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.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,14 @@
 ---
-title: AIN Arabic VLM
-emoji: 🏢
-colorFrom: blue
-colorTo: gray
+title: AIN-Arabic-VLM
+app_file: gradio_app.py
 sdk: gradio
-sdk_version: 5.13.1
-app_file: app.py
+sdk_version: 5.13.0
+license: mit
+colorFrom: blue
+colorTo: red
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+emoji: 🌖
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/656864e12d73834278a8dea7/pNXWle55Sbsg7xjdYijzC.jpeg
+short_description: The best Arabic-English VLM developed by MBZUAI.
+---

app.py

@@ -0,0 +1,72 @@
+from openai import OpenAI
+from flask import Flask, request
+import requests
+from loguru import logger
+
+app = Flask(__name__)
+
+BOT_TOKEN = "8003439194:AAGQNjq3dQSZi4Ztv2Z0yyZx1ZnD0L5MR5o"
+TELEGRAM_API_URL = f"https://api.telegram.org/bot{BOT_TOKEN}"
+
+client = OpenAI(
+    api_key="0",
+    base_url="https://0f21-5-195-0-150.ngrok-free.app/v1",
+)
+
+@app.route("/test")
+def test():
+    return "Hello, World!"
+
+DEFAULT_CAPTION = "Please provide a caption for the image."
+@app.route(f"/{BOT_TOKEN}", methods=["POST"])
+def telegram_webhook():
+    data = request.json
+    chat_id = data["message"]["chat"]["id"]
+    messages: list[dict] = [{
+        "role": "user",
+        "content": []
+    }]
+    logger.debug(f"Received message: {data}")
+    if "text" in data["message"]:
+        user_message = data["message"]["text"]
+        messages[0]['content'].append({"type": "text", "text": user_message})
+
+    if "photo" in data["message"]:
+        file_id = data["message"]["photo"][-1]["file_id"]
+        file_info = requests.get(f"{TELEGRAM_API_URL}/getFile?file_id={file_id}").json()
+        file_path = file_info["result"]["file_path"]
+        image_url = f"https://api.telegram.org/file/bot{BOT_TOKEN}/{file_path}"
+        messages[0]['content'].append({
+                    "type": "image_url",
+                    "image_url": {"url": image_url},
+                })
+        if "caption" in data["message"]:
+            messages[0]['content'].append({"type": "text", "text": data["message"]["caption"]})
+        else:
+            messages[0]['content'].append({"type": "text", "text": DEFAULT_CAPTION})
+
+    if "text" not in data['message'] and "photo" not in data['message']:
+        send_message(chat_id, "Unsupported message type. Please send text or an image.")
+        return "ok", 200
+
+    try:
+        logger.debug(f"Sending message to OpenAI: {messages}")
+        result = client.chat.completions.create(messages=messages, model="test")
+        response_text = result.choices[0].message.content
+    except Exception as e:
+        logger.error(f"Error processing message: {e}")
+        response_text = "There was an error processing your request."
+
+    send_message(chat_id, response_text)
+    return "ok", 200
+
+
+def send_message(chat_id, text):
+    url = f"{TELEGRAM_API_URL}/sendMessage"
+    payload = {"chat_id": chat_id, "text": text}
+    requests.post(url, json=payload)
+
+
+if __name__ == "__main__":
+    app.run(port=5000)
+

call.py

@@ -0,0 +1,26 @@
+from openai import OpenAI
+
+def main():
+    client = OpenAI(
+        api_key="0",
+        base_url="https://0f21-5-195-0-150.ngrok-free.app/v1",
+    )
+    messages = []
+    messages.append(
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Output the color and number of each box."},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/boxes.png"},
+                },
+            ],
+        }
+    )
+    result = client.chat.completions.create(messages=messages, model="test")
+    messages.append(result.choices[0].message)
+    print(result.choices[0].message)
+
+if __name__ == "__main__":
+    main()

chat_interface.py

@@ -0,0 +1,1087 @@
+"""
+This file defines a useful high-level abstraction to build Gradio chatbots: ChatInterface.
+"""
+
+from __future__ import annotations
+
+import builtins
+import copy
+import dataclasses
+import inspect
+import os
+import warnings
+from collections.abc import AsyncGenerator, Callable, Generator, Sequence
+from pathlib import Path
+from typing import Literal, Union, cast
+
+import anyio
+from gradio_client.documentation import document
+
+from gradio import utils
+from gradio.blocks import Blocks
+from gradio.components import (
+    JSON,
+    BrowserState,
+    Button,
+    Chatbot,
+    Component,
+    Dataset,
+    Markdown,
+    MultimodalTextbox,
+    State,
+    Textbox,
+    get_component_instance,
+)
+from gradio.components.chatbot import (
+    ChatMessage,
+    ExampleMessage,
+    Message,
+    MessageDict,
+    TupleFormat,
+)
+from gradio.components.multimodal_textbox import MultimodalPostprocess, MultimodalValue
+from gradio.context import get_blocks_context
+from gradio.events import Dependency, EditData, SelectData
+from gradio.flagging import ChatCSVLogger
+from gradio.helpers import create_examples as Examples  # noqa: N812
+from gradio.helpers import special_args, update
+from gradio.layouts import Accordion, Column, Group, Row
+from gradio.routes import Request
+from gradio.themes import ThemeClass as Theme
+
+
+@document()
+class ChatInterface(Blocks):
+    """
+    ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create
+    a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which
+    takes a function that governs the response of the chatbot based on the user input and chat history. Additional
+    parameters can be used to control the appearance and behavior of the demo.
