Test files for the gradio app

Dockerfile

# Select base image (can be ubuntu, python, shiny etc)
FROM python:3.11-slim

# Create user name and home directory variables.
# The variables are later used as $USER and $HOME.
ENV USER=username
ENV HOME=/home/$USER

# Add user to system
RUN useradd -m -u 1000 $USER

# Set working directory (this is where the code should go)
WORKDIR $HOME/app

# Update system and install dependencies.
RUN apt-get update && apt-get install --no-install-recommends -y \
    build-essential \
    software-properties-common

RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

RUN pip install --no-cache-dir gradio

RUN apt-get install -y curl

COPY main.py $HOME/app/main.py
# copy any other files that are needed for your app with the directory structure as your files expect
COPY start-script.sh $HOME/app/start-script.sh
COPY data/ $HOME/app/data

RUN chmod +x start-script.sh \
    && chown -R $USER:$USER $HOME \
    && rm -rf /var/lib/apt/lists/*

USER $USER
EXPOSE 8080


ENTRYPOINT ["./start-script.sh"]

main.py

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import random
from io import BytesIO

import gradio as gr
import requests
from PIL import Image


def fake_gan():
    images = [
        (random.choice(
            [
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg",
            ]
        ), f"label {i}")
        for i in range(3)
    ]
    return images

def predict(mol: str, confidence: float, imageWidth, imageHeight):
    resp = requests.get(
            "https://cplogdapi.serve.scilifelab.se/api/v2/predictImage",
            params={
                "molecule": mol,
                "confidence": confidence,
                "imageWidth": imageWidth,
                "imageHeight": imageHeight,
            },
        )
    print(resp.raw)
    return Image.open(BytesIO(resp.content), formats=["png"])


with gr.Blocks() as demo:
    gallery = gr.Gallery(
        label="Generated images", show_label=False, elem_id="gallery"
    , columns=[1], rows=[1], object_fit="contain", height="auto")
    btn = gr.Button("Generate images", scale=0)

    btn.click(fake_gan, None, gallery)

    text_inupt = gr.Textbox(lines=5, label="Input smiles", placeholder="Enter smiles here")
    confidence = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="Confidence")
    imageWidth = gr.Slider(minimum=0, maximum=1000, step=1, label="Image width")
    imageHeight = gr.Slider(minimum=0, maximum=1000, step=1, label="Image height")
    output = gr.Image(label="Predicted image", show_label=False, elem_id="output")
    btn2 = gr.Button("Predict", scale=0)

    btn2.click(predict, [text_inupt, confidence, imageWidth, imageHeight], output)

if __name__ == "__main__":
    print("Starting demo server...")
    demo.launch(server_name="0.0.0.0", server_port=8080)
    # demo.launch()

start-script.sh

#!/bin/bash
python3 main.py