cleaned

Makefile

@@ -1,24 +0,0 @@
-install-node:
-	./install-node.sh
-
-build-client:
-	cd frontend && npm install && npm run build && rm -rf ../static && cp -r build/ ../static/
-
-build-dev:
-	cd frontend && npm install && NODE_ENV=development npm run build && rm -rf ../static 2>&1 && cp -rv build/ ../static/
-
-run-dev:
-	FLASK_DEBUG=development python3 app.py
-
-run-prod:
-	python3 app.py & python3 gradio-app.py
-
-run-mock:
-	python3 app.py & python3 gradio-mock-app.py
-
-stop-server:
-	killall python
-
-all: run-prod
-
-mock: run-mock

README copy.md

@@ -1,10 +0,0 @@
----
-title: Drawing2Map
-emoji: 🌍
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

README.md

@@ -1,5 +1,5 @@
 ---
-title: Drawing2Map
+title: Drawing2Map-api
 emoji: 🎨
 colorFrom: blue
 colorTo: green

app.py

@@ -4,36 +4,16 @@ import requests
 from gradio_client import Client
 import base64
 
-from PIL import Image
-from io import BytesIO
-import base64
-import os
-
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from diffusers.utils import load_image
-import torch
-
-import gradio as gr
-
-controlnet = ControlNetModel.from_pretrained("rgres/sd-controlnet-aerialdreams", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
-)
 
-pipe = pipe.to("cuda")
-
-# CPU offloading for faster inference times
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
+base_gradio_url = os.getenv('URL_GRADIO', 'http://localhost:7860')
+client = None
 
 app = Flask(__name__, static_url_path='/static')
 
-
 @app.route('/')
 def index():
     return app.send_static_file('index.html')
 
-
 def save_base64_image(base64Image):
     image_data = base64.b64decode(base64Image)
     path = "input_image.jpg"
@@ -41,28 +21,17 @@ def save_base64_image(base64Image):
         f.write(image_data)
     return path
 
-
 def encode_image_to_base64(filepath):
     with open(filepath, "rb") as image_file:
         encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
     return encoded_image
 
-
-def generate_map(image, prompt, steps, seed):
-    #image = Image.open(BytesIO(base64.b64decode(image_base64)))
-    generator = torch.manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        num_inference_steps=steps,
-        image=image
-    ).images[0]
-
-    return image
-
-
 @app.route('/predict', methods=['POST'])
 def predict():
+    global client
+    if not client:
+      client = Client(base_gradio_url)
+    
     data = request.get_json()
     
     base64Image = data['data'][0]
@@ -71,11 +40,16 @@ def predict():
     seed = data['data'][3]
 
     b64meta, b64_data = base64Image.split(',')
-    image = Image.open(BytesIO(base64.b64decode(b64_data)))
-    
-    return generate_map(image, prompt, steps, seed)
+
+    image_path = save_base64_image(b64_data)
+
+    result = client.predict(
+        image_path, prompt, steps, seed, fn_index=0
+    )
+
+    return b64meta + ',' + encode_image_to_base64(result)
 
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0',  port=int(
-        os.environ.get('D2M_PORT', 8000)), debug=True)
+        os.environ.get('PORT', 8000)), debug=True)

gradio-app.py

@@ -1,42 +0,0 @@
-from PIL import Image
-from io import BytesIO
-import base64
-import os
-
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from diffusers.utils import load_image
-import torch
-
-import gradio as gr
-
-controlnet = ControlNetModel.from_pretrained("rgres/sd-controlnet-aerialdreams", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
-)
-
-pipe = pipe.to("cuda")
-
-# CPU offloading for faster inference times
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe.enable_model_cpu_offload()
-
-def generate_map(image, prompt, steps, seed):
-    #image = Image.open(BytesIO(base64.b64decode(image_base64)))
-    generator = torch.manual_seed(seed)
-
-    image = Image.fromarray(image)
-
-    image = pipe(
-        prompt=prompt,
-        num_inference_steps=steps,
-        image=image
-    ).images[0]
-
-    return image
-
-demo = gr.Interface(
-  fn=generate_map,
-  inputs=["image", "text", gr.Slider(0,100), "number"],
-  outputs="image")
-
-demo.launch(server_port=int(os.getenv('GRADIO_PORT', '7860')))

gradio-mock-app.py

@@ -1,12 +0,0 @@
-from PIL import Image
-from io import BytesIO
-import base64
-import gradio as gr
-
-def generate_map(image, prompt, steps, seed):
-    return image
-
-with gr.Blocks() as demo:
-    button = gr.Button(label="Generate Image")
-    button.click(fn=generate_map, inputs=[gr.Image(), gr.Textbox(), gr.Number(), gr.Number()], outputs=gr.Image())
-demo.launch()

launch_full_interface.ipynb

@@ -1,107 +0,0 @@
-{
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/RubenGres/Drawing2Map-hfspace/blob/main/Launch_interface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "hsn_87UXrUn5"
-      },
-      "outputs": [],
-      "source": [
-        "!git clone https://github.com/RubenGres/Drawing2Map-hfspace.git\n",
-        "%cd Drawing2Map-hfspace/ui"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "uVUasUqwr_Br"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -r requirements.txt\n",
-        "!npm install -g localtunnel"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9OIMlcM9OQWk"
-      },
-      "outputs": [],
-      "source": [
-        "!export URL_GRADIO='http://localhost:7860'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "yhLLdro5s-rf"
-      },
-      "outputs": [],
-      "source": [
-        "!echo \" \"\n",
-        "!echo \"Click the next link and when prompted enter:\"\n",
-        "!curl ipv4.icanhazip.com\n",
-        "!echo \" \"\n",
-        "!lt --port 8000 & make run-prod"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "G2USPEr_4YOm",
-        "outputId": "36de5b2b-0cf7-410b-a225-2869138e5523"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "killall python\n",
-            "python: no process found\n",
-            "make: *** [Makefile:20: stop-server] Error 1\n"
-          ]
-        }
-      ],
-      "source": [
-        "!make stop-server"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "include_colab_link": true,
-      "provenance": []
-    },
-    "gpuClass": "standard",
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

main.py

@@ -1,3 +0,0 @@
-import subprocess
-
-subprocess.run(["make", "run-prod"], shell=False)