+
+    Example:
+        import gradio as gr
+
+        def echo(message, history):
+            return message
+
+        demo = gr.ChatInterface(fn=echo, type="messages", examples=[{"text": "hello", "text": "hola", "text": "merhaba"}], title="Echo Bot")
+        demo.launch()
+    Demos: chatinterface_random_response, chatinterface_streaming_echo, chatinterface_artifacts
+    Guides: creating-a-chatbot-fast, sharing-your-app
+    """
+
+    def __init__(
+        self,
+        fn: Callable,
+        *,
+        multimodal: bool = False,
+        type: Literal["messages", "tuples"] | None = None,
+        chatbot: Chatbot | None = None,
+        textbox: Textbox | MultimodalTextbox | None = None,
+        additional_inputs: str | Component | list[str | Component] | None = None,
+        additional_inputs_accordion: str | Accordion | None = None,
+        additional_outputs: Component | list[Component] | None = None,
+        editable: bool = False,
+        examples: list[str] | list[MultimodalValue] | list[list] | None = None,
+        example_labels: list[str] | None = None,
+        example_icons: list[str] | None = None,
+        run_examples_on_click: bool = True,
+        cache_examples: bool | None = None,
+        cache_mode: Literal["eager", "lazy"] | None = None,
+        title: str | None = None,
+        description: str | None = None,
+        theme: Theme | str | None = None,
+        flagging_mode: Literal["never", "manual"] | None = None,
+        flagging_options: list[str] | tuple[str, ...] | None = ("Like", "Dislike"),
+        flagging_dir: str = ".gradio/flagged",
+        css: str | None = None,
+        css_paths: str | Path | Sequence[str | Path] | None = None,
+        js: str | None = None,
+        head: str | None = None,
+        head_paths: str | Path | Sequence[str | Path] | None = None,
+        analytics_enabled: bool | None = None,
+        autofocus: bool = True,
+        autoscroll: bool = True,
+        submit_btn: str | bool | None = True,
+        stop_btn: str | bool | None = True,
+        concurrency_limit: int | None | Literal["default"] = "default",
+        delete_cache: tuple[int, int] | None = None,
+        show_progress: Literal["full", "minimal", "hidden"] = "minimal",
+        fill_height: bool = True,
+        fill_width: bool = False,
+        api_name: str | Literal[False] = "chat",
+        save_history: bool = False,
+        logo: str | None = None,
+    ):
+        """
+        Parameters:
+            fn: the function to wrap the chat interface around. Normally (assuming `type` is set to "messages"), the function should accept two parameters: a `str` representing the input message and `list` of openai-style dictionaries: {"role": "user" | "assistant", "content": `str` | {"path": `str`} | `gr.Component`} representing the chat history. The function should return/yield a `str` (for a simple message), a supported Gradio component (e.g. gr.Image to return an image), a `dict` (for a complete openai-style message response), or a `list` of such messages.
+            multimodal: if True, the chat interface will use a `gr.MultimodalTextbox` component for the input, which allows for the uploading of multimedia files. If False, the chat interface will use a gr.Textbox component for the input. If this is True, the first argument of `fn` should accept not a `str` message but a `dict` message with keys "text" and "files"
+            type: The format of the messages passed into the chat history parameter of `fn`. If "messages", passes the history as a list of dictionaries with openai-style "role" and "content" keys. The "content" key's value should be one of the following - (1) strings in valid Markdown (2) a dictionary with a "path" key and value corresponding to the file to display or (3) an instance of a Gradio component: at the moment gr.Image, gr.Plot, gr.Video, gr.Gallery, gr.Audio, and gr.HTML are supported. The "role" key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output. If this parameter is 'tuples' (deprecated), passes the chat history as a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message.
+            chatbot: an instance of the gr.Chatbot component to use for the chat interface, if you would like to customize the chatbot properties. If not provided, a default gr.Chatbot component will be created.
+            textbox: an instance of the gr.Textbox or gr.MultimodalTextbox component to use for the chat interface, if you would like to customize the textbox properties. If not provided, a default gr.Textbox or gr.MultimodalTextbox component will be created.
+            editable: if True, users can edit past messages to regenerate responses.
+            additional_inputs: an instance or list of instances of gradio components (or their string shortcuts) to use as additional inputs to the chatbot. If the components are not already rendered in a surrounding Blocks, then the components will be displayed under the chatbot, in an accordion. The values of these components will be passed into `fn` as arguments in order after the chat history.
+            additional_inputs_accordion: if a string is provided, this is the label of the `gr.Accordion` to use to contain additional inputs. A `gr.Accordion` object can be provided as well to configure other properties of the container holding the additional inputs. Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This parameter is only used if `additional_inputs` is provided.
+            additional_outputs: an instance or list of instances of gradio components to use as additional outputs from the chat function. These must be components that are already defined in the same Blocks scope. If provided, the chat function should return additional values for these components. See $demo/chatinterface_artifacts.
+            examples: sample inputs for the function; if provided, appear within the chatbot and can be clicked to populate the chatbot input. Should be a list of strings representing text-only examples, or a list of dictionaries (with keys `text` and `files`) representing multimodal examples. If `additional_inputs` are provided, the examples must be a list of lists, where the first element of each inner list is the string or dictionary example message and the remaining elements are the example values for the additional inputs -- in this case, the examples will appear under the chatbot.
+            example_labels: labels for the examples, to be displayed instead of the examples themselves. If provided, should be a list of strings with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided).
+            example_icons: icons for the examples, to be displayed above the examples. If provided, should be a list of string URLs or local paths with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided).
+            cache_examples: if True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
+            cache_mode: if "eager", all examples are cached at app launch. If "lazy", examples are cached for all users after the first use by any user of the app. If None, will use the GRADIO_CACHE_MODE environment variable if defined, or default to "eager".
+            run_examples_on_click: if True, clicking on an example will run the example through the chatbot fn and the response will be displayed in the chatbot. If False, clicking on an example will only populate the chatbot input with the example message. Has no effect if `cache_examples` is True
+            title: a title for the interface; if provided, appears above chatbot in large font. Also used as the tab title when opened in a browser window.
+            description: a description for the interface; if provided, appears above the chatbot and beneath the title in regular font. Accepts Markdown and HTML content.
+            theme: a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the Hugging Face Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
+            flagging_mode: one of "never", "manual". If "never", users will not see a button to flag an input and output. If "manual", users will see a button to flag.
+            flagging_options: a list of strings representing the options that users can choose from when flagging a message. Defaults to ["Like", "Dislike"]. These two case-sensitive strings will render as "thumbs up" and "thumbs down" icon respectively next to each bot message, but any other strings appear under a separate flag icon.
+            flagging_dir: path to the the directory where flagged data is stored. If the directory does not exist, it will be created.
+            css: Custom css as a code string. This css will be included in the demo webpage.
+            css_paths: Custom css as a pathlib.Path to a css file or a list of such paths. This css files will be read, concatenated, and included in the demo webpage. If the `css` parameter is also set, the css from `css` will be included first.
+            js: Custom js as a code string. The custom js should be in the form of a single js function. This function will automatically be executed when the page loads. For more flexibility, use the head parameter to insert js inside <script> tags.
+            head: Custom html code to insert into the head of the demo webpage. This can be used to add custom meta tags, multiple scripts, stylesheets, etc. to the page.
+            head_paths: Custom html code as a pathlib.Path to a html file or a list of such paths. This html files will be read, concatenated, and included in the head of the demo webpage. If the `head` parameter is also set, the html from `head` will be included first.
+            analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True.
+            autofocus: if True, autofocuses to the textbox when the page loads.
+            autoscroll: If True, will automatically scroll to the bottom of the chatbot when a new message appears, unless the user scrolls up. If False, will not scroll to the bottom of the chatbot automatically.
