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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)
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