV<br>- Write a creative ad and find a name  for a container to run machine learning and computer vision algorithms by providing access to many common ML frameworks<br>- some models support "Chat" conversations. If you see the "Clear Chat" button, this will be one such model. They also support different max tokens, so adapt accordingly. The "Clear Chat" is here to allow you to start a new "Chat". Chat models can be given writing styles using the "system" "role"<br>More examples and hints can be found at https://platform.openai.com/examples')

        if gpt_show_history:
            err, hist = self.oai_gpt.get_history()
            if cf.isNotBlank(err):
                st.error(err)
                cf.error_exit(err)
            if len(hist) == 0:
                st.warning("No prompt history found")
            else:
                cfw.show_history(hist, gpt_allow_history_deletion, 'gpt_last_prompt', self.last_gpt_query)

        if 'gpt_last_prompt' not in st.session_state:
            st.session_state['gpt_last_prompt'] = ''

        prompt_value=f"GPT ({model}) Input (role: {role}) [max_tokens: {max_tokens} "
        if beta_model is False:
            prompt_value += f"| temperature: {temperature}"
        if vision_mode:
            prompt_value += f" | vision details: {vision_details}"
        if beta_model is False:
            prompt_value += f" | preset: {presets}"
        if prompt_preset is not None:
            prompt_value += f" | prompt preset: {prompt_preset}"
        if 'gpt_clear_chat' in st.session_state or clear_chat is True:
            prompt_value += " | Clear Chat"
        prompt_value += f" ]"

        help_text = self.per_model_help[model] if model in self.per_model_help else "No help available for this model"
        prompt = st.empty().text_area(prompt_value, st.session_state['gpt_last_prompt'], placeholder="Enter your prompt", key="input", help=help_text)
        st.session_state['gpt_last_prompt'] = prompt

        img_file = None
        if vision_mode:
            img_file = self.file_uploader(vision_details)
            img_type = "png" # convert everything to PNG for processing
            if img_file is not None:
                img_b64 = None
                img_bytes = io.BytesIO()
                with Image.open(img_file) as image:
                    image.save(img_bytes, format=img_type)
                    img_b64 = base64.b64encode(img_bytes.getvalue()).decode('utf-8')
                if img_b64 is not None:
                    img_str = f"data:image/{img_type};base64,{img_b64}"
                    msg_extra = [ 
                        { 
                            "role": "user", 
                            "content": [
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": img_str,
                                        "details": vision_details
                                    }
                                }
                            ],
                            "oaiwui_skip": True
                        }
                    ]
                if os.path.exists(img_file):
                    os.remove(img_file)

        if st.button("Request Answer", key="request_answer"):
            if cf.isBlank(prompt) or len(prompt) < 10:
                st.error("Please provide a prompt of at least 10 characters before requesting an answer", icon="✋")
                return ()

            prompt = self.gpt_presets[presets]["pre"] + prompt + self.gpt_presets[presets]["post"]
            prompt_token_count = self.oai_gpt.estimate_tokens(prompt)
            requested_token_count = prompt_token_count + max_tokens
            if requested_token_count > self.models[model]["context_token"]:
                st.warning("You requested an estimated %i tokens, which might exceed the model's context window of %i tokens. We are still proceeding with the request, but an error return is possible." % (requested_token_count, self.models[model]["context_token"]))

            if max_tokens > 0:
                tmp_txt = "" if beta_model is True else f" (temperature: {temperature})"
                st.toast(f"Requesting OpenAI ({model} for {max_tokens} tokens{tmp_txt})")
                with st.spinner(f"Asking OpenAI ({model} for {max_tokens} tokens{tmp_txt}. Prompt est. tokens : {prompt_token_count})"):
                    if 'gpt_clear_chat' in st.session_state:
                        clear_chat = True
                        del st.session_state['gpt_clear_chat']
                    if msg_extra is None:
                        if 'gpt_msg_extra' in st.session_state:
                            msg_extra = st.session_state['gpt_msg_extra']
                    else:
                        if 'gpt_msg_extra' in st.session_state:
                            tmp = msg_extra
                            msg_extra = copy.deepcopy(st.session_state['gpt_msg_extra'])
                            msg_extra.append(tmp[0])
                    err, run_file = self.oai_gpt.chatgpt_it(model, prompt, max_tokens, temperature, clear_chat, role, msg_extra, **self.gpt_presets[presets]["kwargs"])
                    if cf.isNotBlank(err):
                        st.error(err)
                    if cf.isNotBlank(run_file):
                        st.session_state[self.last_gpt_query] = run_file
                        st.toast("Done")

        if self.last_gpt_query in st.session_state:
            run_file = st.session_state[self.last_gpt_query]
            run_json = cf.get_run_file(run_file)

            prompt = run_json["prompt"]
            response = run_json["response"]

            chat_history = self.oai_gpt.get_chat_history(run_file)
            if vision_mode is False:
                stoggle('Original Prompt', prompt)
                stoggle('Chat History', chat_history)

            option_list = ('Text (no wordwrap)', 'Text (wordwrap, may cause some visual inconsistencies)',
                        'Code (automatic highlighting for supported languages)')
            option = st.selectbox('Display mode:', option_list)

            if option == option_list[0]:
                st.text(response)
            elif option == option_list[1]:
                st.markdown(response)
            elif option == option_list[2]:
                st.code(response)
            else:
                st.error("Unknown display mode")

            query_output = prompt + "\n\n--------------------------\n\n" + response
            col1, col2, col3 = st.columns(3)
            col1.download_button(label="Download Latest Result", data=response)
            col2.download_button(label="Download Latest Query+Result", data=query_output)
            col3.download_button(label="Download Chat Query+Result", data=chat_history)