+            submit_btn: If True, will show a submit button with a submit icon within the textbox. If a string, will use that string as the submit button text in place of the icon. If False, will not show a submit button.
+            stop_btn: If True, will show a button with a stop icon during generator executions, to stop generating. If a string, will use that string as the submit button text in place of the stop icon. If False, will not show a stop button.
+            concurrency_limit: if set, this is the maximum number of chatbot submissions that can be running simultaneously. Can be set to None to mean no limit (any number of chatbot submissions can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `.queue()`, which is 1 by default).
+            delete_cache: a tuple corresponding [frequency, age] both expressed in number of seconds. Every `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. For example, setting this to (86400, 86400) will delete temporary files every day. The cache will be deleted entirely when the server restarts. If None, no cache deletion will occur.
+            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
+            fill_height: if True, the chat interface will expand to the height of window.
+            fill_width: Whether to horizontally expand to fill container fully. If False, centers and constrains app to a maximum width.
+            api_name: the name of the API endpoint to use for the chat interface. Defaults to "chat". Set to False to disable the API endpoint.
+            save_history: if True, will save the chat history to the browser's local storage and display previous conversations in a side panel.
+        """
+        super().__init__(
+            analytics_enabled=analytics_enabled,
+            mode="chat_interface",
+            title=title or "Gradio",
+            theme=theme,
+            css=css,
+            css_paths=css_paths,
+            js=js,
+            head=head,
+            head_paths=head_paths,
+            fill_height=fill_height,
+            fill_width=fill_width,
+            delete_cache=delete_cache,
+        )
+        self.api_name = api_name
+        self.type = type
+        self.multimodal = multimodal
+        self.concurrency_limit = concurrency_limit
+        if isinstance(fn, ChatInterface):
+            self.fn = fn.fn
+        else:
+            self.fn = fn
+        self.is_async = inspect.iscoroutinefunction(
+            self.fn
+        ) or inspect.isasyncgenfunction(self.fn)
+        self.is_generator = inspect.isgeneratorfunction(
+            self.fn
+        ) or inspect.isasyncgenfunction(self.fn)
+        self.provided_chatbot = chatbot is not None
+        self.examples = examples
+        self.examples_messages = self._setup_example_messages(
+            examples, example_labels, example_icons
+        )
+        self.run_examples_on_click = run_examples_on_click
+        self.cache_examples = cache_examples
+        self.cache_mode = cache_mode
+        self.editable = editable
+        self.fill_height = fill_height
+        self.autoscroll = autoscroll
+        self.autofocus = autofocus
+        self.title = title
+        self.description = description
+        self.logo = logo
+        self.show_progress = show_progress
+        if save_history and not type == "messages":
+            raise ValueError("save_history is only supported for type='messages'")
+        self.save_history = save_history
+        self.additional_inputs = [
+            get_component_instance(i)
+            for i in utils.none_or_singleton_to_list(additional_inputs)
+        ]
+        self.additional_outputs = utils.none_or_singleton_to_list(additional_outputs)
+        if additional_inputs_accordion is None:
+            self.additional_inputs_accordion_params = {
+                "label": "Additional Inputs",
+                "open": False,
+            }
+        elif isinstance(additional_inputs_accordion, str):
+            self.additional_inputs_accordion_params = {
+                "label": additional_inputs_accordion
+            }
+        elif isinstance(additional_inputs_accordion, Accordion):
+            self.additional_inputs_accordion_params = (
+                additional_inputs_accordion.recover_kwargs(
+                    additional_inputs_accordion.get_config()
+                )
+            )
+        else:
+            raise ValueError(
+                f"The `additional_inputs_accordion` parameter must be a string or gr.Accordion, not {builtins.type(additional_inputs_accordion)}"
+            )
+        self._additional_inputs_in_examples = False
+        if self.additional_inputs and self.examples is not None:
+            for example in self.examples:
+                if not isinstance(example, list):
+                    raise ValueError(
+                        "Examples must be a list of lists when additional inputs are provided."
+                    )
+                for idx, example_for_input in enumerate(example):
+                    if example_for_input is not None and idx > 0:
+                        self._additional_inputs_in_examples = True
+                        break
+                if self._additional_inputs_in_examples:
+                    break
+
+        if flagging_mode is None:
+            flagging_mode = os.getenv("GRADIO_CHAT_FLAGGING_MODE", "never")  # type: ignore
+        if flagging_mode in ["manual", "never"]:
+            self.flagging_mode = flagging_mode
+        else:
+            raise ValueError(
+                "Invalid value for `flagging_mode` parameter."
+                "Must be: 'manual' or 'never'."
+            )
+        self.flagging_options = flagging_options
+        self.flagging_dir = flagging_dir
+
+        with self:
+            self.saved_conversations = BrowserState(
+                [], storage_key=f"_saved_conversations_{self._id}"
+            )
+            self.conversation_id = State(None)
+            self.saved_input = State()  # Stores the most recent user message
+            self.null_component = State()  # Used to discard unneeded values
+
+            with Column():
+                self._render_header()
+                if self.save_history:
+                    with Row(scale=1):
+                        self._render_history_area()
+                        with Column(scale=6):
+                            self._render_chatbot_area(
+                                chatbot, textbox, submit_btn, stop_btn
+                            )
+                            self._render_footer()
+                else:
+                    self._render_chatbot_area(chatbot, textbox, submit_btn, stop_btn)
+                    self._render_footer()
+
+            self._setup_events()
+
+    def _render_header(self):
+        if self.logo:
+            Markdown(f"""\
+<p align="center"><img src="{self.logo}" style="height: 100px"/><p>""")
+        if self.title:
+            Markdown(f"""<center><font size=8>{self.title}</center>""")
+        if self.description:
+            Markdown(
+                f"""\
+    <center><font size=3>{self.description}</center>""")
+
+    def _render_history_area(self):
+        with Column(scale=1, min_width=100):
+            self.new_chat_button = Button(
+                "New chat",
+                variant="primary",
+                size="md",
+                icon=utils.get_icon_path("plus.svg"),
+                # scale=0,
+            )
+            self.chat_history_dataset = Dataset(
+                components=[Textbox(visible=False)],
+                show_label=False,
+                layout="table",
+                type="index",
+            )
+
+    def _render_chatbot_area(
+        self,
+        chatbot: Chatbot | None,
+        textbox: Textbox | MultimodalTextbox | None,
+        submit_btn: str | bool | None,
+        stop_btn: str | bool | None,
+    ):
+        if chatbot:
+            if self.type:
+                if self.type != chatbot.type:
+                    warnings.warn(
+                        "The type of the gr.Chatbot does not match the type of the gr.ChatInterface."
+                        f"The type of the gr.ChatInterface, '{self.type}', will be used."
