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BeveledCube commited on
Commit
ba11222
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1 Parent(s): 395adf9

Turned it into a flask API

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Files changed (5) hide show
  1. .env +3 -2
  2. Dockerfile +5 -1
  3. main.py +25 -7
  4. requirements.txt +2 -1
  5. templates/index.html +38 -0
.env CHANGED
@@ -1,2 +1,3 @@
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- HF_HOME=./models
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- TRANSFORMERS_CACHE=./cache
 
 
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+ HF_HOME=/home/container/models
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+ TRANSFORMERS_CACHE=/home/container/cache
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+ TMPDIR=/home/container/cache
Dockerfile CHANGED
@@ -2,13 +2,17 @@ FROM python:3.11
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  WORKDIR /api
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- COPY ./ /api/
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  RUN pip install --upgrade pip
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  RUN pip install -r requirements.txt
 
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  RUN mkdir /api/cache
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  RUN chmod a+rwx /api/cache
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  EXPOSE 7860
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  CMD ["bash", "start.sh"]
 
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  WORKDIR /api
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+ COPY --chown=user ./ /api/
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  RUN pip install --upgrade pip
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  RUN pip install -r requirements.txt
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+
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  RUN mkdir /api/cache
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  RUN chmod a+rwx /api/cache
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+ RUN mkdir /api/models
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+ RUN chmod a+rwx /api/models
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+
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  EXPOSE 7860
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  CMD ["bash", "start.sh"]
main.py CHANGED
@@ -1,19 +1,37 @@
 
 
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  from diffusers import StableDiffusionPipeline
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  import matplotlib.pyplot as plt
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  # Find models in https://huggingface.co/models?pipeline_tag=text-to-image&library=diffusers&sort=trending
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  model_id = "stabilityai/stable-diffusion-2-1"
 
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  pipe = StableDiffusionPipeline.from_pretrained(model_id)
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  pipe = pipe.to("cpu")
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- prompt = "beautiful horse"
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- image = pipe(prompt).images[0]
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- # Convert the torch Tensor to a NumPy array and move to CPU
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- image_np = image.cpu().numpy()
 
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- print("[PROMPT]: ", prompt)
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- plt.axis('off')
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- plt.imsave('generated_image.png', image_np.transpose(1, 2, 0))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import os
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+ from flask import Flask, request, render_template, send_file
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  from diffusers import StableDiffusionPipeline
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  import matplotlib.pyplot as plt
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  # Find models in https://huggingface.co/models?pipeline_tag=text-to-image&library=diffusers&sort=trending
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  model_id = "stabilityai/stable-diffusion-2-1"
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+ imagesPath = "images"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id)
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  pipe = pipe.to("cpu")
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+ app = Flask("AI API")
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+ @app.get("/")
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+ def read_root():
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+ return render_template("index.html")
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+ @app.route("/api", methods=["POST"])
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+ def receive_data():
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+ data = request.get_json()
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+ print("Prompt:", data["prompt"])
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+
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+ prompt = data["prompt"]
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+
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+ image = pipe(prompt).images[0]
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+
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+ # Convert the torch Tensor to a NumPy array and move to CPU
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+ image_np = image.cpu().numpy()
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+
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+ print("[Prompt]: ", prompt)
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+ plt.imsave(f"{imagesPath}/{prompt}.png", image_np.transpose(1, 2, 0))
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+
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+ return send_file(os.path.join(imagesPath, f"{prompt}.png"), mimetype='image/png')
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+
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+ app.run(host="0.0.0.0", port=7860, debug=False)
requirements.txt CHANGED
@@ -1,3 +1,4 @@
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  diffusers
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  transformers
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- matplotlib
 
 
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  diffusers
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  transformers
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+ matplotlib
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+ flask
templates/index.html ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <!DOCTYPE html>
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+ <html lang="en">
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+
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+ <head>
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+ <title>AI API</title>
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+ <style>
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+ body {
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+ font-family: Arial, sans-serif;
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+ margin: 20px;
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+ background-color: rgb(50, 50, 50);
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+ }
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+
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+ .img {
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+ width: 40vh;
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+ height: 40vh;
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+ margin: 30px;
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+ display: inline-block;
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+ }
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+
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+ .video {
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+ width: 40vh;
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+ height: 40vh;
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+ margin: 30px;
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+ display: inline-block;
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+ }
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+
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+ .text {
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+ color: rgb(223, 223, 223);
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+ }
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+ </style>
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+ </head>
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+
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+ <body>
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+ <h1 class="text">Hello there!</h1>
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+ <span class="text">For the API use a POST request</span>
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+ </body>
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+
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+ </html>