+                    )
+                    chatbot.type = cast(Literal["messages", "tuples"], self.type)
+                    chatbot._setup_data_model()
+            else:
+                warnings.warn(
+                    f"The gr.ChatInterface was not provided with a type, so the type of the gr.Chatbot, '{chatbot.type}', will be used."
+                )
+                self.type = chatbot.type
+            self.chatbot = cast(Chatbot, get_component_instance(chatbot, render=True))
+            if self.chatbot.examples and self.examples_messages:
+                warnings.warn(
+                    "The ChatInterface already has examples set. The examples provided in the chatbot will be ignored."
+                )
+            self.chatbot.examples = (
+                self.examples_messages
+                if not self._additional_inputs_in_examples
+                else None
+            )
+            self.chatbot._setup_examples()
+        else:
+            self.type = self.type or "tuples"
+            self.chatbot = Chatbot(
+                label="Chatbot",
+                scale=1,
+                height=400 if self.fill_height else None,
+                type=cast(Literal["messages", "tuples"], self.type),
+                autoscroll=self.autoscroll,
+                examples=self.examples_messages
+                if not self._additional_inputs_in_examples
+                else None,
+            )
+        with Group():
+            with Row():
+                if textbox:
+                    textbox.show_label = False
+                    textbox_ = get_component_instance(textbox, render=True)
+                    if not isinstance(textbox_, (Textbox, MultimodalTextbox)):
+                        raise TypeError(
+                            f"Expected a gr.Textbox or gr.MultimodalTextbox component, but got {builtins.type(textbox_)}"
+                        )
+                    self.textbox = textbox_
+                else:
+                    textbox_component = (
+                        MultimodalTextbox if self.multimodal else Textbox
+                    )
+                    self.textbox = textbox_component(
+                        show_label=False,
+                        label="Message",
+                        placeholder="Type a message...",
+                        scale=7,
+                        autofocus=self.autofocus,
+                        submit_btn=submit_btn,
+                        stop_btn=stop_btn,
+                    )
+
+        # Hide the stop button at the beginning, and show it with the given value during the generator execution.
+        self.original_stop_btn = self.textbox.stop_btn
+        self.textbox.stop_btn = False
+        self.fake_api_btn = Button("Fake API", visible=False)
+        self.api_response = JSON(
+            label="Response", visible=False
+        )  # Used to store the response from the API call
+
+        # Used internally to store the chatbot value when it differs from the value displayed in the chatbot UI.
+        # For example, when a user submits a message, the chatbot UI is immediately updated with the user message,
+        # but the chatbot_state value is not updated until the submit_fn is called.
+        self.chatbot_state = State(self.chatbot.value if self.chatbot.value else [])
+
+        # Provided so that developers can update the chatbot value from other events outside of `gr.ChatInterface`.
+        self.chatbot_value = State(self.chatbot.value if self.chatbot.value else [])
+
+    def _render_footer(self):
+        if self.examples:
+            self.examples_handler = Examples(
+                examples=self.examples,
+                inputs=[self.textbox] + self.additional_inputs,
+                outputs=self.chatbot,
+                fn=self._examples_stream_fn if self.is_generator else self._examples_fn,
+                cache_examples=self.cache_examples,
+                cache_mode=cast(Literal["eager", "lazy"], self.cache_mode),
+                visible=self._additional_inputs_in_examples,
+                preprocess=self._additional_inputs_in_examples,
+            )
+
+        any_unrendered_inputs = any(
+            not inp.is_rendered for inp in self.additional_inputs
+        )
+        if self.additional_inputs and any_unrendered_inputs:
+            with Accordion(**self.additional_inputs_accordion_params):  # type: ignore
+                for input_component in self.additional_inputs:
+                    if not input_component.is_rendered:
+                        input_component.render()
+
+    def _setup_example_messages(
+        self,
+        examples: list[str] | list[MultimodalValue] | list[list] | None,
+        example_labels: list[str] | None = None,
+        example_icons: list[str] | None = None,
+    ) -> list[ExampleMessage]:
+        examples_messages = []
+        if examples:
+            for index, example in enumerate(examples):
+                if isinstance(example, list):
+                    example = example[0]
+                example_message: ExampleMessage = {}
+                if isinstance(example, str):
+                    example_message["text"] = example
+                elif isinstance(example, dict):
+                    example_message["text"] = example.get("text", "")
+                    example_message["files"] = example.get("files", [])
+                if example_labels:
+                    example_message["display_text"] = example_labels[index]
+                if self.multimodal:
+                    example_files = example_message.get("files")
+                    if not example_files:
+                        if example_icons:
+                            example_message["icon"] = example_icons[index]
+                        else:
+                            example_message["icon"] = {
+                                "path": "",
+                                "url": None,
+                                "orig_name": None,
+                                "mime_type": "text",  # for internal use, not a valid mime type
+                                "meta": {"_type": "gradio.FileData"},
+                            }
+                    elif example_icons:
+                        example_message["icon"] = example_icons[index]
+                examples_messages.append(example_message)
+        return examples_messages
+
+    def _generate_chat_title(self, conversation: list[MessageDict]) -> str:
+        """
+        Generate a title for a conversation by taking the first user message that is a string
+        and truncating it to 40 characters. If files are present, add a 📎 to the title.
+        """
+        title = ""
+        for message in conversation:
+            if message["role"] == "user":
+                if isinstance(message["content"], str):
+                    title += message["content"]
+                    break
+                else:
+                    title += "📎 "
+        if len(title) > 40:
+            title = title[:40] + "..."
+        return title or "Conversation"
+
+    def _save_conversation(
+        self,
+        index: int | None,
+        conversation: list[MessageDict],
+        saved_conversations: list[list[MessageDict]],
+    ):
+        if self.save_history:
+            if index is not None:
+                saved_conversations[index] = conversation
+            else:
+                saved_conversations.append(conversation)
+                index = len(saved_conversations) - 1
+        return index, saved_conversations
+
+    def _delete_conversation(
+        self,
+        index: int | None,
+        saved_conversations: list[list[MessageDict]],
+    ):
+        if index is not None:
+            saved_conversations.pop(index)
+        return None, saved_conversations
+
+    def _load_chat_history(self, conversations):
+        return Dataset(
+            samples=[
+                [self._generate_chat_title(conv)]
+                for conv in conversations or []
+                if conv
+            ]
+        )
+
+    def _load_conversation(
+        self,
+        index: int,
+        conversations: list[list[MessageDict]],
+    ):
+        return (
+            index,
+            Chatbot(
+                value=conversations[index],  # type: ignore
+                feedback_value=[],
+            ),
+        )
+
+    def _setup_events(self) -> None:
+        from gradio import on
+
+        submit_triggers = [self.textbox.submit, self.chatbot.retry]
+        submit_fn = self._stream_fn if self.is_generator else self._submit_fn
+
+        synchronize_chat_state_kwargs = {
+            "fn": lambda x: (x, x),
+            "inputs": [self.chatbot],
+            "outputs": [self.chatbot_state, self.chatbot_value],
+            "show_api": False,
+            "queue": False,
+        }
+        submit_fn_kwargs = {
+            "fn": submit_fn,
+            "inputs": [self.saved_input, self.chatbot_state] + self.additional_inputs,
+            "outputs": [self.null_component, self.chatbot] + self.additional_outputs,
+            "show_api": False,
+            "concurrency_limit": cast(
+                Union[int, Literal["default"], None], self.concurrency_limit
+            ),
+            "show_progress": cast(
+                Literal["full", "minimal", "hidden"], self.show_progress
+            ),
+        }
+        save_fn_kwargs = {
+            "fn": self._save_conversation,
+            "inputs": [
+                self.conversation_id,
+                self.chatbot_state,
+                self.saved_conversations,
+            ],
+            "outputs": [self.conversation_id, self.saved_conversations],
+            "show_api": False,
+            "queue": False,
+        }
+
+        submit_event = (
+            self.textbox.submit(
+                self._clear_and_save_textbox,
+                [self.textbox],
+                [self.textbox, self.saved_input],
+                show_api=False,
+                queue=False,
+            )
+            .then(  # The reason we do this outside of the submit_fn is that we want to update the chatbot UI with the user message immediately, before the submit_fn is called
+                self._append_message_to_history,
+                [self.saved_input, self.chatbot],
+                [self.chatbot],
+                show_api=False,
+                queue=False,
+            )
+            .then(**submit_fn_kwargs)
+        )
+        submit_event.then(**synchronize_chat_state_kwargs).then(
+            lambda: update(value=None, interactive=True),
+            None,
+            self.textbox,
+            show_api=False,
+        ).then(**save_fn_kwargs)
+
+        # Creates the "/chat" API endpoint
+        self.fake_api_btn.click(
+            submit_fn,
+            [self.textbox, self.chatbot_state] + self.additional_inputs,
+            [self.api_response, self.chatbot_state] + self.additional_outputs,
+            api_name=cast(Union[str, Literal[False]], self.api_name),
+            concurrency_limit=cast(
+                Union[int, Literal["default"], None], self.concurrency_limit
+            ),
+            postprocess=False,
+        )
+
+        if (
+            isinstance(self.chatbot, Chatbot)
+            and self.examples
+            and not self._additional_inputs_in_examples
+        ):
+            if self.cache_examples or self.run_examples_on_click:
+                example_select_event = self.chatbot.example_select(
+                    self.example_clicked,
+                    None,
+                    [self.chatbot, self.saved_input],
+                    show_api=False,
+                )
+                if not self.cache_examples:
+                    example_select_event = example_select_event.then(**submit_fn_kwargs)
+                example_select_event.then(**synchronize_chat_state_kwargs)
+            else:
+                self.chatbot.example_select(
+                    self.example_populated,
+                    None,
+                    [self.textbox],
+                    show_api=False,
+                )
+
+        retry_event = (
+            self.chatbot.retry(
+                self._pop_last_user_message,
+                [self.chatbot_state],
+                [self.chatbot_state, self.saved_input],
+                show_api=False,
+                queue=False,
+            )
+            .then(
+                self._append_message_to_history,
+                [self.saved_input, self.chatbot_state],
+                [self.chatbot],
+                show_api=False,
+                queue=False,
+            )
+            .then(
+                lambda: update(interactive=False, placeholder=""),
+                outputs=[self.textbox],
+                show_api=False,
+            )
+            .then(**submit_fn_kwargs)
+        )
+        retry_event.then(**synchronize_chat_state_kwargs).then(
+            lambda: update(interactive=True),
+            outputs=[self.textbox],
+            show_api=False,
+        ).then(**save_fn_kwargs)
+
+        self._setup_stop_events(submit_triggers, [submit_event, retry_event])
+
+        self.chatbot.undo(
+            self._pop_last_user_message,
+            [self.chatbot],
+            [self.chatbot, self.textbox],
+            show_api=False,
+            queue=False,
+        ).then(**synchronize_chat_state_kwargs).then(**save_fn_kwargs)
+
+        self.chatbot.option_select(
+            self.option_clicked,
+            [self.chatbot],
+            [self.chatbot, self.saved_input],
+            show_api=False,
+        ).then(**submit_fn_kwargs).then(**synchronize_chat_state_kwargs).then(
+            **save_fn_kwargs
+        )
+
+        self.chatbot.clear(**synchronize_chat_state_kwargs).then(
+            self._delete_conversation,
+            [self.conversation_id, self.saved_conversations],
+            [self.conversation_id, self.saved_conversations],
+            show_api=False,
+            queue=False,
+        )
+
+        if self.editable:
+            self.chatbot.edit(
+                self._edit_message,
+                [self.chatbot],
+                [self.chatbot, self.chatbot_state, self.saved_input],
+                show_api=False,
+            ).success(**submit_fn_kwargs).success(**synchronize_chat_state_kwargs).then(
+                **save_fn_kwargs
+            )
+
+        if self.save_history:
+            self.new_chat_button.click(
+                lambda: (None, []),
+                None,
+                [self.conversation_id, self.chatbot],
+                show_api=False,
+                queue=False,
+            ).then(
+                lambda x: x,
+                [self.chatbot],
+                [self.chatbot_state],
+                show_api=False,
+                queue=False,
+            )
+
+            on(
+                triggers=[self.load, self.saved_conversations.change],
+                fn=self._load_chat_history,
+                inputs=[self.saved_conversations],
+                outputs=[self.chat_history_dataset],
+                show_api=False,
+                queue=False,
+            )
+
+            self.chat_history_dataset.click(
+                lambda: [],
+                None,
+                [self.chatbot],
+                show_api=False,
+                queue=False,
+                show_progress="hidden",
+            ).then(
+                self._load_conversation,
+                [self.chat_history_dataset, self.saved_conversations],
+                [self.conversation_id, self.chatbot],
+                show_api=False,
+                queue=False,
+                show_progress="hidden",
+            ).then(**synchronize_chat_state_kwargs)
+
+        if self.flagging_mode != "never":
+            flagging_callback = ChatCSVLogger()
+            flagging_callback.setup(self.flagging_dir)
+            self.chatbot.feedback_options = self.flagging_options
+            self.chatbot.like(flagging_callback.flag, self.chatbot)
+
+        self.chatbot_value.change(
+            lambda x: x,
+            [self.chatbot_value],
+            [self.chatbot],
+            show_api=False,
+        ).then(**synchronize_chat_state_kwargs)
+
+    def _setup_stop_events(
+        self, event_triggers: list[Callable], events_to_cancel: list[Dependency]
+    ) -> None:
+        textbox_component = MultimodalTextbox if self.multimodal else Textbox
+        if self.is_generator:
+            original_submit_btn = self.textbox.submit_btn
+            for event_trigger in event_triggers:
+                event_trigger(
+                    utils.async_lambda(
+                        lambda: textbox_component(
+                            submit_btn=False,
+                            stop_btn=self.original_stop_btn,
+                        )
+                    ),
+                    None,
+                    [self.textbox],
+                    show_api=False,
+                    queue=False,
+                )
+            for event_to_cancel in events_to_cancel:
+                event_to_cancel.then(
+                    utils.async_lambda(
+                        lambda: textbox_component(
+                            submit_btn=original_submit_btn, stop_btn=False
+                        )
+                    ),
+                    None,
+                    [self.textbox],
+                    show_api=False,
+                    queue=False,
+                )
+            self.textbox.stop(
+                None,
+                None,
+                None,
+                cancels=events_to_cancel,  # type: ignore
+                show_api=False,
+            )
+
+    def _clear_and_save_textbox(
+        self,
+        message: str | MultimodalPostprocess,
+    ) -> tuple[
+        Textbox | MultimodalTextbox,
+        str | MultimodalPostprocess,
+    ]:
+        return (
+            type(self.textbox)("", interactive=False, placeholder=""),
+            message,
+        )
+
+    @staticmethod
+    def _messages_to_tuples(history_messages: list[MessageDict]) -> TupleFormat:
+        history_tuples = []
+        for message in history_messages:
+            if message["role"] == "user":
+                history_tuples.append((message["content"], None))
+            elif history_tuples and history_tuples[-1][1] is None:
+                history_tuples[-1] = (history_tuples[-1][0], message["content"])
+            else:
+                history_tuples.append((None, message["content"]))
+        return history_tuples
+
+    @staticmethod
+    def _tuples_to_messages(history_tuples: TupleFormat) -> list[MessageDict]:
+        history_messages = []
+        for message_tuple in history_tuples:
+            if message_tuple[0]:
+                history_messages.append({"role": "user", "content": message_tuple[0]})
+            if message_tuple[1]:
+                history_messages.append(
+                    {"role": "assistant", "content": message_tuple[1]}
+                )
+        return history_messages
+
+    def _append_message_to_history(
+        self,
+        message: MessageDict | Message | str | Component | MultimodalPostprocess | list,
+        history: list[MessageDict] | TupleFormat,
+        role: Literal["user", "assistant"] = "user",
+    ) -> list[MessageDict] | TupleFormat:
+        message_dicts = self._message_as_message_dict(message, role)
+        if self.type == "tuples":
+            history = self._tuples_to_messages(history)  # type: ignore
+        else:
+            history = copy.deepcopy(history)
+        history.extend(message_dicts)  # type: ignore
+        if self.type == "tuples":
+            history = self._messages_to_tuples(history)  # type: ignore
+        return history
+
+    def _message_as_message_dict(
+        self,
+        message: MessageDict | Message | str | Component | MultimodalPostprocess | list,
+        role: Literal["user", "assistant"],
+    ) -> list[MessageDict]:
+        """
+        Converts a user message, example message, or response from the chat function to a
+        list of MessageDict objects that can be appended to the chat history.
+        """
+        message_dicts = []
+        if not isinstance(message, list):
+            message = [message]
+        for msg in message:
+            if isinstance(msg, Message):
+                message_dicts.append(msg.model_dump())
+            elif isinstance(msg, ChatMessage):
+                msg.role = role
+                message_dicts.append(
+                    dataclasses.asdict(msg, dict_factory=utils.dict_factory)
+                )
+            elif isinstance(msg, (str, Component)):
+                message_dicts.append({"role": role, "content": msg})
+            elif (
+                isinstance(msg, dict) and "content" in msg
+            ):  # in MessageDict format already
+                msg["role"] = role
+                message_dicts.append(msg)
+            else:  # in MultimodalPostprocess format
+                for x in msg.get("files", []):
+                    if isinstance(x, dict):
+                        x = x.get("path")
+                    message_dicts.append({"role": role, "content": (x,)})
+                if msg["text"] is None or not isinstance(msg["text"], str):
+                    pass
+                else:
+                    message_dicts.append({"role": role, "content": msg["text"]})
+        return message_dicts
+
+    async def _submit_fn(
+        self,
+        message: str | MultimodalPostprocess,
+        history: TupleFormat | list[MessageDict],
+        request: Request,
+        *args,
+    ) -> tuple:
+        inputs, _, _ = special_args(
+            self.fn, inputs=[message, history, *args], request=request
+        )
+        if self.is_async:
+            response = await self.fn(*inputs)
+        else:
+            response = await anyio.to_thread.run_sync(
+                self.fn, *inputs, limiter=self.limiter
+            )
+        if self.additional_outputs:
+            response, *additional_outputs = response
+        else:
+            additional_outputs = None
+        history = self._append_message_to_history(message, history, "user")
+        history = self._append_message_to_history(response, history, "assistant")
+        if additional_outputs:
+            return response, history, *additional_outputs
+        return response, history
+
+    async def _stream_fn(
+        self,
+        message: str | MultimodalPostprocess,
+        history: TupleFormat | list[MessageDict],
+        request: Request,
+        *args,
+    ) -> AsyncGenerator[
+        tuple,
+        None,
+    ]:
+        inputs, _, _ = special_args(
+            self.fn, inputs=[message, history, *args], request=request
+        )
+        if self.is_async:
+            generator = self.fn(*inputs)
+        else:
+            generator = await anyio.to_thread.run_sync(
+                self.fn, *inputs, limiter=self.limiter
+            )
+            generator = utils.SyncToAsyncIterator(generator, self.limiter)
+
+        history = self._append_message_to_history(message, history, "user")
+        additional_outputs = None
+        try:
+            first_response = await utils.async_iteration(generator)
+            if self.additional_outputs:
+                first_response, *additional_outputs = first_response
+            history_ = self._append_message_to_history(
+                first_response, history, "assistant"
+            )
+            if not additional_outputs:
+                yield first_response, history_
+            else:
+                yield first_response, history_, *additional_outputs
+        except StopIteration:
+            yield None, history
+        async for response in generator:
+            if self.additional_outputs:
+                response, *additional_outputs = response
+            history_ = self._append_message_to_history(response, history, "assistant")
+            if not additional_outputs:
+                yield response, history_
+            else:
+                yield response, history_, *additional_outputs
+
+    def option_clicked(
+        self, history: list[MessageDict], option: SelectData
+    ) -> tuple[TupleFormat | list[MessageDict], str | MultimodalPostprocess]:
+        """
+        When an option is clicked, the chat history is appended with the option value.
+        The saved input value is also set to option value. Note that event can only
+        be called if self.type is "messages" since options are only available for this
+        chatbot type.
+        """
+        history.append({"role": "user", "content": option.value})
+        return history, option.value
+
+    def _flatten_example_files(self, example: SelectData):
+        """
+        Returns an example with the files flattened to just the file path.
+        Also ensures that the `files` key is always present in the example.
+        """
+        example.value["files"] = [f["path"] for f in example.value.get("files", [])]
+        return example
+
+    def example_populated(self, example: SelectData):
+        if self.multimodal:
+            example = self._flatten_example_files(example)
+            return example.value
+        else:
+            return example.value["text"]
+
+    def _edit_message(
+        self, history: list[MessageDict] | TupleFormat, edit_data: EditData
+    ) -> tuple[
+        list[MessageDict] | TupleFormat,
+        list[MessageDict] | TupleFormat,
+        str | MultimodalPostprocess,
+    ]:
+        if isinstance(edit_data.index, (list, tuple)):
+            history = history[: edit_data.index[0]]
+        else:
+            history = history[: edit_data.index]
+        return history, history, edit_data.value
+
+    def example_clicked(
+        self, example: SelectData
+    ) -> Generator[
+        tuple[TupleFormat | list[MessageDict], str | MultimodalPostprocess], None, None
+    ]:
+        """
+        When an example is clicked, the chat history (and saved input) is initially set only
+        to the example message. Then, if example caching is enabled, the cached response is loaded
+        and added to the chat history as well.
+        """
+        history = self._append_message_to_history(example.value, [], "user")
+        example = self._flatten_example_files(example)
+        message = example.value if self.multimodal else example.value["text"]
+        yield history, message
+        if self.cache_examples:
+            history = self.examples_handler.load_from_cache(example.index)[0].root
+            yield history, message
+
+    def _process_example(
+        self, message: ExampleMessage | str, response: MessageDict | str | None
+    ):
+        result = []
+        if self.multimodal:
+            message = cast(ExampleMessage, message)
+            if self.type == "tuples":
+                for file in message.get("files", []):
+                    result.append([file, None])
+                if "text" in message:
+                    result.append([message["text"], None])
+                result[-1][1] = response
+            else:
+                for file in message.get("files", []):
+                    if isinstance(file, dict):
+                        file = file.get("path")
+                    result.append({"role": "user", "content": (file,)})
+                if "text" in message:
+                    result.append({"role": "user", "content": message["text"]})
+                result.append({"role": "assistant", "content": response})
+        else:
+            message = cast(str, message)
+            if self.type == "tuples":
+                result = [[message, response]]
+            else:
+                result = [
+                    {"role": "user", "content": message},
+                    {"role": "assistant", "content": response},
+                ]
+        return result
+
+    async def _examples_fn(
+        self, message: ExampleMessage | str, *args
+    ) -> TupleFormat | list[MessageDict]:
+        inputs, _, _ = special_args(self.fn, inputs=[message, [], *args], request=None)
+        if self.is_async:
+            response = await self.fn(*inputs)
+        else:
+            response = await anyio.to_thread.run_sync(
+                self.fn, *inputs, limiter=self.limiter
+            )
+        return self._process_example(message, response)  # type: ignore
+
+    async def _examples_stream_fn(
+        self,
+        message: str,
+        *args,
+    ) -> AsyncGenerator:
+        inputs, _, _ = special_args(self.fn, inputs=[message, [], *args], request=None)
+
+        if self.is_async:
+            generator = self.fn(*inputs)
+        else:
+            generator = await anyio.to_thread.run_sync(
+                self.fn, *inputs, limiter=self.limiter
+            )
+            generator = utils.SyncToAsyncIterator(generator, self.limiter)
+        async for response in generator:
+            yield self._process_example(message, response)
+
+    def _pop_last_user_message(
+        self,
+        history: list[MessageDict] | TupleFormat,
+    ) -> tuple[list[MessageDict] | TupleFormat, str | MultimodalPostprocess]:
+        """
+        Removes the message (or set of messages) that the user last sent from the chat history and returns them.
+        If self.multimodal is True, returns a MultimodalPostprocess (dict) object with text and files.
+        If self.multimodal is False, returns just the message text as a string.
+        """
+        if not history:
+            return history, "" if not self.multimodal else {"text": "", "files": []}
+
+        if self.type == "tuples":
+            history = self._tuples_to_messages(history)  # type: ignore
+        i = len(history) - 1
+        while i >= 0 and history[i]["role"] == "assistant":  # type: ignore
+            i -= 1
+        while i >= 0 and history[i]["role"] == "user":  # type: ignore
+            i -= 1
+        last_messages = history[i + 1 :]
+        last_user_message = ""
+        files = []
+        for msg in last_messages:
+            assert isinstance(msg, dict)  # noqa: S101
+            if msg["role"] == "user":
+                content = msg["content"]
+                if isinstance(content, tuple):
+                    files.append(content[0])
+                else:
+                    last_user_message = content
+        return_message = (
+            {"text": last_user_message, "files": files}
+            if self.multimodal
+            else last_user_message
+        )
+        history_ = history[: i + 1]
+        if self.type == "tuples":
+            history_ = self._messages_to_tuples(history_)  # type: ignore
+        return history_, return_message  # type: ignore
+
+    def render(self) -> ChatInterface:
+        # If this is being rendered inside another Blocks, and the height is not explicitly set, set it to 400 instead of 200.
+        if get_blocks_context() and not self.provided_chatbot:
+            self.chatbot.height = 400
+            super().render()
+        return self

gradio_app.py

@@ -0,0 +1,82 @@
+import gradio as gr
+from collections.abc import Generator
+from openai import OpenAI
+# from gradio.chat_interface import ChatInterface
+from chat_interface import ChatInterface
+
+USERNAME = "ahmedheakl"
+SPACE_NAME = "arabic-vlm-app"
+TITLE = "AIN Arabic VLM"
+DESCRIPTION = "Welcome to the AIN Arabic VLM chatbot. The best Arabic-English VLM developed by MBZUAI."
+TOP_N_HISTORY = 2
+LOGO_PATH = "https://huggingface.co/spaces/ahmedheakl/arabic-vlm-app/resolve/main/logo.jpeg"
+
+
+
+def load_chat(
+    base_url: str,
+    model: str,
+    token: str | None = None,
+    *,
+    system_message: str | None = None,
+    **kwargs,
+) -> gr.ChatInterface:
+    client = OpenAI(api_key=token, base_url=base_url)
+    start_message = (
+        [{"role": "system", "content": system_message}] if system_message else []
+    )
+
+    def open_api_stream(
+        message: str, history: list | None
+    ) -> Generator[str, None, None]:
+        history = history or start_message
+        if len(history) > 0 and isinstance(history[0], (list, tuple)):
+            history = history[:TOP_N_HISTORY]
+            history = ChatInterface._tuples_to_messages(history)
+        files = message.get('files', [])
+        content = [
+            {"type": "text", "text": message.get('text', '')}
+        ]
+        if files:
+            src_path = files[0]
+            image_url = f"https://{USERNAME}-{SPACE_NAME}.hf.space/gradio_api/file={src_path}"
+            content.append({"type": "image_url", "image_url": {"url": image_url}})
+        stream = client.chat.completions.create(
+            model=model,
+            messages=history + [{"role": "user", "content": content}],
+            stream=True,
+        )
+        response = ""
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                response += chunk.choices[0].delta.content
+                yield response
+
+    return ChatInterface(
+        open_api_stream, type="messages", **kwargs
+    )
+
+load_chat(
+    "https://0f21-5-195-0-150.ngrok-free.app/v1",
+    model="test",
+    token="ollama",
+    multimodal=True,
+    title=TITLE,
+    description=DESCRIPTION,
+    theme="ocean",
+    examples=[
+        {
+            "text": "أخبرني ما اسم المبنى الموجود في الصورة والشيء المثير للاهتمام فيه",
+            "files": ["https://cdn.mos.cms.futurecdn.net/5HrnHp9ybAqYrtruKAsfkN-1200-80.jpg"],
+        },
+        {
+            "text": "ما هو العلم الموجود في الصورة؟", 
+            "files": ["https://mtc.ae/wp-content/uploads/2023/09/Satin-UAE-Flag-UAE-F-B-Blank.jpg"],
+        },
+        {
+            "text": "How many people are there in the image?",
+            "files": ["https://i0.wp.com/eatpitapita.com/wp-content/uploads/2020/02/Arab-Muslim-or-Middle-Eastern-Preview.jpg"]
+        },
+    ],
+    logo=LOGO_PATH,
+).queue().launch(allowed_paths=["/static"])

old_app.py

@@ -0,0 +1,97 @@
+import gradio as gr
+from collections.abc import Generator
+from openai import OpenAI
+from gradio.chat_interface import ChatInterface
+from pathlib import Path
+import shutil
+import os
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+
+USERNAME = "ahmedheakl"
+SPACE_NAME = "arabic-vlm-app"
+TITLE = "AIN Arabic VLM"
+DESCRIPTION = "Welcome to the AIN Arabic VLM chatbot. The best Arabic-English VLM developed by MBZUAI."
+PUBLIC_DIR = Path("static")
+TOP_N_HISTORY = 2
+LOGO_PATH = "./logo.jpeg"
+os.makedirs(PUBLIC_DIR, exist_ok=True)
+
+app = FastAPI()
+app.mount("/static", StaticFiles(directory=PUBLIC_DIR), name="static")
+
+
+# move the logo to public directory
+shutil.copy(LOGO_PATH, PUBLIC_DIR / Path(LOGO_PATH).name)
+logo_path = f"/static/{Path(LOGO_PATH).name}"
+
+def load_chat(
+    base_url: str,
+    model: str,
+    token: str | None = None,
+    *,
+    system_message: str | None = None,
+    **kwargs,
+) -> gr.ChatInterface:
+    client = OpenAI(api_key=token, base_url=base_url)
+    start_message = (
+        [{"role": "system", "content": system_message}] if system_message else []
+    )
+
+    def open_api_stream(
+        message: str, history: list | None
+    ) -> Generator[str, None, None]:
+        history = history or start_message
+        if len(history) > 0 and isinstance(history[0], (list, tuple)):
+            history = history[:TOP_N_HISTORY]
+            history = ChatInterface._tuples_to_messages(history)
+        files = message.get('files', [])
+        content = [
+            {"type": "text", "text": message.get('text', '')}
+        ]
+        if files:
+            src_path = Path(files[0])
+            dest_path = PUBLIC_DIR / src_path.name
+            shutil.move(src_path, dest_path)
+            image_url = f"https://{USERNAME}-{SPACE_NAME}.hf.space/static/{src_path.name}"
+            content.append({"type": "image_url", "image_url": {"url": image_url}})
+        stream = client.chat.completions.create(
+            model=model,
+            messages=history + [{"role": "user", "content": content}],
+            stream=True,
+        )
+        response = ""
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                response += chunk.choices[0].delta.content
+                yield response
+
+    return ChatInterface(
+        open_api_stream, type="messages", **kwargs
+    )
+with gr.Blocks(theme=gr.themes.Soft()) as gradio_interface:
+    # Add CSS for better styling
+    gr.Markdown(
+        """
+        <style>
+        .container { margin: 0 auto; max-width: 1200px; padding: 20px; }
+        .header { text-align: center; margin-bottom: 40px; }
+        </style>
+        """
+    )
+    # chatbot = gr.Chatbot()
+    # textbox = gr.MultimodalTextbox(file_count="single", file_types=["image"], sources=["upload"])
+    
+load_chat(
+    "https://0f21-5-195-0-150.ngrok-free.app/v1",
+    model="test",
+    token="ollama",
+    multimodal=True,
+    # chatbot=chatbot,
+    # textbox=textbox,
+).launch()
+app = gr.mount_gradio_app(app, gradio_interface, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -0,0 +1,4 @@
+openai==1.60.0
+requests==2.32.3
+gradio==5.13.0
+gradio_client==1.6.0

run_app.sh

@@ -0,0 +1,9 @@
+#!/bin/bash
+source /apps/local/anaconda3/conda_init.sh
+conda activate .ara-app
+
+gunicorn -b 0.0.0.0:5000 app:app
+# connect telegram bot
+#  
+# curl -X POST https://api.telegram.org/bot8003439194:AAGQNjq3dQSZi4Ztv2Z0yyZx1ZnD0L5MR5o/setWebhook \
+#     -d "url=https://497f-5-195-0-145.ngrok-free.app/8003439194:AAGQNjq3dQSZi4Ztv2Z0yyZx1ZnD0L5MR5o"

setup_amazon.sh

@@ -0,0 +1,8 @@
+sudo apt update
+sudo apt install python3-pip -y
+sudo apt install python3-venv -y
+python3 -m venv venv
+. venv/bin/activate
+sudo apt install nginx -y
+sudo systemctl start nginx
+sudo systemctl enable nginx